Download the AI & Machine Learning For Dummies PRO App: iOS - Android Our AI and Machine Learning For Dummies PRO App can help you Ace the following AI and Machine Learning certifications:
What is machine learning and how does Netflix use it for its recommendation engine?
What is an online recommendation engine?
Think about examples of machine learning you may have encountered in the past such as a website like Netflix that recommends what video you may be interested in watching next? Are the recommendations ever wrong or unfair? We will give an example and explain how this could be addressed.
Machine learning is a field of artificial intelligence that Netflix uses to create its recommendation algorithm. The goal of machine learning is to teach computers to learn from data and make predictions based on that data. To do this, Netflix employs Machine Learning Engineers, Data Scientists, and software developers to design and build algorithms that can automatically improve over time. The Netflix recommendations engine is just one example of how machine learning can be used to improve the user experience. By understanding what users watch and why, the recommendations engine can provide tailored suggestions that help users find new shows and movies to enjoy. Machine learning is also used for other Netflix features, such as predicting which shows a user might be interested in watching next, or detecting inappropriate content. In a world where data is becoming increasingly important, machine learning will continue to play a vital role in helping Netflix deliver a great experience to its users.
Netflix’s recommendation engine is one of the company’s most valuable assets. By using machine learning, Netflix is able to constantly improve its recommendations for each individual user.
Machine learning engineers, data scientists, and developers work together to build and improve the recommendation engine.
They start by collecting data on what users watch and how they interact with the Netflix interface.
This data is then used to train machine learning models.
The models are constantly being tweaked and improved by the team of engineers.
The goal is to make sure that each user sees recommendations that are highly relevant to their interests.
Thanks to the work of the team, Netflix’s recommendation engine is constantly getting better at understanding each individual user.
How Does It Work?
In short, Netflix’s recommendation algorithm looks at what you’ve watched in the past and then makes recommendations based on that data. But of course, it’s a bit more complicated than that. The algorithm also looks at data from other users with similar watching habits to yours. This allows Netflix to give you more tailored recommendations.
For example, say you’re a big fan of Friends (who isn’t?). The algorithm knows that a lot of Friends fans also like shows like Cheers, Seinfeld, and The Office. So, if you’re ever feeling nostalgic and in the mood for a sitcom marathon, Netflix will be there to help you out.
But That’s Not All…
Not only does the algorithm take into account what you’ve watched in the past, but it also looks at what you’re currently watching. For example, let’s say you’re halfway through Season 2 of Breaking Bad and you decide to take a break for a few days. When you come back and finish Season 2, the algorithm knows that you’re now interested in similar shows like Dexter and The Wire. And voila! Those shows will now be recommended to you.
Of course, the algorithm isn’t perfect. There are always going to be times when it recommends a show or movie that just doesn’t interest you. But hey, that’s why they have the “thumbs up/thumbs down” feature. Just give those shows the old thumbs down and never think about them again! Problem solved.
Another angle :
When it comes to TV and movie recommendations, there are two main types of data that are being collected and analyzed:
1) demographic data
2) viewing data.
Demographic data is information like your age, gender, location, etc. This data is generally used to group people with similar interests together so that they can be served more targeted recommendations. For example, if you’re a 25-year-old female living in Los Angeles, you might be grouped together with other 25-year-old females living in Los Angeles who have similar viewing habits as you.
Viewing data is exactly what it sounds like—it’s information on what TV shows and movies you’ve watched in the past. This data is used to identify patterns in your viewing habits so that the algorithm can make better recommendations on what you might want to watch next. For example, if you’ve watched a lot of romantic comedies in the past, the algorithm might recommend other romantic comedies that you might like based on those patterns.
Are the Recommendations Ever Wrong or Unfair? Yes and no. The fact of the matter is that no algorithm is perfect—there will always be some error involved. However, these errors are usually minor and don’t have a major impact on our lives. In fact, we often don’t even notice them!
The bigger issue with machine learning isn’t inaccuracy; it’s bias. Because algorithms are designed by humans, they often contain human biases that can seep into the recommendations they make. For example, a recent study found that Amazon’s algorithms were biased against women authors because the majority of book purchases on the site were made by men. As a result, Amazon’s algorithms were more likely to recommend books written by men over books written by women—regardless of quality or popularity.
These sorts of biases can have major impacts on our lives because they can dictate what we see and don’t see online. If we’re only seeing content that reflects our own biases back at us, we’re not getting a well-rounded view of the world—and that can have serious implications for both our personal lives and society as a whole.
One of the benefits of machine learning is that it can help us make better decisions. For example, if you’re trying to decide what movie to watch on Netflix, the site will use your past viewing history to recommend movies that you might like. This is possible because machine learning algorithms are able to identify patterns in data.
Another benefit of machine learning is that it can help us automate tasks. For example, if you’re a cashier and have to scan the barcodes of the items someone is buying, a machine learning algorithm can be used to automatically scan the barcodes and calculate the total cost of the purchase. This can save time and increase efficiency.
The Consequences of Machine Learning
While machine learning can be beneficial, there are also some potential consequences that should be considered. One consequence is that machine learning algorithms can perpetuate bias. For example, if you’re using a machine learning algorithm to recommend movies to people on Netflix, the algorithm might only recommend movies that are similar to ones that people have already watched. This could lead to people only watching movies that confirm their existing beliefs instead of challenged them.
Another consequence of machine learning is that it can be difficult to understand how the algorithms work. This is because the algorithms are usually created by trained experts and then fine-tuned through trial and error. As a result, regular people often don’t know how or why certain decisions are being made by machines. This lack of transparency can lead to mistrust and frustration.
A new Netflix documentary series finds even more compelling evidence that Vallejo pedophile Arthur Leigh Allen was the Zodiac Killer, but…Continue reading on Medium »
I still think "The Cable Guy" is Jim Carrey's best comedy because it was dark, it was unexpected, it was daring and Carrey's humor wasn't just him do the talking anus or do silly poses, it had meaning and Carrey dared to play the bad guy without coming across as Ace Ventura in a green body suit like he did in "Batman Forever". I think everything in this movie works. I didn't even mind Matthew Broderick or Leslie Mann being wasted in a girlfriend part. Broderick works as a straight man to Carrey's character, even if I didn't find Broderick charismatic enough to worth all that work from Carrey. It's like you watch Bridget Fonda in Single White Female, you get it, but Broderick always came across as a middle-aged nerd in everything I've seen him in. submitted by /u/Desiderata_Darrieux [link] [comments]
Gen Z loves Martha Stewart as an icon and Snoop Dogg’s bestie. But before she became a social media darling, she taught Gen X how to cook…Continue reading on Medium »
La UEFA Champions League es el torneo más emocionante de Europa, y los aficionados no quieren perderse ni un segundo de la acción. Si…Continue reading on Medium »
The UEFA Champions League is back, bringing the best teams in Europe head-to-head in thrilling matches. For fans, it’s essential to have a…Continue reading on Medium »
The UEFA Champions League is here, and the competition is fiercer than ever. If you’re looking for a way to watch every game, every goal…Continue reading on Medium »
La UEFA Champions League siempre trae emociones y partidos inolvidables que los fanáticos no pueden perderse. Con zon4k.com, puedes…Continue reading on Medium »
Die Champions League-Saison ist in vollem Gange, und Fußballfans möchten keine Minute der Action verpassen. Egal, ob du zu Hause oder…Continue reading on Medium »
Cada temporada, la UEFA Champions League reúne a los mejores equipos de Europa para ofrecer partidos llenos de emoción y rivalidad. Para…Continue reading on Medium »
What actor or actress do you think has the best track record of not just 'giving a great performance but also 'not being in any bad movies'? My wife and I were having this debate over the weekend and she suggested Daniel Day Lewis, but I feel like he's been in a couple movies that just were not very good. So my pick is Frances McDormand. Consistently great and I cant think of anything she was in that stunk. submitted by /u/mr_evilweed [link] [comments]
I had issues with the LG app connecting to Netflix after 20 mins of TV and internet disconnected i got it to work on 3rd try. But now I get a strange articfect on the top of the screen every so often, haven't noticed before or on anything else. It a digital line on top and then goes away submitted by /u/Carneyguydr [link] [comments]
Download the AI & Machine Learning For Dummies PRO App: iOS - Android Our AI and Machine Learning For Dummies PRO App can help you Ace the following AI and Machine Learning certifications:
Techies and Geek Inspired Movies and TV Shows – Netflix Amazon Prime Video HBO YouTube TV
If you’re a movie buff or a geek, there’s no doubt you love spending your free time watching films and TV shows that inspire your passions. And in the age of Netflix, Amazon Prime Video, and HBO, there’s no shortage of inspiring content to watch. This blog post takes a look at some of the best geeky and tech-inspired movies and TV shows available on streaming services today. So whether you’re a die-hard Star Wars fan or you can’t get enough of Andy Griffith reruns, there’s something for everyone in this roundup!
As a software engineer who works long hours and go to kids activities after work, TV shows and movies help me relax after work.
This blog is an aggregate of trailers, questions and answers about of Geek inspired Movies and TV Shows.
Where to watch Black Panther 2: Is Wakanda Forever on Disney Plus?
Marvel Pictures! Here’s options for downloading or watching Black Panther 2: Wakanda Forever streaming the full movie online for free on 123movies & Reddit including where to watch Universal Pictures’ movie at home. Is Black Panther 2: Wakanda Forever 2022 available to stream? Is watching Black Panther 2: Wakanda Forever on Disney Plus, HBO Max, Netflix or Amazon Prime? Yes we have found an authentic streaming option / service. Details on how you can watch Black Panther 2: Wakanda Forever for free throughout the year are described below.
Watch Here: Black Panther 2: Wakanda Forever Free Streaming
Is Black Panther 2: Wakanda Forever on Netflix? Black Panther 2: Wakanda Forever is not available to watch on Netflix. If you’re interested in other movies and shows, one can access the vast library of titles within Netflix under various subscription costs depending on the plan you choose: $9.99 per month for the basic plan, $15.99 monthly for the standard plan, and $19.99 a month for the premium plan. Is Black Panther 2: Wakanda Forever on Hulu? They’re not on Hulu, either! But prices for this streaming service currently start at $6.99 per month, or $69.99 for the whole year.
Hulu + Live TV. Is Black Panther 2: Wakanda Forever on Disney Plus? No sign of Black Panther 2: Wakanda Forever on Disney+,which is proof that the House of Mouse doesn’t have its hands on
Watch Here: Black Panther 2: Wakanda Forever Free Streaming
every franchise! Home to the likes of ‘Star Wars’, ‘Marvel’, ‘Pixar’, National Geographic’, ESPN,
STAR and so much more, Disney+ is available at the annual membership fee of $79.99, or the
monthly cost of$7.99. If you’re a fan of even one of these brands, then signing up to Disney+ is definitely worth it, and there aren’t any ads, either.
Is Black Panther 2: Wakanda Forever on HBO
Max? Sorry, Black Panther 2: Wakanda Forever is not available on HBO Max. There is a lot of
content from HBO Max for $14.99 a month, such a subscription is ad-free and it allows you to access all the titles in the library of HBOMax. The streaming platform announced an ad-supported version that costs a lot less at the price of $9.99 per month.
Amazon Prime Video. However, you can choose othershows and movies to watch from there as it has a wide variety of shows and movies that youcan choose from for $14.99 a month. Is Black Panther 2:
Black Panther 2: Wakanda Forever is not available to watch onLine
Peacock at the time of writing. Peacock offers a subscription costing $4.99 a month or $49.99 per year for a premium account. As their namesake, the streaming platform is free with content out in the open, however, limited.
Is Black Panther 2: Wakanda Forever on Paramount Plus?
Black Panther 2: Wakanda Forever is not on Paramount Plus. Paramount Plus has two subscription options: the basic version ad-supported Paramount+ Essential service costs$4.99 per month,
How long have you fallen asleep during Black Panther (2018) Movie? The music, the story, and the message are phenomenal in Black Panther. I have never been able to see another Movie five times like I did this. Come back and look for the second time and pay attention.
Watch Black Panther (2018) WEB-DL movies This is losing less lame files from streaming Black Panther (2018), like Netflix, Amazon Video. Hulu, Black Panther (2018) chy roll, Discovery GO, BBC iPlayer, etc. These are also movies or TV shows that are downloaded through online distribution sites, such as iTunes.
Is Black Panther 2: Wakanda Forever on Amazon Prime?
Amazon Prime is not streaming Black Panther 2: Wakanda Forever movies. However, the streamer has a wide range of latest movie collections for their viewers, including Train to Busan, The Raid: Redemption, Hell or High Water, The Florida Project, and Burning.
Is Black Panther 2: Wakanda Forever on HBO Max?
No. Black Panther 2: Wakanda Forever is a Sony movie, not a Warner Bros. movie. Also, HBO Max will no longer be streaming theatrical movies in 2022. (Last year, Warner Bros. opted to simultaneously release its theatrical slate on streaming, meaning HBO Max subscribers could watch movies like Matrix Resurrections at home. This year, however, Warner Bros. theatrical movies will have a 45-day theaters-only run before moving to HBO Max.)
Lupita Nyongo will be the new Black Panther. The opening of the trailer shows her in the Black Panther suit and she is missing from the rest of the trailer.
n the trailer the Queen/Bassett says her entire family is dead. It also shows Lupita giving birth to T’Challa’s child.
So, the Atlanteans attack. Shuri is killed in her attempt to take the BP mantle and defend Wakandan, or maybe captured and presumed dead. Then Nakia gives birth. Somehow a heart shaped herb is found and given to her, she is powered up and quickly recovers from giving birth (she may even take it while pregnant because she is seriously injured in the Atlantean attack.) Nakia becomes BP and queen regent. She fights Namor, and maybe also rescues Shuri.
Something like that.
Day Shift | Jamie Foxx, Dave Franco, and Snoop Dogg | Official Trailer | Netflix
source: r/movies
Day Shift:
Bud Jablonski (Jamie Fox) is a pool cleaner who scores extra cash as an exterminator, ridding the San Fernando Valley of its enduring vampire problem. But he does this work freelance—not part of the slayers union—so it’s not as lucrative as it is for his friend Big John Elliott (Snoop Dogg,) who is using his clout to bring Bud back to guild wages and benefits.
DEATH OF A LADIES’ MAN (2022) – Official Trailer
Trying to wrap my head around why they called the movie Death of a Ladies Man but used Bird on a Wire instead.
The movie was “inspired by the songs of Leonard Cohen.” So, I assume they’re going to use more than one.
Source: /r/movies
Ticket to Paradise | Official Trailer [HD]
What if in the end of Ocean’s Thirteen Danny and Tess got married, had a kid and divorced and now 20 years later try to sabotage their child’s engagement.
Looks like a rom com where there’s not a single unpredictable moment and yet will still be kind of fun.
Bodies Bodies Bodies | Official Trailer 2 HD | A24
SEE HOW THEY RUN | Official Trailer | Searchlight Pictures
It’s obvious from the popularity of “only murders in the building”, “knives out”, and the recent Christie movies that we’re entering a new era of campy mystery movies and I couldn’t be more excited for it.
source: r/movies
Thirteen Lives – Official Trailer | Prime Video
Just watching the trailer is making me feel claustrophobic.
source: r/movies
NOPE – Official Alternate Trailer – July 22
s it just me or do all of the night scenes look like they’re filmed in the day and color shifted to look like it’s night?
I remember they did that in 28 weeks later and it looked really jarring and cheap.
source: r/movies
Honor Society | Official Trailer | Paramount+ | July 29th
It wrinkles my brain that McLovin is now old enough to play a teacher.
Vikrant Rona | Official trailer (English) | releasing on July 28
Kannada film industry producing really great movies.., visuals are at Hollywood level.., KGF 2, 777 Charli, Vikrant Rona..
What TV show was amazing at first but became unwatchable for you later on?
Heroes
Season 1 was great and fresh. Season 2 didn’t know what to do with itself and just started giving everyone super powers.
By Season 3, characters were just changing motivations at the drop of a hat and it was just a huge mess of bad writing.
The blacklist, so many loopholes and a never ending plot. I mean, the female hero (forgot her name) was wanted and had her pictures broadcast nationwide live, but a couple of weeks after she can do undercover work.
Not the worst offender, but That 70’s Show tanked pretty hard once Eric left. He was sorely needed to make the chemistry of the group work.
Once Upon a Time. The first 3 seasons were good! And then after that they just kept getting worse
source: r/askreddit
What are movies where the final line of dialogue is the title of the movie?
I was watching Chinatown last night (great movie, btw!) and got to the final scene already knowing what the last line is, cause it’s famous and all, but only then clicked that the last word spoken is the title of the movie. (I know you can hear some cops sorta talking in the background as the camera pans out but I don’t think anyone would say that is the final dialogue in the movie).
Anyway, got me thinking, what are some other movies like this? Last words are the title of the film.
1- “And then they realized they were no longer little girls…They were little women.” – As read by Moe
I’d argue he’s the second most powerful member of The Seven
(Note, I’m doing TV Show Black Noir)
Black Noir is my favorite character in The Boys only second to Stormfront (cause she’s hot). So I always wanted to publicly share where I scale Noir, but before I do this, I have to share a few stuff stuff.
The Boys Presents Diabolical is mostly canon, specifically episodes 6,7 and 8.
That’s really all, now sit back and enjoy (or dislike) this answer.
Section 1: AP
Black Noir’s AP feat-wise isn’t too impressive since his speed is the real deal, but he’s still no joke since he’s able to fight and defeat Kimiko in a battle.
(I consider this fight to be an outlier since Black Noir should be faster than her, but it doesn’t change the fight since Black Noir is stronger)
And keep in mind this is the same Kimiko that survived attacks from A-Train
And even reacted to A Train while he was going fast
Kimiko speed feat
Imgur: The magic of the Internet
https://imgur.com/a/C5EF0Y1
This says a lot since she was badly damaged while fighting Noir (Had to use her self healing to survive), and she didn’t even get harmed that much while fighting A-Train since she got back up a few seconds later.
And he has shown himself to be much stronger than Starlight in multiple instances
This is the same Starlight that survived an attack from Stormfront (Same with Kimiko)
And Starlight tanked a Serbu BFG 50A with no injury
Starlight durability #1
Imgur: The magic of the Internet
https://imgur.com/a/crwJMq7
So already Noir scales above Kimiko and Starlight and scales above A Train due to a better performance aganist Kimiko, you could also argue his AP being at least on par with Maeve due to the tree nut scene and her deciding to use tree nuts instead of fighting him.
But it’s an iffy argument, doesn’t really matter though cause I think Noir beats her regardless.
Section 2: Speed
My favorite section yet.
Noir is stated to be much faster than a car
Not too impressive but this is just the start.
Black Noir was consistently able to dodge Homelander’s beams
And dodged Homelander himself
Blacknoirvshomelander GIF – Find & Share on GIPHY
Discover & share this Blacknoirvshomelander GIF with everyone you know. GIPHY is how you search, share, discover, and create GIFs.
I still think "The Cable Guy" is Jim Carrey's best comedy because it was dark, it was unexpected, it was daring and Carrey's humor wasn't just him do the talking anus or do silly poses, it had meaning and Carrey dared to play the bad guy without coming across as Ace Ventura in a green body suit like he did in "Batman Forever". I think everything in this movie works. I didn't even mind Matthew Broderick or Leslie Mann being wasted in a girlfriend part. Broderick works as a straight man to Carrey's character, even if I didn't find Broderick charismatic enough to worth all that work from Carrey. It's like you watch Bridget Fonda in Single White Female, you get it, but Broderick always came across as a middle-aged nerd in everything I've seen him in. submitted by /u/Desiderata_Darrieux [link] [comments]
What actor or actress do you think has the best track record of not just 'giving a great performance but also 'not being in any bad movies'? My wife and I were having this debate over the weekend and she suggested Daniel Day Lewis, but I feel like he's been in a couple movies that just were not very good. So my pick is Frances McDormand. Consistently great and I cant think of anything she was in that stunk. submitted by /u/mr_evilweed [link] [comments]
It waa one of my childhood fav, a pretty old movie now but amazing nonetheles.Ive watched it atleast 6 times and thats prob the most ive rewatched a movie. It still makes me crack up evry time I watch it. However if bizzare movies arent ur thing i wont recommend it. Those who havent watched it pls give it a try 😊 submitted by /u/PriorDetective4285 [link] [comments]
I first began watching movies as a hobby around my junior year of high school, in early 2016. I was still experimenting with it, so it…Continue reading on Medium »
The Devil Wears Prada (2006) is a unique film because it both challenges and supports gender stereotypes, especially about women in the…Continue reading on Medium »
The horror film “Smile” marked the feature writing/directing debut of Parker Finn, based on his own 2020 short film “Laura Hasn’t Slept”…Continue reading on Medium »
This month in the Movieland spotlight we have Shane McDermott from the classic rollerblading film “Airborne”. Join us for our exclusive interview as we review the highlights of his filmography. submitted by /u/creep187 [link] [comments]
When social media first became a thing, it was great to connect with friends and family to stay in touch even if they were a thousand…Continue reading on Medium »
Paddington in Peru Rotten Tomatoes 95% (21 Reviews) Metacritic: ( Reviews) Reviews The Hollywood Reporter: While PIP sadly lacks the absurdist wit and decidedly dark edges that elevated the first two Paddington films, it’s serviceable enough given its limitations. Deadline: It doesn’t matter if this film is much the same sort of thing as the last one, warmed over. In fact, that’s exactly what you want: third time around, the story of the little bear welcomed by strangers remains magical. Variety: “Paddington in Peru” is, as any Paddington adventure should be, fast and buoyant and disarmingly sunny in a way that viewers who weren’t alive -- or at least of cinemagoing age -- for the 2017 release of “Paddington 2” will lap up. HeyUGuys (5/5): A wonderfully fun adventure with all the warmth, humour, and heart fans of this beloved franchise have come to adore. With just the right blend of humour and heartfelt moments, Paddington in Peru is a joyful adventure and a must-see for fans old and new! Flick Feast (4/5): Paddington in Peru promises wholesome fun and adventure - and the reassurance that there will be more to come from the marmalade-loving bear. South China Morning Post (4/5): Given how often a threequel flops, Paddington in Peru effortlessly rises above its peers. Digital Spy (4/5): Paddington in Peru takes a bit of time to find its bearings but ends up delivering all the Paddington feels you hoped for. Total Film (4/5): It's a welcome return for Paddington and the Brown family who in this installment journey to Peru to see his beloved Aunt Lucy. Antonio Banderas chews scenery with varying results but Olivia Colman is pitch-perfect as the all-singing all-dancing Reverend Mother. Paddington's latest adventure may be the weakest of the films so far but it remains a total delight. Daily Mail (4/5): Grown-ups, however, might feel (as I did) that a little of the intrinsic charm and fun is lost by plucking Paddington away from London... On the other paw, it’s a pleasure to spend an hour and 43 minutes with Paddington in any setting. BBC (4/5): Paddington in Peru offers a fun and lively hour-and-three-quarters in the cinema, and that's not to be sniffed at, but it comes across as the solid third part of an established franchise rather than a stellar pop-cultural phenomenon in its own right. The Times (3/5): Well, the bad news is that Paddington in Peru isn’t as good as Paddington 2. The good news is that Wilson has made an entertaining and endearing yarn that is worth 106 minutes of your time. The Film Stage (C+): It’s clearly a disappointment compared to the two King-directed efforts, but is not without moments of comic inspiration, enjoyable supporting performances, and well-engineered adventure blockbuster set pieces. Empire Magazine (3/5): The Paddington series goes 'Raiders of The Lost Bear', swapping Primrose Hill for the savage jungles of Peru. It's a decent threequel, though a little of the enchantment has been lost in transit. IndieWire (C+): There’s no denying that even Paul King’s table scraps taste better than most of what family audiences have been served over the last six years. Independent (3/5) Really, all you can do is take what joy you can from Paddington in Peru, because its pleasures are rarer but still sweet. The Guardian (3/5): This Paddington threequel is a perfectly decent bet for the holidays, and never anything other than entertaining, but the gag density has thinned out and removing Paddington from Blighty... is a slightly shark-jumping move. The Telegraph (3/5): Let’s be fair: absolutely no one will have a bad time at Paddington in Peru, which is bouncy, unobjectionable and raises plenty of smiles. But most viewers are likely to come out more sated than elated. Synopsis: PADDINGTON IN PERU brings Paddington’s story to Peru as he returns to visit his beloved Aunt Lucy, who now resides at the Home for Retired Bears. With the Brown Family in tow, a thrilling adventure ensues when a mystery plunges them into an unexpected journey through the Amazon rainforest and up to the mountain peaks of Peru. Staring: Main Cast Hugh Bonneville as Henry Brown Emily Mortimer as Mary Brown Madeleine Harris as Judy Brown Samuel Joslin as Jonathan Brown Julie Walters as Mrs. Bird Jim Broadbent as Samuel Gruber Olivia Colman as The Reverend Mother Antonio Banderas as Hunter Cabot Carla Tous as Gina Cabot Voices Ben Whishaw as Paddington Brown Imelda Staunton as Aunt Lucy Directed by: Dougal Wilson Screenplay by: Mark Burton, Jon Foster, and James Lamont Story by: Paul King, Simon Farnaby, Mark Burton Produced by: Rosie Alison Cinematography: Erik Wilson Music by: Dario Marianelli Running time: 106 minutes Release date: November 8, 2024 (United Kingdom); January 17, 2025 (United States) submitted by /u/DemiFiendRSA [link] [comments]
The evil AI trope is, I think, overused in movies to the point where it is almost its own subgenre. The initially benevolent artificial intelligence turns evil and the last act is man vs cold unfeeling machine? boring, cliche, played out. I just saw Duncan Jones' movie Moon, and I fully expected the AI in it to do what so many lesser sci-fi movies do and go full HAL 9000 on Sam Rockwell - and considering the AI was voiced by Kevin fucking Spacey I wouldn't have been surprised. But the movie's robot companion remains a faithful and loyal servant throughout, and even ends up saving the protagonist in the end. It's very touching and I think means something, because in the end none of the human characters really cared for Sam - his bosses don't care about him because he's a disposable clone, his wife isn't even really his wife, even his original self sold out to the company and allowed his clones to be worked and killed off. In the end, it is a cold, unfeeling machine that is his only aide. For a movie that is about man's ability to use technology to serve himself regardless of the moral implications, it's interesting that the robot acts the most "human". submitted by /u/letsgopablo [link] [comments]
I finally got to the Amadeus and I all I can is, F*ck you salieri. His character continuously infuriated me and I felt so bad for Mozart, especially since he looked up to Salieri and depended on him. It made me so sad seeing Mozart beg for help from the devil himself. Argh. I don't know. It just makes me sad. submitted by /u/Comfortable_Buy5690 [link] [comments]
Or don’t see for a large part of a story and by the time we do they are already terrifying from what we know of them. I was thinking Cameron’s dad from Ferris Bueller. We never see even a picture of the guy but his threat is so terrifying, almost embodying the threat of abusive parents. What an achievement in writing to make us fear for Cameron so much without even meeting his father. submitted by /u/Lobsterman06 [link] [comments]
Loved the movie even more than on my first watch in 2016. I love the movie for exploring the difficulties of dealing with another species in terms of communication/language/different world views. Seeing a ‚peaceful‘ approach is also so refreshing. Are there any movies you would recommend in the subgenre ‚realistic alien drama/thriller‘? And which movie in your opinion has the most realistic „alien“ trope in terms of societys reaction to the species? I don‘t enjoy the typical „neon green, very sinister aliens destroy New York City“ action trope tbh… submitted by /u/masterciara [link] [comments]
What do you think his best role is? I think it’s gotta be American History X, but it’s very close between that and Primal Fear. He was just perfect for that role in AHX. I think he and Edward Furlong, which by the way is a huge shame what happened to him. But the whole time watching AHX I was amazed at Nortons ability to pull off that character which was a very hard role to be good in for any actor in my opinion, for obvious reasons lol. After that he became lead man in many great critically acclaimed films in which a few have been considered classics. Norton has been nominated for Oscar’s for AHX as best actor, and Birdman and Primal Fear for best supporting actor. I believe he should have won for AHX and also Primal Fear, but unfortunately the iconic film came out the same year so Roberto Benigni won in 1999. In 1997 Cuba Gooding won best supporting and 2014 JK Simmons won for Whiplash, very deservingly. Some other notable films Norton has been in other than American History X and Primal Fear and Birdman are F**** C*** (😂), 25th hour, The Score, Kingdom of Heaven, Red Dragon, Rounders, The People vs Larry Flynt, The Italian Job, The Illusionist, Moonrise Kingdom, and The Grand Budapest Hotel. Absolutely unreal filmography, not as good as Leo or Brad Pitt or some others but way better than your average actor. What is your favorite Norton performance? submitted by /u/oceanic_traveler [link] [comments]
Download the AI & Machine Learning For Dummies PRO App: iOS - Android Our AI and Machine Learning For Dummies PRO App can help you Ace the following AI and Machine Learning certifications:
Tech Jobs and Career at FAANG (now MAANGM): Facebook Meta Amazon Apple Netflix Google Microsoft
The FAANG companies (Facebook, Amazon, Apple, Netflix, Google, and Microsoft) are some of the most sought-after employers in the tech industry. They offer competitive salaries and benefits, and their employees are at the forefront of innovation.
The interview process for a job at a FAANG company is notoriously difficult. Candidates must be prepared to answer tough technical questions and demonstrate their problem-solving skills. The competition is fierce, but the rewards are worth it. Employees of FAANG companies enjoy perks like free food and transportation, and they often have the opportunity to work on cutting-edge projects.
If you’re interested in a career in tech, Google, Facebook, or Microsoft are great places to start your search. These companies are leaders in their field, and they offer endless opportunities for career growth.
That’s my guess. It hasn’t changed when Google became Alphabet.
FAANG stared as FANG circa 2013. The 2nd A became customary around 2016 as it wasn’t clear whether A referred to Apple or Amazon. Originally, FANG meant “large public, fast growing tech companies”. Now in 2021, the scope of what FANG referred to just doesn’t correspond to these 5 companies.
From an investment perspective (which is the origin of FANG) Facebook stock has grown the slowest of the 5 companies over the past 5 years. And they’re all dwarfed by Tesla.
From an employment desirability perspective (which is the context where FAANG is most used today). Microsoft is very similar to the group. It wasn’t “cool” around 2013 but its stock actually did better than Facebook or Alphabet over the past five years. Other companies like Airbnb, Twitter or Salesforce offer the same value proposition to employees, that is stability and tradable equity as part of the compensation.
FAANG refers to a category more than a specific list of companies.
As a side note, I expect people to routinely call the company Facebook, just like most people still say Google when they really mean Alphabet.
People frequently fail FAANG interviews because they choke — they experience anxiety and just forget their knowledge — or they don’t know the material to begin with.
Inverting a binary tree, matching up pairs of brackets, finding the duplicate in an array of distinct integers, etc., are all weeder-questions that should be solvable in 5–10 minutes, if you’re the type to suffer from interview jitters. You should know which data structures to use, intuitively, and you should be doing prep work to cover your knowledge gaps if you don’t.
Harder questions will take longer, but ultimately, you’ll have 45 minutes or so to solve 2–3 questions.
Technical interviews at FAANG companies are only difficult if you have shaky computer science fundamentals. Luckily, the process for cracking the code interview *cough* is very well-documented, hence, you only need to follow the already established strategies. If you’re interested in maximizing income while prioritizing career growth, it behooves you to spend a month or two studying these strategies.
In FAANG interview process, when you fail at the 1st (or 2nd stage), does it mean that single interviewer on the respective stage failed you, or is it still team collaboration /hiring manager decision?
If you were dropped after doing a single interview (usually called a “screen”) it means that this interviewer gave negative feedback. I would guess at some companies this feedback is reviewed by the hiring manager, but mostly I think a recruiter will just reject if the interviewer recommends no hire. Even if a hiring manager looks at it, they would probably reject almost always if the feedback is negative. The purpose of the screen is to quickly evaluate if a person is worth interviewing in depth.
If you were rejected after a whole interview panel, probably a hiring manager or similar did look at the entire feedback, and much of the time there was a discussion where interviewers looked at the entire feedback as well and shared their thoughts. However, if the feedback was clearly negative, it could’ve been just a snap decision by a manager without much discussion. Source.
What do you do after you absolutely flop a technical interview?
Take care of yourself / don’t beat yourself up.
It happens. It happened to me, it happened to smarter people. It’s ok.
Two thoughts to help here –
Getting to the interview stage is already a huge achievement. If you are interviewed, this means that in the expert opinion of the recruiters, people that did tech screens etc. you stand a chance to pass the interview. You earned your place in the interviewee seat. This is an accomplishment you can be proud of.
The consequences are probably* negligible in the long run. There’s at least 100 very desirable tech companies to work at at a given moment. You didn’t get in 1% of them at a moment in time. Big deal. You can probably retry in a few months. It’s very likely that you get an equivalent or even better opportunity, and there’s no use imagining what would have happened if you had had that job. (*“probably” because if you’re under time pressure to get a job rapidly… it may sting differently. But hey, there’s still the first thought).
As a bonus, you’ll probably remember very well the question on which you failed. Source: Jerome Cukier
If an interviewer says “we’re still interviewing other candidates at the moment”, and then walks you out into the lobby, does that mean they want to hire you potentially after or no?
Here’s a secret. I have been a recruiter for 24 years and when they walk you out after your interview and tell you that they are still interviewing other candidates at the moment, it really means they’re still interviewing other candidates at the moment. There’s no secret language here to try to interpret. It means what it means. You will have to wait for them to tell you what next steps are for you because, again, they have other people to interview. By Leah Roth
The difficulty of the interview is going to vary more interviewer to interviewer, than company to company. Also, how difficult the questions are is not directly related to how selective the process is; the latter being heavily influenced by business factors currently affecting these companies and what are their current hiring plans.
#1: So, how do know you this? You don’t. An affirmative answer to this question can only come from data.
#Answer #1: Fair question. I have been very involved in interviewing in a number of large tech cos. I have read, by now, thousands of interview debriefs. I have also interviewed a fair amount as a candidate, although I have not interviewed in each of the “FAANG” and I have definitely be more often on the interviewing side.
As such, I have seen for the same position, very easy questions and brutally difficult ones; I have seen very promising candidates not brought to onsite interviews because the hiring organization didn’t currently have resources to hire, but also ok-ish candidates given offers because the organization had trouble meeting their hiring targets. As a candidate I also experienced: easy interview exercises but no offer, very hard interview exercises and offer (with the caveat that I never know exactly how well I do, but I certainly can tell if a coding question or a system design question is easy or hard).
So. I am well aware that it’s still anecdotal evidence, but it’s still based on a fairly large sample of interviews and candidates.
#Reply to #1: Nope, you’re wrong. I have experience in the interview process at Amazon and Microsoft and have a different conclusion. Moreover, “experts” in lots of disparate fields make claims that are a bunch of bullcrap due to their own experiential biases. Additionally, you would need to be involved at all of the companies listed, not just some of the them, for that experience to be relevant in answering this question. We need to look at the data. If you don’t have data, I will not trust you just because of “your experience”. I don’t think it’s possible for Jerry C to have the necessary information to justify the confidence that is projected in this answer.
What you need is not so much a list of “incidents” but more generally some self-awareness on what you care about and how you’ve progressed and how you see your career.
The best source for this material is your performance reviews. Ideally you also kept some document about your career goals and/or conversation with your manager. (If you haven’t such documents, it’s never too late to start them!).
You should have 5–6 situations that are fairly recent and that you know on the back of your hand. These must include something difficult, and some of these situations must be focused on interpersonal relationships (or more generally, you should be aware of more situations that involved a difficult interpersonal relation). They may or may not have had a great outcome – it’s ok if you didn’t save the day. But you should always know the outcome both in terms of business and on your personal growth.
Once you have your set of situations and you can easily access these stories / effortlessly remember all details, you’ll find it much easier to answer any behavioural question.
In a software engineering interview, How should one answer the question, ‘Could you tell me about some of the technical challenges in your previous projects’?
To take a few steps back, there are 2 things that interviewers care about in behavioural interviews – whether the candidate has the right level, and whether they exhibit certain skillsets.
When you look at this question from the first angle, it’s important to be able to present hard problems on which it’s clear what the candidate’s personal contribution was. Typically, later projects are better for that than earlier ones.
Now, in terms of skillsets, this really depends company by company but typically, how well a candidate is able to describe a problem especially to someone with a different expertise, and whether they spontaneously go on to describe impact metrics, goes a long way.
So great answer: hard, recent, large scale project, that the candidate is able to contextualize (why was is important, why was it hard, what was at stake), where they are able to describe what they’ve done and what was the potential impact, and what were the actual consequences.
Not so great answer: a project that no one asked the candidate to do, but which they insisted on doing because they thought it was cool/interesting, on which they worked alone and which didn’t have any business impact. Source.
This question (like many other things in life) is much more complicated than it appears on the surface. That’s because it is conflating several very different issues, including:
What is retirement?
What is “early”?
At what age do most software engineers stop working in that role?
How long do employees stay on average at the FAANGs?
In the “old” days (let’s arbitrarily call that mid-20th century America), the typical worker was white, male and middle class, employed on location at a job for 40–50 hours a week. He began his working career at 18 (after high school) or 22 (after college), and worked continuously for a salary until the age of 65. At that time he retired (“stopped working”) and spent his remaining 5–10 years of life sitting at home watching tv or traveling to places that he had always wanted to visit.
That world has, to a large extent, been transmogrified over the past 50 years. People are working longer, changing employment more frequently, even changing careers and professions as technology and the economy change. The work force is increasingly diverse, and virtually all occupations are open to virtually all people. Over the past two years we have seen that an astonishing number of jobs can be done remotely, and on an asynchronous basis. And all of these changes have disproportionately affected software engineering.
So, let’s begin by laying out some facts:
When people plan to retire is a factor of their generation: Generation Y — ages 25 to 40 — plans to retire at an average age of 59. For Generation X — now 41 to 56 — the average age is 60. Baby boomers — who range from 57 to 75 — indicated they plan to work longer, with an average expected retirement age of 68.[1]
The average actual retirement age in the US is 62[2]
Most software engineers retire between the ages of 45 and 65, with less than 1% of developers working later than 65.[3]
But those numbers are misleading because many software engineers experience rapid career progression and move out of a pure development role long before they retire.
The average life expectancy in Silicon Valley is 85 years.[4]
The tenure of employment at the FAANGs is much shorter than than one might imagine. Unlike in the past, when a person might spend his or her entire career working for one or two employers, here are the average lengths of time that people work at the FAANGs: Facebook 2.5 years, Google 3.2 years, Apple 5 years.[5]
Therefore, if the question assumes that a software engineer gets hired at a FAANG company in his or her 20s, works there for 20 or 30 years as a coder, and then “retires early”, that is just not the way things work.
Much more likely is the scenario in which an engineer graduates from college at 21, gets a masters degree in computer science by 23, starts as a junior engineer at a small or large company for a few years, gets hired into a FAANG by their early 30s, spends 3–5 years coding there, is recruited to join a non-FAANG by their early 40s in a more senior role, and moves into management by their late 40s.
At that point things become a matter of personal preference: truly “retire”, start your own venture; invest in cryptocurrency; move up to senior management; begin a second career; etc.
The fact is that software engineering at a high level (such as would warrant employment at a FAANG in the first place) pays very well in relative terms, and with appropriate self-control and a moderate lifestyle would enable someone to “retire” at a relatively early age. But paradoxically, that same type of person is unlikely to do so.
Are companies like Google and Facebook heaven on earth in terms of workplaces?
No. In fact Google’s a really poor workplace by comparison with most others I’ve had in my career. Having a private office with a door you can close is a real boon to doing thoughtful, creative work, and having personal space so that you can feel psychologically safe is important too.
You don’t get any of that at Google, unless you’re a director or VP and your job function requires closed-door meetings. I have a very nice, state-of-the-art standing desk, with a state-of-the-art monitor, and the only way for me to avoid hearing my tech lead’s conversations is to put headphones on. (You can get very nice, state-of-the-art headphones, too.)
On the other hand, I also have regular access to great food, and an excellent gym, and all the La Croix water I can drink. I get to work on the most incredible technological platform on earth. And the money’s good. But heaven on earth? Nah. That’s one of the reasons the money’s good.
What is the starting salary of a software engineer at Google?
A new grad software engineer (L3) at Google makes a salary around $193,000 including stock compensation and bonus. The industry is getting a lot more competitive and top companies such as Google have to make offers with really generous stock packages. The below diagram shows a breakdown for the salary. View all the crowdsourced reports as well as other levels on Levels.fyi.
Hope that helps!
What is the best Google employee perk, and why?
Having recent left Google for a new startup I have to agree that the most-missed perk is the food. It’s not so much that it’s free — you can get lunch for about $10 per day so the cost is not a huge deal. There is simply nowhere you can go, even in a Silicon Valley city like Mountain View, that has healthy low-fat, varied choices that include features like edible fruits and vegetables. The food is even color-coded (red/yellow/green) based on how healthy it is (it always bothered me that the peanut-butter cups are red….).
Outside of Google you end up having muffins for breakfast and pizza for lunch. It tastes good but it’s not the same to your body.
But beyond just the food, the long term health impact of the set of perks at Google is huge. There is nothing better than being able to come in early, work out at the (free) gym by your office, shower (with towels provided as noted by others), then have eggs (or egg whites if you prefer) and toast (or one of a dozen other breakfasts). Source
Everyone has a study plan and list of resources they like to use. Different plans work for different people and there is no one size fits all.
This by no means is the only list of resources to join a larger technology company. But it is the list of resources I used myself to prepare for all my technology interviews.
Quick Background
I’m a current engineer at Microsoft who previously worked at Amazon for 1 year each respectively. I don’t have a master’s degree and I graduated from NYU, not an Ivy League. I’ll soon be joining Google and the following resources is how I got there.
Yes, the purchasable resources are affiliate links that help support this blog. Regardless, these are the resources I’ve used both purchasable and free.
This is the simplest book to get anyone started in studying for coding interviews.
If you’re an absolute beginner, I recommend you to start here. The questions have very details explanations that are easy to understand with basic knowledge of algorithms and data structures.
Elements of Programming Interviews (Python, Java, C++)
If you’re a little more experienced, every question in this book is at the interviewing level of all large technology companies.
If you’ve mastered the questions in this book, then you are more than ready for the average technology interview. The book is not as beginner friendly as CTCI but it does include a study plan depending on how much you need to prepare for your interviews. This is my personal favorite book I carried everywhere in university.
Blind has a list of 75 questions that is generally enough to solve most coding interviews. It’s a very curated and focused list for the most essential algorithms to leverage your time.
The playlist above is one of the clearest explanations I’ve ever seen and highly recommend if you need an explanation on any of the problems.
These problems are hard. Really hard for anyone who hasn’t practiced algorithms and is not beginner friendly. But if you are able to complete the sorting and searching section, you will be more capable than the average LeetCode user and be more than ready for your coding interview.
Consider this if you’re comfortable with LeetCode medium questions and find the questions in CTCI too easy.
This is the most common and best textbook anyone could use to learn algorithms. It’s also the textbook my university used personally to learn the core and essential algorithms to most coding problems.
The 4th edition was recently released and is still relevant to MIT students. If you need structure and a traditional classroom setting to study, follow MIT’s algorithm course here.
Graph theory does come up in interviews (and was a question I had at both Bloomberg and Google). Stay prepared and follow William Fiset’s graph theory explanation.
The diagrams are comprehensive and the step-by-step explanations are the best I’ve ever seen on the topic.
This handbook is for people who are strongly proficient with most Leetcode algorithms. It’s a free resource that strongly complements the CSES.fi curriculum.
For the most experienced algorithm enthusiasts, this book will cover every niche data structure and algorithm that could possibly be asked in any coding interview. This level of preparation is not generally needed for FAANG type companies but can show up if you’re considering hedge fund type companies.
In my opinion, you will be more than ready for any system design interview using these resources. The diagrams are clear and the explanations are as simple as possible in each book to help you learn system design concepts quickly.
I recommend the online course personally because yes the content from both books is great to own, it’s the online community discord you get access to that makes the yearly subscription worth it. The discord includes mock interview buddies, salary discussion, and over view on each system design topics to study with other users on.
The system design primer is the best free resource on all things system design. Dig deep into the Git repository and you will learn everything you need to know on system design. It’s all curated in a single repository and the clearly structured to give you a guided curriculum.
This quick overview on system design is great to review if you’re in a rush. The read typically takes users 45 minutes but you’ll be left knowing more system design than the average engineer.
Give it a read. If concepts are unclear or confusing, that might be a sign you’re not ready for interviews.
Regardless if you’re learning design patterns for the object-oriented programming interview, you will need to know design patterns as a software engineer at these large companies.
The book is the origin of the world’s most common design patterns today and showing proficiency in these for your object oriented interview is a requirement for certain large technology companies like Amazon.
The above resource is dense and written in language that’s hard to understand. While the original source material in design patterns is great, it doesn’t help much if it’s difficult to understand.
Consider Head First Design patterns to study a simplified explanation of those common design patterns. It might not be as in-depth as the original source material, but your understanding in design patterns will be more than enough to crack any object-oriented interview.
Closing Thoughts
Honestly, I did not go through all of these resources from cover to cover. If you do, I’m sure you wouldn’t need to study for another interview again. But likely we don’t have the time to do that so make sure that once you understand the core concepts in the any of the above categories that you invest your time moving on to the next.
Again, these are the resources I used and is not at all inclusive of anyone else’s study plan.
3 Years ago I applied to Google and was rejected immediately after the phone screen. Fast forward 2022 and was given another chance to re-interview. Here’s how the entire experience went.
Quick Background
I am currently a junior level software engineer at Microsoft (L60) with previous experience at Amazon (SDE I). My tenure is 1 year at Microsoft and 1 year at Amazon.
The first time I applied to Google was fall of my senior year of college at NYU. I failed the phone screen horribly and never thought I would join a company as competitive as Google. But I did not want to count myself out before even interviewing.
Recruiter Screen
I slowly built my LinkedIn to make sure recruiters would notice me whenever I wrote a LinkedIn post. With 15,000 followers at the time, it wasn’t too difficult to have one of them reach out with the chance to interview. A message came in my LinkedIn inbox and I responded promptly to schedule the initial recruiter call.
The chat was focused more on my previous experiences engineering and some of the projects I worked on. It was important to talk about what languages I was using and how much of my day was spent coding (70% of my day at Microsoft).
The recruiter was interested in having me follow through with a full-loop and asked when I would like to go through the process. It was important to me to ask what engineering level I was applying for. He shared it was L3/L4 role where the interviews would calibrate me depending on my performance. Knowing that, I mentioned I’d like to interview 1 month later and asked what the process looked like as explained to me.
Technical Phone Screen
6 Hour Virtual On-site a. 4 Technical Coding Interviews or 3 Technical Coding Interviews + 1 system design b. Behavioral “Googliness” interview
Phone Screen
Following the initial recruiter phone screen, I received an email from Google. It explained that I would be exempt from the Google Technical Phone Screen.
Why? I am personally not sure but it likely had to do with prior experience at large technology companies. I was personally surprised because to this day my first Google Phone Screen is still one of the toughest coding interviews I have ever been given.
It looked like that was as relevant as my current work experience and I didn’t have much to complain about moving quicker through the process and directly on-site.
Technical Onsite
Every coding question I had was a coding question that was either on LeetCode or could be solved with the patterns you find solving coding questions. Here’s what my experience for each of them looked like
Coding Interview #1
The interviewer looked like someone who was my age and likely joined Google directly after university. Maybe I wasn’t jealous. Maybe I was.
The question I was given was a string parsing Hash-Map question. Easily doable if you worked through a few medium questions regarding hash-maps and string parsing. But if you’re not careful, you may have fallen into a common trap.
Let me point it out for you. Abstract away the logic for tedious parsing logic by writing something like “parsingFunction()”. Otherwise 30 minutes may pass without you solving the question. I wrote a short “ToDo” mentioning I’d come back to it if the interviewer cared.
Spoiler: The interviewer didn’t care.
They lastly asked me to optimize with a heap and what the running time was. Unlike others who assert the running time, I solved for it and the interview concluded there.
Coding Interview #2
The interviewer who was more senior than the previous interviewer. I heard the coding question and thought the on-site was over.
The thing about some coding questions is whether you see the pattern for the algorithm or not. The recognizing the pattern for the algorithm can be much more difficult than actually writing the code for it. This was one of those interviews.
After hearing the questions I was thinking of ways to brute force the question or if there was a pattern I could see using smaller test cases. I wasn’t able to recognize it and eventually the interviewer told me what the pattern was.
I tried not to come off embarrassed but followed up with the algorithm to implement that pattern and the interviewer gave me the “go ahead” to code. I finished coding the pattern and answer the follow up by the interviewer on how to make my code modular to handle another requirement. This did not require implementation.
Afterwards was a discussion on time and space complexity and the interview was over.
Coding Interview #3
The interviewer was a mid-level engineer who was not as keen on chatting as much as the interviewers.
Some coding interviews are just one interview where you have to get the question correct or not. This one started off easy and iterated to be tougher.
My quick advice to anyone is to never come off arrogant for any coding question. You may know the question is easy and the interviewer likely does as well. Often times it’ll get harder and all that ego will go out the window. Go through the motions and communicate you always do for any other coding problem.
The problem given was directly on LeetCode and I felt more comfortable knowing I had solved this awhile ago before. If you’re familiar with “sliding window” then you more than likely would be able to solve it. But here’s where the challenge was.
After the warm-up question, the follow up had another requirement on top of the previous question. That follow up was more array manipulation. Finally the last iteration was shared.
I implemented the algorithm where Math.max was being called more than necessary. To me it didn’t affect the output of the algorithm and looked like it didn’t matter. But it mattered to the interviewer. I took that feedback and carefully implemented it the way the interviewer asked me to (whether it actually affected the algorithm or not).
Time and space complexity was solved and the interview was over.
Coding Interview #4
This was another interviewer who had joined Google after university and had the same work experience I did.
This prompt was not given to me and I expected I had to write down the details to the question myself. After asking some clarifying questions on what was and wasn’t in scope, I shared my algorithm.
The question was an object-oriented question to implement a graph. If you had taken any university course on graph theory, you would be more than prepared.
The interesting discussion was whether I had to implement the graph with BFS or DFS and explain the pro’s and con’s of each. Afterwards, I decided with BFS (because BFS is easier for me to implement) and the requirement followed up with taking K-most steps iterative.
I’m not sure if that’s the follow-up because I implemented it in BFS or if that was always the follow-up but I quickly adjusted the algorithm and solved for space and time complexity as always.
The Googliness interview
Googliness is just Google’s behavioral interview. Most questions were along the lines of
Tell me about yourself
What’s a project you worked on?
When was a time you implemented a change?
When was a time you dealt with a coworker who wasn’t pulling their weight?
To prepare for these, I’d recommend learning about the STAR format and outlining your work experiences if you can recall them before interviewing.
This seemed to go well but then I was given a question I didn’t expect. A product question and my thought process on how to work with teammates to answer the question.
My key point of advice: Nothing matters if the user doesn’t want it.
Emphasize how important user research is to build a product that a user will use otherwise everyone’s time could be better invested in other initiatives. Avoid jumping straight into designing the product and coordinating talks with product managers and UX designers.
Offer
2 weeks later, an informal offer was shared with me in my email.
Most of the interview didn’t not pertain to my previous experience directly. A systematic way of approaching, communicating, and implementing coding problems is enough without experience from Amazon/Microsoft.
That means you interviewed well. Someone else interviewed better for the first role, but the recruiter sees that there other roles for which you might be a better fit.
The eight interviews is a sign that someone in the process wanted you specifically for some role.
I think there may be two different things going on.
First, are you sure whether it’s a FAANG recruiter, or someone from an external sourcing firm which is retained by a FAANG company? I had this experience where someone reached out on LinkedIn and said they were recruiting for a Google role and passed along a job description. As I started asking them questions, it became clear that they just wanted me to fill out an application so that they can pass it to someone else. Now, as it happens, I am a former Google employee, so it quickly became clear that this person was not from Google at all, but just retained to source candidates. The role they wanted me to apply for was not in fact suitable, despite their claim that they reached out to me because I seemed like a good match.
If you are dealing with a case like this, probably what happens is that they source very broadly, basically spamming people, on the chance that some of the people they identify will in fact be a good fit. So they would solicit a resume, pass it to someone who is actually competent to judge, and that person would reject. And the sourcing firm will often ghost you at this point.
If you are dealing with an actual internal recruiter, I think it can be a similar situation. A recruiter often doesn’t really know if you are a fit or not, and it will often be some technical person who decides. That person may spend 30 seconds on your resume and say “no”. And positions get filled too, which would cause everyone in the pipeline to become irrelevant.
In such cases there is no advantage for the recruiter to further interact with you. Now, every place I worked with, I am pretty sure, had a policy that if a recruiter interacted with the candidate at all, they were supposed to formally reject them (via email or phone). But I imagine there’s very little incentive for a recruiter to do it, so they often don’t. And as a candidate, you don’t really have any way to complain about it to the company, unless you have a friend or colleague on the inside. If you do, I suggest you ask them, and it may do some good, if not to you (you are rejected either way), at least to the next applicant.
It’s not actually a line of code, so to speak, but lines of code.
I work in Salesforce, and for those who are not familiar with its cloud architecture, a component from QA could be moved to production only if the overall test coverage of the production is 75% or more. Meaning, if the total number of lines of code across all components, including the newly introduced ones, is 10000, enough test classes must be written with appropriate test scenarios so as to cover at least 7500 lines of the lump. This rule is enforced by Salesforce itself, so there’s no going around it. Asserts, on the other hand, could be done without.
If the movement of your components causes a shift in balance in production and tips its overall coverage to below 75%, you are supposed to work on the new components and raise their coverage before deployment. A nightmare of sorts, because there is a good chance your code is all clean and the issue occurs only because of a history of dirty code that had already gone in over years to drag the overall coverage to its teetering edges.
Someone in my previous company found out a sneaky way to smuggle in some code of his (or hers) without having to worry about this problem.
So this is simple math, right? If you have got 5000 lines of code, 3750 must be covered. But what if I have managed to cover only 2500 (50%) and my deadline is dangerously close?
Simple. I add 5000 lines of unnecessary code that I can surely cover by just one function call, so that the overall line number now is 10000 and covered lines are 7500, making my coverage percentage a sweet 75.
For this purpose they introduced a few full classes with a lone method in each of them. The method starts with,
Integer i = 0;
and continues with a repetition of the following line thousands of times.
i++;
And they had the audacity to copy and paste this repetitive ‘code’ throughout a bulky method and across classes in such a reckless manner that you could see a misplaced tab in first line replicated exactly in every 100th line or so.
Now all that is left for you to do is call this method in a test class, and you can cover scores of lines without breaking a sweat. All the code that actually matters may lie untested in automated coverage check, glaring red if one should care to take a look at, but you have effectively hoodwinked Salesforce deployment mechanism.
And the aftermath is even crazier. Seeing the way hoards of components could be moved in without having to embark on the tedious process of writing test classes, this technique acquired a status equivalent to ‘Salesforce best practices’ in our practice. In almost all the main orgs, if you search for it, you can find a class with streams of ‘i++;’ flowing along the screen for as far as you have the patience to scroll down.
Well, these cloaked dastards remained undetected for years before some of the untested scenarios started reeking. More sensible developers fished out the ‘i++;’ classes, raised the alarm and got down to clean up the mess. Just removing those classes drove the overall production coverage to abysmal low, preventing any form of interaction with production. What can I say, that kept many of us busy for at least a month.
I wouldn’t call the ‘developers’ that put this code in dumb. I would rather go for ‘wicked’. The higher heads and testers who didn’t care to look while this passed under their noses do qualify as dumb.
And the code… Man, that’s the dumbest thing I’ve ever seen.
If you are in the pipeline and you have interviews scheduled, then your recruiter will know exactly what loop will be set up for you and what kind of questions you may have. Recruiters try to get their candidates all the information they need to approach the interviews at the top of their potential, so ask the everything you need to know.
The actual answer depends on the candidate level and profile, the composition of the interviews is pretty much bespoke.
Dev: Alright, let the competition begin! Startup A: We will give you 50% of the revenue! Startup B: To hell with it, we will give you 100%! Startup A: Eh… we will give you 150%!
TL;DR: Nearly impossible. If you are a Google-sized company, of course. Totally impossible in other cases.
I run an outsourcing company. Our statistics so far:
500 CVs viewed per month
50 interview invitations sent per month
10 interviews conducted per month
1 job offer made (and usually refused) per month
And here we are looking for a mid-level developers in Russia.
Initially we wanted to hire some top-notch engineers and were ready to pay “any sum of money that would fit on the check”. We sent many invitations. Best people laughed at us and didn’t bother. Those who agreed – knew nothing. After that we had to shift our expectations greatly.
Still, we manage to find good developers from time to time. None of them can be considered super-expert, but as a team they cooperate extremely effectively, get the job done and all of them have that engineering spirit and innate curiosity that causes them to improve.
It takes constant human effort to keep sites like Google and Gmail online. Right now a Google engineer is fixing something that no one will ever know was broken. Some server somewhere is running out of memory, a fiber link has gone down, or a new release has a problem and needs to be rolled back. There are careful procedures, early warnings, and multiple layers of redundancy to ensure that problems never become visible to end users, but.
Sometimes problems do become visible but not in a way that an individual user can attribute to the site. A request might not get a prompt response, or any at all, but the user will probably blame the internet or their computer, not the site. Google itself is very rarely glitchy, but services like image search do sometimes have user visible problems.
And then of course, very rarely, a giant outage brings down something giant like YouTube or Google Cloud. But if it weren’t for an army of very smart, very diligent people, outages would happen much more often.
It’s what they don’t understand. 10x software engineers don’t really understand their job description.
They tend to think all these other things are their responsibility. And they don’t necessarily know why they’re doing all these other things. They just sense that it’s the right thing to do. If they spot something is wrong, they will just fix it. Sometimes it even seems like they’re not in control of what they do. It’s like a conscientiousness overdose.
10x engineers are often all over the code base. It is like they had no idea they were just part of one eng team.
I don’t think the premise behind the question is entirely true. These companies rely completely on programming problems with junior candidates that are not expected to have significant experience . Senior candidates do, in fact, get assessed based on their experience, although it might not always feel like it.
Let me illustrate this with an interview process I went through when interviewing for one of the aforementioned companies (AFAIK it’s typical for all the above). After the phone screen, there was a phone site interview with 5 consecutive interviews – 2 whiteboard coding + 2 whiteboard architecture problems + 1 behaviour interview. On the surface, it looks like experience doesn’t play a part, but, SURPRISE, experience and past projects play part in 3 interviews out of 5. A large part of the behavioural interview was actually discussing past projects and various decisions. As for the architecture problems – it’s true that the problem discussed is a new one, but those are essentially open ended questions, and the candidates experience (or lack thereof) clearly shines through. Unlike the coding exercises, these questions are almost impossible to solve without tackling something similar in the past.
Now, here a few reasons to why the emphasis is still on solving new problems and not diving into the candidates home territory, in no particular order:
Companies do not want to pass over strong candidates that just happen to be working on some boring stuff.
Most times companies do not want to clone a system that the candidate has worked on, so the ability to learn from experience, and apply it to new problems is much more valuable.
When the interviewer asks different candidates to design the same system, they can easily compare different candidates against one another. The interviewer is also guaranteed to have a deep understating of the problem they want the candidate to solve.
People can exaggerate (if not outright lie) their role in working on a particular project. This might be hard to catch-on in one hour, so it’s to avoid in the first place.
(This one is a minor concern, but still) Large companies hire by committee, where interviewers are gathered from the whole company. The fact that they shouldn’t discuss previous projects, removes the need to coordinate on questions, by preventing a situation where two interviewers accidentally end up talking about the same system, and essentially doing the interview twice.
Originally Answered: What can I, currently 17 years old, do to become an engineer/entrepreneur like Elon Musk?
This is a quick recap of my earlier response to a similar question on Quora:
I would recommend that you take a close look at the larger scheme of things in your life, by spending some time and effort to design your life blueprint, using Elon Musk as your inspiration and/or visual model.
By the way, here’s my quick snapshot of his beliefs and values:
1) Focus on something that has high value to someone else;
2) Go back to first principles, so as to understand things more deeply and widely, especially their implications;
3) Be very rigourous in your own self analysis; constantly question yourself, especially on the practicality of the idea(s) you have;
4) Be extremely tenacious in your pursuits;
5) Put in 100 hours or more every week, as sweat equity of intense efforts and focused execution count like hell;
6) Constantly think about how you could be doing better, faster, cheaper and smarter;
7) Relentlessly and ruthlessly think about how to make a better world;
Again, here’s my quick snapshot of his unique traits and characteristics:
ix) spiritual development (including contributions to society, volunteering, etc.);
2) Translate all your long-range goals and objectives in (1) into specific, prioritised and executable tasks that you need to accomplish daily, weekly, monthly, quarterly and even annually;
3) With the end in mind as formulated in (1) and (2), work out your start-point, endpoint and the developmental path of transition points in between;
4) Pinpoint specific tasks that you need to accomplish at each transition point till the endpoint;
5) Establish metrics to measure your progress, or milestone accomplishments;
6) Assign and allocate personal accountability, as some tasks may need to be shared, e.g. with team members, if any;
7) Identify and marshal resources that are required to get all the work done;
[I like to call them the 7 M’s: Money; Methods; Men; Machines; Materials; Metrics; and Mojo!]
8) Schedule a timetable for completion of each predefined task;
9) Highlight potential problems or challenges that may crop up along the Highway of Life, as you traverse on it;
10) Brainstorm a slew of possible strategies to deal with (9);
This is your contingency plan.
11) Institute some form of system, like a visual Pert Chart, to track, control and monitor your forward trajectory, as laid out in your systematic game plan, in conjunction with all the critical elements of (4) to (10);
12) Follow-up massively and follow-through consistently your systematic game plan;
13) Put in your sweat equity of intense effort and focused execution;
14) Stay focused on your strategic objectives, but remain flexible in your tactical execution;
You aren’t so stressed and nervous when you are practicing LeetCode, because your career doesn’t depend on how well you do while solving LeetCode.
When solving LeetCode, you aren’t expected to talk to the interviewer to get clarifications on the problem statement or input format. You aren’t expected to get hints and guidance from the interviewer, and to be able to pick them up. You aren’t expected to be able to communicate with other human beings in general, and to be able to talk about technical details of your solution in particular. You aren’t expected to be able to prove and explain your idea in clear, structured way. You aren’t expected to know how to test your solution, how to scale it, or how to adjust it to some unexpected additional constraints or changes. You may not be able to simply get constraints on input size and use them to figure out what is the complexity of expected solution. You have limited amount of time, so if you slowly got through most of the LeetCode, you may still struggle to get stuff done in 45 minutes. And many more… For all these things, you don’t need them to solve LeetCode, so you usually don’t practice them by solving LeetCode; you may not even know that you need to improve something there.
To sum it up: two main reasons are:
Higher stakes.
Lack of skills that are required at typical Google/Facebook interview, but not covered by solving LeetCode problems on your own.
You should also keep in mind that LeetCode isn’t the list of problems being asked at Google or Facebook interviews. If anything, it is more of a list of problems that you aren’t going to be asked, because companies ban leaked questions 🙂 You may get a question that is surprisingly different from what you did at LeetCode.
Originally Answered: I failed all technical interviews at Facebook, Google, Microsoft, Amazon and Apple. Should I give up the big companies and try some small startups?
Wanted to go Anonymous for obvious reasons.
Reality is stranger than Fiction.
In 2010: After graduation, I was interviewed by one of the companies mentioned above for an entry level Software Engineering Role. During the interview, the person tells me: ‘You can never be a Software Engineer’. Seriously? Of-course I didn’t get hired.
In 2013: I interviewed again with the same company but for a different department and got hired.
Fast Forward to 2016 Dec: I received 2 promotions since 2013 and now I am above the grade level of the guy who interviewed me. I remember the date, Dec 14 2016, I went to his desk and asked him to go out for a coffee. Initially he didn’t recognize me but later he did and we went out for a coffee. Needless to say, he was apologetic for his behavior.
For me, it felt REALLY GOOD. Its a story I’ll tell my Grandkids! 🙂
Big tech interviews at FAANG companies are intended to determine – as much as possible – whether you’ve got the knowledge and attributes to be a successful employee. A big part of that for software developers is familiarity with a good set of data structures and algorithms. Interview loops vary, but a good working knowledge of common algorithms will almost always come in handy for both interviews and the job.
Algorithm-related to questions I was asked in my first five years, or that I ask people with less than 5 years: sorting, searching, applying hashes correctly, mapping, medians and averages, trees, linked lists, traveling salesman (I was asked this a couple times, never asked it), and many more.
I never recommend an exhaustive months-long review before an interview, but it’s always a good idea to make sure you’re current on your basics: hash tables and sets, string operations, working with arrays and vectors and lists, binary trees, and linked lists.
Compared to other modern languages, python has two features that make it attractive, and then also make learning a second language difficult if you started with python. The first is that, despite some minor steps to allow annotation, python is loosely and dynamically typed. The second is that python provides a lot of syntactic sugar; this is shorthand, like a map function, where you can apply a function to each element in a data structure.
Do these features make it harder to switch to another language that is strongly and statically typed? For some people, yes, and for others, no.
Some programmers are naturally curious what’s happening under the hood. How are data being represented and manipulated? Why does an operation produce one type of result in one situation, and another type of result in another situation? If you are the kind of person who asks these questions, you are more likely to have an easier time transitioning. If you are a person who finds these questions uninteresting or even distasteful, transitioning to another language can be very painful.
I have excellent skills and experience on my resume, which makes it stand out.
Seriously, there is no magical spell that will make a crappy resume attractive to recruiters. Most people give up believing in magic after they are 5 or 6 years old. A software engineer who believes in magic is not a good candidate for hire.
All those complaints you have about their products? The people working there complain about the same exact things. Microsoft employees complain about how slow Outlook is. Google employees complain about everything changing all the time. Salesforce employees complain about how hard our products are to use.
So why don’t we do something about it? There are a few possible answers:
We are actively doing something about it right now and it will be fixed soon.
The problem is technically difficult to fix. For example, it’s currently beyond the state of the art to change the wake word (“Alexa”/”OK Google”) to a user-selected word. A variation of this is the problem that’s more expensive to fix than the amount of annoyance saved.
The team responsible for that functionality has problems. Maybe they have a bad manager or have been reorged a lot, and as a result they haven’t been doing a good job. Even once the problem is solved, it can take a long time to catch up.
The problem is related to making money. For example, Microsoft used to have a million different versions of Office, each including different programs and license restrictions. It was super confusing. But the bean counters knew how much extra money the company made from these bundles, compared to a simpler scheme, and it was a lot. So the confusion stayed.
The problem is cultural. For example, Google historically made its reputation by offering new features constantly. Everything about the culture was geared towards change and innovation. When they started making enterprise products, that cultural became baggage.
But none of that keeps the employees from complaining.
That’s perhaps the first stage of learning, recitation.
Using the four-stage model of learning that goes
Unconscious Incompetence
Conscious Incompetence
Conscious Competence
Unconscious Competence
that’s maybe a 2 to 2.5 there. You know you haven’t really understood why you are doing things that way and without detailed step-by-step, you don’t yet know how you would design those solutions.
You need to step back a bit, by reviewing some working solutions and then using those as examples of fundamentals. That might mean observing that there is a for() loop, for example – why? What is it there for? How does it work? What would happen if you changed it? If you wanted to use a for loop to write out “hello!” 8 times, how would you code that?
As you build up the knowledge of these fundamental steps, you’ll be able to see why they were strung together the way they were.
Next, practice solving smaller challenges. Use each of these tiny steps to create a solution – one where you understand why you chose the pieces you chose, what part of the problem it solves and how.
Early 2020 has been a very rough period for many companies who laid off tons of good people, many of which have bounced to a company who was not a good fit and eventually went to a third one. Forced remote work was also difficult for many folks. So in the current context, having changed 3 jobs in the last 4 years is really a non-event.
Now more generally, would my hiring recommendation be influenced by a candidate having changed jobs several times in a short period of time?
The assumption here is that if a candidate has switched jobs 3 times in 4 years, there must be something wrong.
I think this is a very dangerous assumption. There are lots of things that cause people to change jobs, sometimes choice, sometimes circumstances, and they don’t necessarily indicate anything wrong in the candidate. However, what could be wrong in a candidate can be assessed in the interview, such as:
is the candidate respectful? Is the candidate able to disagree consrtuctively?
does the candidate collaborate?
Does the candidate naturally support others?
Has the candidate experience navigating difficult human situations?
etc, etc.
There are a lot of signals we can detect in the interview and we can act upon them. Everything that comes outside of the interview / outside of reference check is just bias and should be ignored.
My IQ was around 145 the last time I checked (I’m 19).
I feel lots of gratitude for my ability to deeply understand and comprehend ideas and concepts, but it has definitely had its “downsides” throughout my life. I tend to think very deeply about things that I find interesting and this overwhelming desire to understand the world has led me to some dark places. When I was around 9 or 10, I discovered the feeling of existential panic. I had watched an astronomy documentary with my father (who is a geoscience professor) and was completely overwhelmed with the fact that I was living on an unprotected orb, orbiting around a star at speeds far faster than I could even comprehend. I don’t think anyone in my family expected me to really grasp what the documentary was saying so they were a bit alarmed when I spent that whole night and most of the next week panicking and hyperventilating in my bedroom.
I lost my mom to suicide when I was 11 which sent me into a deep depression for several years. I found myself thinking a lot about death and the meaning of human existence in my earlier teenage years. I was really unmotivated to do school work all throughout high school because I found no meaning in it. I didn’t understand why I was alive, or what being alive meant, or if there even was any true meaning to life. I constantly struggled to see how any of it truly mattered in the long run. What was the point of going to the grocery store or hanging out with my friends or getting a drivers license? I was an overdeveloped primate forced to live in and contribute to a social group that I didn’t ask to be in. I was living in a strange universe that made no sense and I was being expected to sit at a desk for 8 hours every day? Surrounded by people who didn’t care about anything except clothing and football games? No way man, count me out. I spent a lot of nights just sitting in my bedroom wondering if anything I did really mattered. Death is inevitable and the whole universe will one day end, what’s the point. I frequently wondered if non-existence was inherently better than existence because of all of the suffering that goes hand in hand with being a conscious being. I didn’t understand how anyone could enjoy playing along in this complex game if they knew they were all going to die eventually.
Heavy stuff, yeah.
When I was 18 I suddenly experienced what some people label as an “ego death” or a “spiritual awakening” in which it suddenly occurred to me that the inevitably of death doesn’t mean that life itself is inherently meaningless. I realized that all of my actions affect the universe and I have the ability to set off chain reactions that will continue to alter the world long after I’m gone. I also realized that even if life is inherently meaningless, then that is all the more reason to enjoy being alive and to experience the beauty and wonder of the world while I’m still around. After that day I began meditating daily to achieve a deeper awareness of myself and try to find inner peace. I began living for the experience of being alive and nothing else. All of this has brought me great peace and has allowed me to enjoy learning again. For so long learning was terrifying to me because it meant that I was going understand new information that could potentially terrify me. Information that I could not unlearn. I have become a very emotionally sensitive person after the death of my mother, so I simply could not handle the weight of learning about existential concepts for a while. Now that I’ve been able to find a state of peace within myself and radically accept the fact that I will die one day (and that I do not know what occurs after death) I have begun to enjoy learning again! I read a lot of nonfiction and fiction alike. I enjoy traveling and seeing the world from as many different perspectives as possible. Talking to new people and attempting to see my world through their eyes is very enjoyable for me. Picking up new skills is generally very easy for me and I spend a lot of my free time pondering philosophical issues, just because it’s fun for me. I’m not a very social person, I like having a few close friends, but I mostly enjoy being alone.
So all in all, I think having an IQ of 140+ is a very turbulent experience that can be very beautiful! When you are able to truly understand deep concepts, it can seriously freak you out, especially when you’re searching for meaning and answers to philosophical problems. If I hadn’t embraced a way of life that revolves around radically acceptance, I don’t think I would have the guts to look as deeply into some things as I do. However, since I do have that safety cushion, I’m able to shape my perception of the world with the knowledge that I learn. This allows me to see incredible beauty in our world and not take things too personally. When I have a rough day, all I need to do is sit on my roof for half an hour and look at the stars. It reminds me that I am a very small animal in a very big place that I know very little about. It really puts all of my silly human problems in perspective.
If you can explain to me how “no-code is the future”, maybe there’s a useful response to this.
As far as I can tell, “no-code” means that somebody already coded a generic solution and the “no-code” part is just adapting the generic solution for a specific problem.
Somebody had to code the generic solution.
As to the second part, “is a CS major even worth it?” I’ve had a 30+ year career in software engineering, and I didn’t major in CS. That hasn’t kept me from learning CS concepts, it hasn’t kept me from delivering good software, and it hasn’t stopped me from getting software jobs.
Is a CS major even worth it? Only the student knows the answer to that.
People have written no-English versions of many programming languages – but they aren’t used as much as you’d think because it’s just not that useful.
Consider the C language – there are no such English words as “int”, “bool”, ”enum”, “struct”, “typedef”, “extern”, or “const”. The words “auto”, “float” and “char” are English words – but with completely different meanings to how they are used in C.
This is the complete list of C “reserved words” – things you’d have to essentially memorize if you’re a non-English speaker…
…but very few of those words are used in their usual English meanings…and you have to just know what things like “union” mean – even if you’re a native english speaker.
But if you really think there is an advantage to this being your native language then:
#define changer switch
#define compteur register
#define raccord union
…and so on – and now all of your reserved words are in French.
I don’t think it’s going to help much.
IT”S ABOUT LIBRARIES AND DOCUMENTATION:
The problem isn’t something like the C language – we could easily provide translations for the 30 or so reserved words in 50 languages and have a #pragma or a command to the compiler to tell it which language to use.
No problem – easy stuff.
However, libraries are a much bigger problem.
Consider OpenGL – it has 250 named function, and hundreds of #defined tokens.
glBindVertexArray would be glLierTableauDeSommets or something. Making versions of OpenGL for 50 languages would be a hell of a lot more painful.
Then, someone has to write documentation for all of that in all of those languages.
But a program written and compiled against French OpenGL wouldn’t link to a library written in English – which would be a total nightmare.
Worse still, I’ve worked on teams where there were a dozen US programmers, two dozen Russians and a half dozen Ukrainians – spread over two continents – all using their own languages ON THE SAME PIECE OF SOFTWARE.
Without some kind of control – we’d have a random mix of variable and function names in the three languages.
So the rule was WE PROGRAM IN ENGLISH.
But that didn’t stop people from writing comments and documentation in Russian or Ukranian.
SO WHAT IS THE SOLUTION?
I don’t think there actually is a good solution for this…picking one human language for programmers to converse in seems to be the best solution – and the one we have.
There are 1.3 billion English speakers, 1.1 billion Mandarin speakers, 600 million Hindi speakers, 450 Spanish speakers…and no other language gets over half of that.
So if you have to pick a single language to standardize on – it’s going to be English.
Those who argue that Mandarin should be the choice need to understand that typing Mandarin on any reasonable kind of keyboard was essentially impossible until 1976 (!!) by which time using English-based programming languages was standard. Too late!
SO – ENGLISH IT IS…KINDA.
Even though we seem to have settled on English the problems are not yet over.
British English or US English – or some other dialect?
As a graphics engineer, it took me the best part of a decade to break the habit of spelling “colour” rather than “color” – and although the programming languages out there don’t use that particular word – the OpenGL and Direct3D libraries do – and they use the US English spelling rather than the one that people from England use in “English”.
ARE PROGRAMMERS UNIQUE IN THIS?
No – we have people like airline pilots, ships’ captains.
ICAO (International Civil Aviation Organization), require all pilots to have attained ICAO “Level 4” English ability. In effect, this means that all pilots that fly international routes must speak, read, write, and understand English fluently.
However, that’s not what happened for ships. In 1983 a group of linguists and shipping experts created “Seaspeak”. Most words are still in English – but the grammar is entirely synthetic. In 1988, the International Maritime Organization (IMO) made Seaspeak the official language of the seas.
Here’s the thing. The compensation will never be comparable.
When you join a big tech, public company, all of your compensation is public. Also it’s relatively easy to get a fair estimate of what comp looks like a few years down the road.
When you join a private company, the comp is a bet on a successful exit.
In 2015, Zenefits was a super hot company. Zoom had been around for.4 years and was very confidential.
In a now infamous Quora question[1] a user asked wether they should take an offer at Zenefits or Uber. As a result, The Zenefits CEO rescinded their offer. But most people would have chosen an offer at Zenefits or Uber, whose IPO was the most anticipated back then, over one at Zoom.
And yet Zenefits failed spectacularly, Uber’s IPO was lackluster, while Zoom went beyond all expectations.
So this is mostly about to risk aversion. Going to a large co means a “golden resume” that will always get you interviews, so it has a lot of long term value.
Working in a large company has other benefits. Processes are usually much better and there’s a lot to learn. This is also the opportunity to work on some problems at a huge scale. No one has billions of users outside of Google, Meta, Apple or Microsoft.
But working in a small private company whose valuation explodes is the only way for a software engineer to become very wealthy. The thing is though that it’s impossible for an aspiring employee to tell which company is going to experience that growth versus fail.
The pro’s and con’s really depend on the specific situation.
(1) When quitting for a new position…
Pros:
Better pay & benefits
More promotion opportunities
New location
New challenges (old job may have been boring)
New job aligned to your interests.
Cons:
New job/company was seriously misrepresented
“New boss same as the old boss” (no company is perfect!)
You might have wanted a new challenge, but you are now over your head.
Note: if you have a job and are not desperate, please do your homework and remember you are also interviewing them! You want a better job in most cases (unless that moving thing is going on).
(2) When quitting over a conflict…
Pros:
Can sleep at night (providing it was a ethical issue and you were in the right)
You showed them who is the boss!
Plus, you wont be on the local news if they get sued, or the IRS does a audit.
Again, if it was a toxic environment that you get to live as opposed to a stroke on the job! No job is worth it that is impacting your health, including mental health.
Cons:
No unemployment in most states if you just up and quit.
Job search with no income puts a lot of pressure at some point to take any job
the good news though, is you can continue looking while earning a paycheck (and hopefully still growing skills & experience)
The reason so many people are quitting now…
Note there is a third category, when you quit due to a lifestyle change. In this case, we are looking a women quitting to be a full-time mother, or someone going back to school. A spouse getting promoted but with a move might also place the other mate in this position…
Pro:
You get to live the life you want.
You are preparing for a better career
Con:
Loss of income
Reduced social interaction (for the full-time mom)
Note here that most couples that decide to do the stay at home mom generally plan ahead so one income will cover their expenses.
Second, I also don’t consider serious health issues when you leave the work force in general to fall under the scope of this discussion.
Originally Answered: Is practicing 500 programming questions on LeetCode, HackerEarth, etc enough to prepare for Google interview?
If you have 6 months to prepare for the interview I would definitely suggest the following things assuming that you have a formal CS degree and/or you have software development experience in some company:
Step 1 (Books/Courses for good understanding)
Go through a good data structure or algorithms book and revise all the topics like hash tables, arrays and strings, trees, graphs, tries, bit hacks, stacks, queues, sorting, recursion, and dynamic programming. Some good books according to me are:
The Stanford Coursera algorithms courses are also very good and you can look at them if you have time. It’s a bit more theoretical though.
Step 2 (Programming practice for algorithms and data structures)
Once you are done with Step 1 you need a lot of practice. It need not be a set number of problems like 500 or 1000. The best way to practice problems is to mimic an interview setting and time yourself for half an hour and solve a problem without any distraction. The steps here are to read a problem, think of a brute force solution that works very quickly, and then think of an optimized version that works and then write clean working code and come up with test cases within half an hour. Most of the top companies ask you 1 or 2 medium problems or 1 hard problem in 45 mts to 1 hour. Once you are done solving the problem you can compare your solution with the actual solution and see if there is scope to improve your solution or learn from the actual solution.
If you do the math it takes half an hour to solve a problem and at least 15 mts to look and compare with the correct solution. So 500 problems take 500 * 45 mts = 375 hours. Even if you spend 5 solid hours a day for problem-solving it comes to 75 days (2.5 months). If you are in a full-time job it’s hard to spend so much time every single day. Realistically if you spend 2–3 hours a day we are talking about 5 months just for practicing 500 problems. In my opinion, you don’t need to solve so many problems to crack the interview. All you need is a few problems in each topic and understand the fundamentals really well. The different topics for algo and ds are:
arrays and strings, bit hacks, dynamic programming, graphs, hash tables, linked lists, math problems, priority queues, queues, recursion, sorting, stacks, trees, and tries. As a starter try to solve 4–5 problems in each topic after you finish step 1 and then if you have time solve 2–3 problems a day for fun in each topic and you should be good. Also, it is far better to solve 5 problems than to read 50 problems. In fact, trying to cover problems by reading problems is not going to be of any use.
Step 3 (this can be done in parallel with step 1) (Systems Design)
Practice problems in systems, design (distributed systems, concurrency, OO design). These questions are common in Google and other top companies. The best way to crack this section is to actually do complex systems projects at work or school projects. There are lots of resources online which are very good for preparation for this topic.
Edit: Since I have received some request to point some resources I am listing some of my favorite ones:
Please know your resume in and out and make sure you can explain all the projects mentioned in the resume. You should be able to dive as deep as needed (technically) for the projects mentioned. Also do enough research about the company you are interviewing, the product, engineering culture and have good questions to ask them
Step 5 (mock interviews)
Last but not least please make sure you have some good friends working in a good company or your classmate mock interview you. You also have several resources online for this service. Also, work on the feedback you get from the mock interview. You can also interview a few companies you are not interested to work as a practice interview before your goal companies.
It is possible for some people; I don’t know whether it is possible for you.
You’re solving 50% of easy problems. Reality check: that’s…cute. Your target success rate, to have a good chance, should be near-100% on Easy, 75% on Medium, and 50% on Hard. On top of that, non-Leetcode rounds like system design should be solid, too.
You can see there’s a big gap between where you are and where you need to be.
The good news is that despite how large that gap is, without a doubt, there have been cases of people being able to learn fast enough to cover that gap in 90 days. These cases are not at all common, and I will warn you that the vast majority of people who are where you are now cannot get to where you need to be in 90 days. So, the odds are against you, but you might be better than the odds would say.
What is special about the situations of the people who can get there that fast? Off the top of my head, the key factors are:
A strong previous background in CS and algorithms
Being able to spend a significant amount of time daily to study
High aptitude / talent / intelligence for learning these sorts of concepts
Having an effective methodology for learning. The fact that you’re actively solving problems on Leetcode is a decent start here.
If the above factors describe you, you might be better off than the odds would suggest. It is at least possible that you could achieve your goal.
(Note: I’ve interviewed hundreds of developers in my time at Facebook, Microsoft and now as the co-founder and CEO of Educative. I’ve also failed several coding interviews because I wasn’t prepared. At Educative, we’ve helped thousands of developers level up their careers with hands-on courses on programming languages, system design, and interview prep.)
Is Interview Prep a Full-time Job?
Let’s break it down. A full-time job – 40 hours per week, 52 weeks per year – encompasses 2080 hours. If you take two weeks of vacation, you’re actually working 2,000 hours. The 1,000 hours recommendation is saying you need six months of full-time work to prepare for your interview at a top tech company. Really?
I think three months is a reasonable timeframe to fully prepare. And if you’ve interviewed more recently, studying the specific process of the company where you’re applying can cut that time down to 4-6 weeks of dedicated prep.
I’ve written more about the ideal interview prep roadmap for DEV Community, but I’ll give you the breakdown here.
The “Secret” to a Successful Interview Prep Plan
First of all, I want to be clear that there’s no silver bullet to interview prep. But during my time interviewing candidates at Facebook and Microsoft, I noticed there was one trait that all the best candidates shared: they understood why companies asked the questions they did.
The key to a successful interview prep program is to understand what each question is actually trying to accomplish. Understanding the intent behind every step of the interview process helps you prepare in the right way.
A lot of younger developers think they need to be experts in a few programming languages, or even just one language in order to crack the developer interview. Writing efficient code is a crucial skill, but what software companies are actually looking for (especially the big ones with custom libraries and technology stacks that you will be expected to learn anyway) is an understanding of the various components of engineering, as well as your creative problem-solving ability.
That breaks down into five key areas that “Big Tech” companies are focused on in the interview process:
1. Coding
Interviewers are testing the basics of your ability to code. What language should you be using? Start with the language you know best. Especially in larger companies, new syntaxes can be taught or libraries used if you establish you can execute well. I have interviewed people that used programming languages that I barely know myself. I know C++ inside and out, so even though Python is a more efficient language, I would always personally choose to interview using C++. The most important thing is just to brush up on the basics of your favorite programming language.
The questions in coding interviews focus on generic problem-solving, data structures (Mastering Data Structures: An interview refresher), and algorithms. So revisit concepts that you haven’t touched since undergrad to have a fresh, foundational understanding of topics like complexity analysis (Algorithms and Complexity Analysis: An interview refresher), arrays, queues, trees, tries, hash tables, sorting, and searching. Then practice solving problems using these concepts in the programming language you have chosen.
Whether you’re building a mobile app or web-scale systems, it’s important to understand threads, locks, synchronization, and multi-threading. These concepts are some of the most challenging and factor heavily into your “hiring level” at many organizations. The more expert you are at concurrency, the higher your level, and the better the pay.
Since you’ve already determined the language you’re using in (1), study up on process handling using that same language. Prepare for an interview – Concurrency
3. System Design
Like concurrency problems, system design is now key to the hiring process at most companies, and has an impact on your hiring level.
There isn’t a clear-cut answer to an open-ended question where a candidate must work their way to an efficient, meaningful solution to a general problem with multiple parts.
Most candidates don’t have a background designing large-scale systems in the first place, as reaching that level is several years into a career path and most systems are designed collaboratively anyway.
For this reason, it is important to spend time clarifying the product and system scope, a quick back-of-the-envelop estimation, defining APIs to address each feature in the system scope and defining the data model. Once this foundational work is done, you can take the data model and features to actually design the system.
In Object-Oriented Design questions, interviewers are looking for your understanding of design patterns and your ability to transform the requirements into comprehensible classes. You spend most of your time explaining the various components, their interfaces and how different components interact with each other using the interfaces. Interviewers are looking for your ability to identify patterns and to apply effective, time-tested solutions rather than re-inventing the wheel. In a way, it is the partner of the system design interview.
This is the one that doesn’t have a clear cut learning path, and because of that, it is often overlooked by developers. But for established companies like Google and Amazon, culture is one of the biggest factors. The skills you demonstrate in coding and design interviews prove that you know programming. But without the right attitude, are you open to learning? Are you passionate about the product and want to build things with the team? If not, companies can think you’re not worth hiring. No organization wants to create a toxic work environment.
Since every company has a few different distinguishing features in their culture, it’s important to read up on what their values and products are (Coding Interview Preparation | Codinginterview has information on many top tech companies, including Google and Facebook). Then enter the interview track ready to answer these basics:
Interest in the product, and demonstrate understanding of the business. (Don’t mistake Facebook’s business model, which relies on big data, for AWS or Azure, which facilitate big data as a service. If you’re going into Google, know how user data and personalization is the core of Google’s monetization for its various products and services, while knowing what makes Android unique compared to iOS. Be an advocate.)
Be prepared to talk about disagreements in the workplace. If you’ve been working for more than a few years, you’ve had disagreements. Even if you’re coming out of school, group projects apply. Companies want to know how you work on a team and navigate conflict.
Talk about how the company helps you build and execute your own goals both as a technologist and in your career. What are you passionate about?
Talk about significant engineering accomplishments – what have you built; what crazy/difficult bugs have you solved?
Conclusion
Strategic interview prep is essential if you want to present yourself as the best candidate for an engineering role.
It doesn’t have to take 1,000 hours, nor should it – but at big companies like Google and Facebook where the interview process is so intentional, it will absolutely benefit you to study that process and fully understand the why behind each step.
There are plenty of battle-tested resources linked in my answer that will guide you throughout the prep process, and I hope they can be helpful to you on your career journey.
Originally Answered: I have practiced over 300 algorithms questions on LintCode and LeetCode but still can’t get any offer, what should I do?
I have interviewed and been interviewed a number of times, and I have found out that most of the time people (including myself) flunk an interview due to the following reasons:
Failing to come up with a solution to a problem: If you can’t come up with even one single solution to a problem, then it’s definitely a red flag since that reflects poorly on your problem solving skills. Also, don’t be afraid to provide a non-optimal solution initially. A non-optimal solution is better than no solution at all.
Coming up with solutions but can’t implement them: That means you need to work more on your implementation skills. Write lots and lots of code, and make sure you use a whiteboard or pen and paper to mimic the interview experience as much as possible. In an interview you won’t have an IDE with autocomplete and syntax highlighting to help you. Also make sure that you’re very comfortable in your programming language of choice.
Solving the problem but not optimally: That could mean that you’re missing some fundamental knowledge of data structures and algorithms, so make sure that you know your basics well.
Solving the problem but after a long time, or after receiving too many hints: Again, you need more problem solving practice.
Solving the problem but with many bugs: You need to properly test your code after writing it. Don’t wait for the interviewer to point out the bugs for you. You wouldn’t want to hire someone who doesn’t test their code, right?
Failing to ask the interviewer enough questions before diving into the code: Diving right into the code without asking the interviewer enough questions is definitely a red flag, even if you came up with a good solution. It tells the interviewer that either you’re arrogant, or that you’re reckless. It’s also not in your favor, because you may end up solving the wrong problem. Discussing the problem and asking questions to the interviewer is important because it ensures that both of you are on the same page. The interviewer’s answers to your questions may also provide with some very useful hints that may greatly simplify the problem.
Being arrogant: If you’re perceived as arrogant, no one will want to hire you no matter how good you are.
Lying on the resume: Falsely claiming knowledge of something, or lying about employment history is a huge red flag. It shows dishonesty, and no one wants to work with someone who is dishonest.
I hope this helps, and good luck with your future interviews.
Unless we’re talking about Google, which has problems that are unique to them in comparison to the rest, you can be sure that big tech companies ask LeetCode-style questions quite often. Seeing LeetCode Hard problems specifically, however, is not that common in these interviews, and it’s more likely that you’ll be facing LeetCode Medium questions and one or two Hard questions at best. This is because having a time limit to solve them as well as an interviewer right beside you already adds enough pressure to make these questions feel harder than they normally would be; increasing their difficulty would simply be detrimental to the interviewing process.
I suggest that you avoid using the difficulty of LeetCode questions that you can solve as a way of telling if you’re prepared for your interviews as well because it can be pretty misleading. One reason this is the case is that LeetCode’s environment is different from an interviewing environment; LeetCode cares more about running time and the optimal solution to a problem, while an interviewer cares more about your approach to the question (an intuitive solution can always be optimized further with a discussion between you and the interviewer).
Another reason you should avoid worrying too much about LeetCode-style questions is that FAANG companies are starting to refrain from asking them, as they’re noticing that many candidates come to their interviews already knowing the answer to some of their questions; currently, if your interviewer notices that you already know the answer to the question you’re given, they won’t take it into account and instead will move on to another question, as already knowing how to solve the problem tells them nothing about the way you approach challenging situations in the first place.
Also, you should consider that LeetCode only lets you practice what you already know in coding; if you don’t have a good knowledge of data structures & algorithms beforehand, LeetCode will be a difficult resource to use efficiently, and it also won’t teach you anything about important non-technical skills like communication skills, which is a crucial aspect that interviewers also evaluate. Therefore, I also suggest that you avoid using LeetCode as your only resource to prepare for your technical interviews, as it doesn’t cover everything that you need to learn on its own.
For example, you may want to enroll in a program like Tech Interview Proas you use LeetCode. TIP is a program that was created by an ex-Google software engineer and was designed to be a “how to get into big tech” course, with over 20 hours of instructional video content on data structures & algorithms and system design.
Another good resource that you could use, this time to cover the behavioral aspect of interviews, is Interviewing.io. With it, you can engage in mock interviews with other software engineers that have worked with Facebook and Google before and also receive feedback on your performance.
You could also read a book like Cracking the Coding Interview, which offers plenty of programming questions that are very similar to what you can expect from FAANG companies, as well as valuable insight into the interviewing process.
Harvard is seen in popular culture as being very selective, and so any funnel which has a conversion rate lower than 5% is going to describe itself as “more selective than Harvard”. “More selective than Harvard” has 70m hits on Google. When Walmart opened a DC store, it hired about 2.5% of the people that sent applications, and ran a story that it was “twice as selective as Harvard”. Tech internships, somewhat unsurprisingly, are harder to get as jobs at Walmart.
Generally speaking, the more LeetCode problems you solve, the better your odds of getting an offer will be. Be careful, however, as using the number of problems you solve on LeetCode as a reference for how ready you are for your technical interviews is misleading, especially if it’s for Google and Facebook. Even if you solve every problem on LeetCode (please don’t try this), there’s still a chance you won’t get an offer, and there are several reasons why.
First of all, coding is not the only thing taken into consideration by interviewers from big tech companies. One of the main things they look for in a candidate is the presence of strong soft skills like teamwork, leadership, and communication. If you’re raising red flags in that department—if the interviewer doesn’t think you have the leadership skills to lead a team down the road, for example—odds are that you’re going to get overlooked. They also expect you’ll be able to clearly explain your thought process before solving a given coding problem, which is something a surprising number of developers have trouble with.
The second problem with using LeetCode alone is that it can only help you practice data structures & algorithms and system design, but not exactly teach you about them. This might not be an issue if you’re solving questions from the Easy section of LeetCode, but once you get to the Medium and Hard problem sets, you’ll need more theoretical knowledge to properly handle these problems.
So, ideally, you’ll want to prepare using resources that help you learn more about DS&A and systems design before you start practicing on LeetCode, and you’ll also want to work on your behavioral skills to ensure you do well there, too. Here are some tools that can help:
Interviewing.io: A site where you can engage in mock interviews with other software engineers—some of whom have worked at Google and Facebook—and receive immediate, objective feedback on your performance.
Tech Interview Pro: An interview prep program designed by a former Google software engineer that includes 150+ instructional video lessons on data structures & algorithms, systems design, and the interview process as a whole. TIP members also get access to a private Facebook group of 1,500+ course graduates who’ve used what they learned in the course to land jobs at Google, Facebook, and other big tech companies.
So, using LeetCode on its own would prepare you well for questions about data structures & algorithms, but may leave you unprepared for questions related to systems design and the behavioral aspect of your interviews. But by complementing LeetCode with other resources, you’ll put yourself in a much better position to receive an offer from Google, Facebook, or anyone else. Best of luck.
Dmitry Aliev is correct that this was introduced into the language before references.
I’ll take this question as an excuse to add a bit more color to this.
C++ evolved from C via an early dialect called “C with Classes”, which was initially implemented with Cpre, a fancy “preprocessor” targeting C that didn’t fully parse the “C with Classes” language. What it did was add an implicit this pointer parameter to member functions. E.g.:
struct S {
int f();
};
was translated to something like:
int f__1S(S *this);
(the funny name f__1S is just an example of a possible “mangling” of the name of S::f, which allows traditional linkers to deal with the richer naming environment of C++).
What might comes as a surprise to the modern C++ programmer is that in that model this is an ordinary parameter variable and therefore it can be assigned to! Indeed, in the early implementations that was possible:
struct S {
int n;
S(S *other) {
this = other; // Possible in C with Classes.
this->n = 42; // Same as: other->n = 42;
}
};
Interestingly, an idiom arose around this ability: Constructors could manage class-specific memory allocation by “assigning to this” before doing anything else in the constructor. E.g.:
struct S {
S() {
this = my_allocator(sizeof(S));
…
}
~S() {
my_deallocator(this);
this = 0; // Disabled normal destructor post-processing.
}
…
};
That technique (brittle as it was, particularly when dealing with derived classes) became so widespread that when C with Classes was re-implemented with a “real” compiler (Cfront), assignment to this remained valid in constructors and destructors even though this had otherwise evolved into an immutable expression. The C++ front end I maintain still has modes that accept that anachronism. See also section 17 of the old Cfront manual found here, for some fun reminiscing.
When standardization of C++ began, the core language work was handled by three working groups: Core I dealt with declarative stuff, Core II dealt with expression stuff, and Core III dealt with “new stuff” (templates and exception handling, mostly). In this context, Core II had to (among many other tasks) formalize the rules for overload resolution and the binding of this. Over time, they realized that that name binding should in fact be mostly like reference binding. Hence, in standard C++ the binding of something like:
struct S {
int n;
int f() const {
return this->n;
}
} s = { 42 };
int r = s.f();
is specified to be approximately like:
struct S { int n; } s = { 42 };
int f__1S(S const &__this) {
return (&__this)->n;
}
int r = f__1S(s);
In other words, the expression this is now effectively a kind of alias for &__this, where __this is just a name I made up for an unnamable implicit reference parameter.
C++11 further tweaked this by introducing syntax to control the kind of reference that this is bound from. E.g.,
struct S {
int f() const &;
int g() &&;
};
can be thought of as introducing hidden parameters as follows:
int f__1S(S const &__this);
int g__1S(S &&__this);
That model was relatively well-understood by the mid-to-late 1990s… but then unfortunately we forgot about it when we introduced lambda expression. Indeed, in C++11 we allowed lambda expressions to “capture” this:
struct S {
int n;
int f() {
auto lm = [this]{ return this->n; };
return lm();
}
};
After that language feature was released, we started getting many reports of buggy programs that “captured” this thinking they captured the class value, when instead they really wanted to capture __this (or *this). So we scrambled to try to rectify that in C++17, but because lambdas had gotten tremendously popular we had to make a compromise. Specifically:
we introduced the ability to capture *this
we allowed [=, this] since now [this] is really a “by reference” capture of *this
even though [this] was now a “by reference” capture, we left in the ability to write [&, this], despite it being redundant (compatibility with earlier standards)
Our tale is not done, however. Once you write much generic C++ code you’ll probably find out that it’s really frustrating that the __this parameter cannot be made generic because it’s implicitly declared. So we (the C++ standardization committee) decided to allow that parameter to be made explicit in C++23. For example, you can write (example from the linked paper):
struct less_than {
template <typename T, typename U>
bool operator()(this less_than self,
T const& lhs, U const& rhs) {
return lhs < rhs;
}
};
In that example, the “object parameter” (i.e., the previously hidden reference parameter __this) is now an explicit parameter and it is no longer a reference!
Here is another example (also from the paper):
struct X {
template <typename Self>
void foo(this Self&&, int);
};
struct D: X {};
void ex(X& x, D& d) {
x.foo(1); // Self=X&
move(x).foo(2); // Self=X
d.foo(3); // Self=D&
}
Here:
the type of the object parameter is a deducible template-dependent type
the deduction actually allows a derived type to be found
This feature is tremendously powerful, and may well be the most significant addition by C++23 to the core language. If you’re reasonably well-versed in modern C++, I highly recommend reading that paper (P0847) — it’s fairly accessible.
When an employee is hired, there is a step in the process where they are given a stack of documents to sign that (anecdotally) I’ll venture maybe 1 in 1,000 actually read. One of the least understood (or read) is the notice that the company controls, collects and analyzes all communications, internet activity and data stored on company-owned or -managed devices and systems.
This includes network traffic that flows across their servers. It’s safe to assume that mid-to-large employers are fully aware of the amount of on-the-clock time employees spend shopping, tweeting or watching YouTube, and know which employees are spending inordinate amounts of ‘company time’ shopping on Amazon rather than tackling assignments.
This also include Bring Your Own Device policies— where employees are allowed to use their personal smartphone, tablet or laptop for business purposes. Companies don’t always ‘exploit’ the policy for nefarious surveillance purposes, but employers are within their rights to collect information like location data from your BYOD smartphone both on and off the clock.
An example of where this can hurt employees is when they start to look for another job.
If you email/Slack/message your supervisor and ask for a personal day off to attend to a family matter, but your device logs show you are accessing job-search sites and your location data suggests your aren’t at home or even within the radius of a competitor’s office, they know. This tends to make your boss cranky, and can adversely impact your employment to the point of losing your job.
I disagree with this kind of intrusive surveillance, and the presumption of guilt employees face when they take steps to protect themselves by using encrypted tools like Signal, proxy servers or switching devices to Airplane Mode intrudes on the employee’s legitimate rights to privacy: you may not want your employer to know that you’re seeing a psychiatrist on your lunch hour, and they really have no reasonable expectation for you to disclose this (or not take steps to conceal it.)
I think so. I remember there was a noticeable number of people going to Facebook, and some discussion of it among the employees. And then there was an explicit event where Google rearranged its compensation strategy. Everyone got a huge raise just at that moment, and from that point on the salaries and stock grants became close to the top of the market, as they need to be for a company that hires top talent.
If you can’t get FAANG to pay attention to you, you probably need to get another job first. Perhaps one of the companies that are considered to be pretty good would be interested.
It is actually quite hard to get an entry-level role at a top tech company, because where you went to college (and internships, which you don’t have) plays a disproportionate role. It’s not surprising, because what else can they go on? Interviewing is expensive, and there are hundreds of applicants per opening, so they want to pre-filter candidates somehow.
Once you have a few years of experience, things look a little better, especially if you climb up the prestige pole. For instance, Microsoft (or Twitter where I work today) isn’t FAANG, but you can be sure that recruiters would take applicants from there seriously, and you would have a good chance to get an interview. But the main factor is what you manage to do in your time at work. If you do well, get promoted, demonstrate clear impact (that you can articulate externally), build your professional network, that would improve your chances to both get your foot in the door, and also to pass the interviews.
There are also other things you can do, but I think they depend on luck too much. Slowly improving your portfolio is the way to go, I think.
All of these companies assume that if you know the front-end domain, you can learn whatever technology du jour to become a front-end developer, and besides, if you don’t know anything about front-end, you can still grow into a front-end developer if that’s the path you’re interested in.
That being said, TypeScript is increasingly becoming the standard way to write client-side web code. Both Microsoft and Google are very committed to TS, while Facebook uses JavaScript with Flow. Google also uses Dart for some of its front end.
Likewise, there are a number of technologies on which the larger companies have taken diverging choices. Google is very committed to gRPC, I mean, g stands for Google; while Facebook is behind graphQL. (graph being, originally. the “social graph” of Facebook). AFAIK, Microsoft uses both.
Neither Google nor Facebook have ever really embraced node.js. This would have seemed odd a few years ago but now the web ecosystem is generally turning away from tools and web servers written in node.js. I don’t know for sure what Microsoft uses for its web servers.
Facebook is unsurprisingly very committed to React and React Native. Google though uses a number of web frameworks, including non-open sourced ones, and among others Angular and Flutter. Microsoft, AFAIK, uses React and React Native and Angular.
But all these skills are transferable. If you understand React, it’s easy to learn Angular and conversely; TypeScript and Flow have similarities, etc.
One common denominator is HTML, CSS, web APIs and web standards, which are always relevant.
Your goal, in an interview, is not to impress your interviewer, but to demonstrate that you have the necessary skill set to be hired.
In a large tech company, the threshold to be considered “impressive” is pretty high… you have people that had superlative achievements in their field (or outside of tech), and in their day to day they’re just treated like normal people. I never interviewed for Amazon, but I interviewed (and got hired) at both Facebook and Google, and both of my interviewer brackets included folks who had their own Wikipedia entry (and since then, all of my Facebook interviewers had amazing careers and most got their own Wikipedia page). So that’s the caliber of folks that your interviewers work with on a daily basis.
So your interviewer is not going to be impressed by your interview performance. That said, I’ve observed that many tech employees treat others as if they could be the next Ada Lovelace or the next Steve Jobs no matter their current achievements. This is not forced, but it’s an attitude that comes naturally because we’ve observed so many people achieve greatness. Interviewers would love nothing more than to give the highest recommendation for the candidate that they are seeing right now, it’s very fulfilling (conversely, having to reject a candidate is always a bit frustrating). So I think it’s fair that your interviewer is hoping you can become a superstar, but that hope is the same as for every other candidate and not directly linked to how well you are doing right now.
Google’s interview process leans towards making sure that an unsuitable candidate is not hired, they are ok if a few suitable candidates are missed in the process.
There is also a factor of chance involved in the process. Here is a story to prove that:
I have personally asked at least 5 engineers at Google if they would be willing to interview again assuming they would be offered 1.5 times their current compensation. Obviously they loose the job if they don’t clear the interview. I am yet to meet somebody willing to take this bargain , I wont take it either.
Btw google also offers anybody who leaves google to comeback and join at the same level without an interview if they comeback within 2 years. My guess is that they also realize the chance involved.
Not clearing an interview at google is an indicator of only one thing, that you did not clear a google interview. Don’t draw conclusions about your ability based on this.
At Google there’s a selection of laptops you can choose from: a couple of Macs, a couple of Chromebooks, a couple of Linux laptops and a couple of windows laptops. Usually there’s a smaller, lighter version, for people who favor portability, and a larger version if you prefer a larger screen.
I’ve seen developers use all. I’d guess that Macs are most common (but under 50%} and Windows machines are least common.
I use a Chromebook (well, two Chromebooks). You turn it on, you log in and it looks exactly the same as your other Chromebook. This saves me carrying a laptop between work and home. If you work from another office, you don’t need to carry your laptop, you just grab one off the shelf, log in, and it looks the same as the computer you left at home.
(I tried using a Mac, I couldn’t get used to it, I didn’t know how to do anything, the keyboard shortcuts drove me crazy and so I gave it back and got a Chromebook).
Google and Meta (formerly Facebook) have a long-standing culture where employees believe that they’re hot stuff and that the company has to keep them happy because the company needs them as much as they need the company. Amazon doesn’t have that, probably because they fire people pretty often, making many of the remaining employees feel disposable.
Google and Meta have different concepts of culture fit—or at least they did historically. At Google, culture fit means “don’t be a person who’s hard to work with”. At Meta, culture fit means “be a person who believes that we are doing great things here and who will be excited to work hard on those great things”. As a result, it tends to be easy for Meta to keep convincing their existing employees that the company is doing the right thing. Google, on the other hand, ends up with a significant proportion of employees who are not easily convinced, and demand change.
Though it’s been so long since I’ve actually worked in the tech industry that I’m not sure if Meta still fits the description I gave above, and there are signs that Google has been trending away from the description I gave above.
The question was:
Why is employee activism seen more in Google but not in other companies like Facebook and Amazon?
Just to add a small note to Dimitriy’s great answer, computer science PhDs tend to be analytical and hyperrational. Working for Google is probably the single best “pass” to choosing whatever the hell you want for the rest of your career, or at least for the next step or two. I think some CS PhDs work for Google not because it’s what they want, but because they don’t know what they want, and if you don’t know what you want and you can get a job there, it would be hard to do better than Google. Why not make $250,000 a year while figuring out your next step? The other companies in this so-called “top-tier” have issues; they are potentially great employers, but their issues make them anywhere from slightly to dramatically less attractive.
The main factor why top prop trading firms and hedge funds are difficult to get into compared to tech companies is their size.
According to Wikipedia Two Sigma has about 1600 employees[1] and Jane Street has about 1900 employees .[2] Even the largest hedge fund, Bridgewater, only has 1500[3] and the third largest hedge fund, Renaissance Technology manages $130 billion with 310 employees.
Maybe these numbers on Wikipedia aren’t exact but I’d bet they’re well within the ballpark of being accurate.
Facebook has nearly 60,000 employees ,[4] Amazon has 160,000 ,[5] Apple has 154,000,[6] Netflix has around 12,000[7], and Google has 140,000[8]. Again, maybe these number aren’t precise but I don’t feel like doing more in depth research.
However, it’s pretty obvious to see that the big tech companies employ multiples of what those finance firms do and quite simply there are far more opportunities at those tech companies. More seats mean it’s going to be less competitive to be hired.
Second, those top hedge funds and prop trading firms pay well. Like really well.
And Jane Street’s 2020 graduate hires straight from college were paid a $200k annual base salary, plus a $100k sign-on bonus, plus a $100k-$150k guaranteed performance bonus. Junior bankers’ high salaries look a little paltry by comparison.[9]
So a new college grad makes $400-$450k. That’s a 22–23 year old making that. That same article found documents that said the average per employee in their London office was $1.3 million. Some make more and some make less, but that’s an eye wateringly high number when you consider all of the admin and support aren’t making close to that.
A friend’s younger brother worked at Jane Street about 10 years ago. He may still but I haven’t talked to her much since we moved. He was a rock star at Jane Street, and while I’m relying on my memory of a 10 year old conversation so I may not be totally accurate, he was in his late 20’s or early 30’s and made $4 million (and it may actually have been $8M) that year.
I know tech people are paid well but I doubt many, if any, make $400-$450k in year one and are making millions by their late 20’s is unheard of unless they founded or join a startup at the right time.
In addition, the interview processes at those firms is insanely difficult. I’ve never worked or interviewed at them but I’ve heard war stories. Just to get your foot in the door is nearly impossible then getting an offer to work there is basically impossible
My friend’s brother was half way through an absolutely top PhD program in Physics when he was recruited by them. I don’t consider myself a slouch and I’ve met a ton of highly intelligent people, but this guy was like his brain was plugged into a computer and the internet. And he was a dynamic personality.
They hire the absolute best of the best and because they’re small and privately held they don’t actually ever need to hire or grow because the public markets can’t punish their stock price because they don’t have one. If some of those top investment firms can’t find the right fit they may simply not need to make a hire right then and can wait. They’re not big banks like Goldman that need to hire X number of analysts and associates because they need to replace the people who left.
So the main reasons that it’s tougher to get into a top hedge fund or prop trading firm than big tech is because they’re much smaller, they pay more, they are even more diligent in their hiring practices, and they hire very intelligent people.
If that were to happen, we’ll have bigger problems to deal with. The Google monorepo exists on tens of thousands of machines. That would mean: every data center, every workstation used by Google would suddenly be out of commission – not just turned off, but so that storage isn’t even available. This is only possible in a complete doomsday scenario.
It’s generally possible to find better compensated jobs for people with experience in big tech cos. This experience is very desirable for companies in fast growth mode – not just the technical expertise but also knowledge of processes of world-class engineering organizations. Smaller but fast-growing companies can offer better packages but with an element of risk – if the company ends up failing, the employee will only get their salary.
To Conclude:
The tech industry is booming, and there are a lot of great opportunities for those with the skills and experience to land a job at one of the FAANG companies. Google, Facebook, Amazon, Apple, Netflix, and Microsoft are all leaders in the tech industry, and they offer competitive salaries and benefits. The interview process for these companies can be intense, but if you’re prepared and knowledgeable about the company’s culture and values, you’ll have a good chance of landing the job. Perks at these companies can include free food and transportation, stock options, and generous vacation time. If you’re looking for a challenging and rewarding career in the tech industry, consider applying for a job at one of the FAANGM companies.
Simple thing, create a map, put some directions on it, print it. Nope! The image you see before you click on Print is large and legible. The resulting print version shows a large canvas with the current view zoomed out to where the image is 1/5th the original desired size with no detail. Useless. What I want What I got submitted by /u/tkrafte1 [link] [comments]
I just got a new Galaxy S24+ and a certain Google feature is missing. I used to hold the home button, which opens Google over the current screen, and then be able to tap the microphone to search whatever audio is playing on the phone at the moment (like an Instagram reel)--that's gone on my new phone. So I'm looking for a permission I'm not toggling on, or an update I need to activate, or something. Any help is appreciated. submitted by /u/BuildsByBenjamin [link] [comments]
I had issues with the LG app connecting to Netflix after 20 mins of TV and internet disconnected i got it to work on 3rd try. But now I get a strange articfect on the top of the screen every so often, haven't noticed before or on anything else. It a digital line on top and then goes away submitted by /u/Carneyguydr [link] [comments]
anyone else got this? been going back and forth with support agents for a month and I’ve filled out the exact same form again and again every time, feels like I’ve been robbed lmfao submitted by /u/BlueEyesGalaxyDragon [link] [comments]
(AI) refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence…Continue reading on Medium »
I have completed my google Hiring Assessment yesterday and got the results and I have passed . Will the recruiter contacts me for the next steps . submitted by /u/Real-Competition-628 [link] [comments]
True story... I once had a boss that looked over at me as I was running around 4 or 5 global RFP's at the same time and would not give up…Continue reading on Medium »
Today, I want to talk about the concept of “pocket AI,” specifically in light of Apple’s recent unveiling of their new A-Intelligence…Continue reading on Medium »
As in the topic, I don't see the option to remove the device either on the website or in the dedicated application. submitted by /u/LonleyPaladin [link] [comments]
As someone who’s actively using AWS for data engineering, I’m always curious about what similar tools and services other cloud platforms…Continue reading on Medium »
In a previous article, I outlined a ClickOps process for manually creating a DNS Firewall domain list on AWS [link]. While this manual…Continue reading on Medium »
Imagine a scenario where you have data spread across multiple AWS accounts and regions. To ensure data durability, security, andContinue reading on Medium »
There have been a lot of InfoSec groups/forums where questions around AWS Security Hub are asked. I have observed that the people asking…Continue reading on Medium »
Big Brother is coming to your desktop. It will be taking screenshots of your computer and controlling your mouse and keyboard.Continue reading on Recurrent Patterns »
EU Regulators to assess whether Apple's iPad OS allows for alternative digital pens, headphones and app stores submitted by /u/Drtysouth205 [link] [comments]
Dropbox began in 2007 courtesy of Drew Houston and Arash Ferdowsi by beginning with the file sharing service that Dropbox essentially…Continue reading on Medium »
KuKu FM CusTomer Care Number//❽⓿❺-⓿❽❸❽-❸❶❽//91–8050838318// Call Me KuKu FM CusTomer Care Number//❽⓿❺-⓿❽❸❽-❸❶❽//91–8050838318// Call Me Continue reading on Medium »
Google has announced a range of updates for Google Maps, including a new feature to help in cases of DANAs (isolated depressions at high…Continue reading on Medium »
Visiting Stonehenge is a unique experience that connects you with history and mystery. The ancient stone circle, located in Wiltshire…Continue reading on Medium »
Give your Apple device a luxurious look and feel with top-of-the-range accessories designed by industry leaders such as Gucci and Louis…Continue reading on Medium »
Welcome to the Daily Advice Thread for /r/Apple. This thread can be used to ask for technical advice regarding Apple software and hardware, to ask questions regarding the buying or selling of Apple products or to post other short questions. Have a question you need answered? Ask away! Please remember to adhere to our rules, which can be found in the sidebar. Join our Discord and IRC chat rooms for support: Discord IRC Note: Comments are sorted by /new for your convenience. Here is an archive of all previous Daily Advice Threads. This is best viewed on a browser. If on mobile, type in the search bar [author:"AutoModerator" title:"Daily Advice Thread" or title:"Daily Tech Support Thread"] (without the brackets, and including the quotation marks around the titles and author.) The Daily Advice Thread is posted each day at 06:00 AM EST (Click HERE for other timezones) and then the old one is archived. It is advised to wait for the new thread to post your question if this time is nearing for quickest answer time. submitted by /u/AutoModerator [link] [comments]
What's even worse is the inability to select all and the message that covers up the"Load more" option. I don't have 1000 apps on my phone. These are apps that I downloaded and removed from my phone over the course of many years. So even if we uninstall them "ad personalization" continues. A new iPhone for Christmas is sounding better and better. submitted by /u/Used-Falcon-87 [link] [comments]
Please take a moment to complete this very short survey and share your preferences. Your feedback will help us design a product that meets your style and expectations. Your answers are anonymous. Thank you for your time. submitted by /u/Equivalent_Voice_597 [link] [comments]
So i just finished the 3rd season. It was a light watch. First season was my fav. I hate that some characters disappeared without any explanation. Funny overall! Anyway i feel this void right now once i finished it and i don’t know what to watch next. Any suggestions? submitted by /u/Thejoe923 [link] [comments]
Jose Menendez talks about dog collars in an episode, while he is out with Lyle. What's your take on it? Having a spike collar and treating the way he did with his sons. submitted by /u/Alternative-Major866 [link] [comments]
"Microsoft Rewards asks if you want to win a million dollars for a minimal amount of effort. If so? Look no further." submitted by /u/ControlCAD [link] [comments]
Trying to see if anyone else experiences this and how to fix it. Playback quality is terrible on iOS devices. I have 1Gb internet speed and settings are placed to highest quality. Some shows do not have a problem while others do. If I download the episode and play it that way, the quality is in HD. What gives? submitted by /u/smile-sunshine [link] [comments]
Any help would be greatly appreciated. I have three TVs in my house. Every time I watch Netflix on a different TV, I have to send myself a text and make that TV the head of household. It's extremely inconvenient and annoying. I feel that's not how it's intended either. submitted by /u/Choice-Pipe7509 [link] [comments]
I have adhd and my only streaming platform is Netflix so I need show recommendations (due to them not having the rest of the seasons of shows I like on Netflix) Some shows/movies I like are Dr Stone MLP Tuca & Bertie MHA Sonic X The babadook Star Vs the Forces of evil The Owl house Amphibia Danganronpa The nightmare before Christmas Southpark Squidbillies Ruby Gloom Invader Zim Corpse Party Tortured Souls In the Tall Grass That’s all I can think of rn but I’m pretty sure it gives you an idea of shows I like :b submitted by /u/Green_Nerve3473 [link] [comments]
Their putting the ads right inside the video is super annoying. I want to remove it as the default news aggregator on all my Windows PCs. submitted by /u/toskey [link] [comments]
Does anyone really think Tyson can win this? Feels like such a blatant money grab when, realistically, Paul will surely win. Feels closer to a WWE type of hype match than a real boxing match. Kind of surprised to see this on Netflix. By the end of this fight, it’s just going to be an old man getting abused submitted by /u/TylerDurdenEsq [link] [comments]
When is BUNK'D Season 7 going to stream on Netflix? The final season aired on Disney channel in August 2024, so it's been almost 3 months now. Does anyone know when Netflix might start streaming the final season please? Thanks! submitted by /u/MrCharmingMan [link] [comments]
Does anybody know what all the finance rotation options? I am an incoming intern and would love to learn more about all the possible route to go, just hard to find a comprehensive list anywhere. submitted by /u/GimmeALilSquirt [link] [comments]
I currently have a Netflix account that's an "extra member" from my sister's household account but she's canceling her subscription now. My question is can I still use the email address of my current "extra member" account for a different household and still keep the data/profile? I just want to save a couple of bucks since an extra member is significantly cheaper than subscribing to a whole premium plan that I don't really have a use for. submitted by /u/Ezekiel_093 [link] [comments]
Has anyone else been getting movies with a main genre that just isnt what the movie is? Ive been getting a lot of drama movies advertised as comedy first when they have barely any comedy at all Same with stuff like action as well, a movie being advertised as action but its not really that... For example, one of the movies i got was "ela camino christmas" advertised mainly as comedy. But even objectively looking it barely had anything like that. Id understand it being a subgenre but definitely not the main one submitted by /u/ullneverknowme [link] [comments]
Originally Answered: What can I improve on for my next FAANG Sr SWE interview?
I’m going to read between the lines and assume that you are working at a grade below senior at a company which is not a FAANG. I’m also assuming that you feel that you are ready and that you’ve already done the obvious, read the books, practiced questions etc.
Your senior eng interview has 3 facets, coding, system design and behavioral.
Your levers to do better at each are:
To get better at coding interviews, interview more candidates. Seeing what others do well and less well is very helpful. This really applies to all sorts of interviews but IMO is most helpful for coding interviews.
To get better at system design interviews, read more design docs at your existing company, attend more design reviews, and force yourself to participate. Comment, ask questions. It doesn’t matter if you’re off the mark. See what doesn’t make sense to you and challenge it.
To get better at behavioral interviews, read your perf packets and the feedback from your coworkers. Read the docs that you wrote on your career plans (If you don’t have any, ask yourself why and start one). Reflect, regularly, on what has been hardest in your career, what you have done very well, where you struggled, what you would do differently.
I’d like to answer first in general — about attrition rates in the tech sector — and then about Amazon specifically.
Industry-Wide Retention
Retention in the US high-tech industry is very challenging. I believe there are two main reasons for that.
First, there is an acute shortage of qualified workers, which means companies are desperate to get employees anywhere they can, including — sometimes mainly — by poaching them from other companies. This is why so many companies moved into the Seattle East Side in the ’90s or South Lake Union in the last five years, for example: to poach from Microsoft and Amazon, respectively.
I remember the crazy late-90’s in the Israel high-tech industry. People would come in, work for 6–12 months, then jump ship for a fancier title and a bump in pay. It was insane; it was disgusting (I mean that literally: I would sometimes feel physically sick thinking about how stupid it all was.)
The second reason — which I’m not as certain about — is that the high-tech industry is so incredibly dynamic. Things change constantly: new companies spring up and grow like crazy (Uber anyone?); “old” companies that were considered the cream of the crop a couple of years ago are suddenly untouchable (Yahoo!). New technologies explode onto the scene and old ones stagnate.
Not only does that create a lot of churn as companies keep growing and shrinking; it also creates incredible pressure on tech workers to stay on top of their game. We’re always looking for the next big technology, the next big field, then next big product… The sad part is that a lot of it is just hype, but the psychological pressure is real enough, and it makes people move around always looking for the next great opportunity.
Amazon
The reason I want to talk about Amazon — which generally suffers from the same problems I’ve described above — is that there’s a perception in the public that Amazon is somehow worse than the rest of the industry; that it has awful attrition, because it’s a terrible place to work. I’ve tackled that in a couple of other answers (e.g. this one and this one), but it’s a very persistent myth.
Much of the fault is in reports like this one from PayScale, which then get regurgitated in hundreds of stories like this one (from BuzzFeed). The basic story seems very simple: the average tenure of an Amazon employee is about a year, which is — undoubtedly — really low, even in tech-industry terms.
That’s a great example of (supposedly) Benjamin Disraeli’s famous quote, “lies, damned lies and statistics”. There are at least two reasons why this number is completely meaningless:
Short tenure does not mean high attrition: in the last 6–7 years the number of employees at Amazon has grown exponentially, and I mean this literally:
This means that at any time, pretty much, about 20–40% of all Amazon employees have joined less than a year ago. It’s no really surprising that they have a short tenure, is it?
Measuring retention is not trivial, but this methodology is just plain dumb (or maybe intentionally misleading).
Amazon is not (only) a tech company: sure, if you compare Amazon to Google and Facebook it comes out bad. But unlike those companies, the majority of Amazon employees are not tech workers. They’re warehouse workers, drivers, customer-service people, etc. Many of them are temp workers, and many others are not considering the job as a career.
There is a good discussion to be had about how Amazon treats these workers and whether it can do better, but it makes no sense to compare it with Microsoft or Apple; Walmart and Target would be much better comparisons.
Download the AI & Machine Learning For Dummies PRO App: iOS - Android Our AI and Machine Learning For Dummies PRO App can help you Ace the following AI and Machine Learning certifications:
Data Sciences – Top 400 Open Datasets – Data Visualization – Data Analytics – Big Data – Data Lakes
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
A dataset is a collection of data, usually presented in tabular form. Good datasets for Data Science and Machine Learning are typically those that are well-structured (easy to read and understand) and large enough to provide enough data points to train a model. The best datasets are often those that are open and freely available – such as the popular Iris dataset. However, there are also many commercial datasets available for purchase. In general, good datasets for Data Science and Machine Learning should be:
Well-structured
Large enough to provide enough data points
Open and freely available whenever possible
In this blog, we are going to provide popular open source and public data sets, data visualization, data analytics and data lakes.
Fertility rates all over the world are steadily declining
Yes, fertility rates have been declining globally in recent decades. There are several factors that contribute to this trend, including increased access to education and employment opportunities for women, improved access to family planning and birth control, and changes in societal attitudes towards having children. However, the rate of decline varies significantly by country and region, with some countries experiencing more dramatic declines than others.
11 developing countries with higher life expectancy than the United States
Healthcare expenditure per capita vs life expectancy years
1.2% of adults own 47.8% of world’s wealth
How to Mathematically Win at Rock Paper Scissors
Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021
Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.
At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.
Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021
Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.
At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.
The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.
The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.
When will computers replace humans?
This chart is essentially measuring “How good is a human at a computers’ area of strength”.. meanwhile computers simply can not compete in human areas of strength.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.
The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams. TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.
The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.
The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.
This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link
PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.
The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation
This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation
DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.
NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.
The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here
FlightAware.com has data but you need to pay for a full dataset.
The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:
flights: all flights that departed a given airport in a given year and month
weather: hourly meterological data for a given airport in a given year and month
airports: airport names, FAA codes, and locations
airlines: translation between two letter carrier (airline) codes and names
planes: construction information about each plane found in flights
The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here
Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.
flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.
Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021
Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.
At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.
The University of Chicago Project on Security and Threats presents the updated and expanded Database on Suicide Attacks (DSAT), which now links to Uppsala Conflict Data Program data on armed conflicts and includes a new dataset measuring the alliance and rivalry relationships among militant groups with connections to suicide attack groups. Access it here.
The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.
The GDC Data Portal is a robust data-driven platform that allows cancer researchers and bioinformaticians to search and download cancer data for analysis.
The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer.
The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program applies a comprehensive genomic approach to determine molecular changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into a collaborative network of disease-specific project teams. TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive. Access it here.
The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. Downloads
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Access it here.
The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.
This dataset contains interactions between drugs and targets collected from DrugBank, KEGG Drug, DCDB, and Matador. It was originally collected by Perlman et al. It contains 315 drugs, 250 targets, 1,306 drug-target interactions, 5 types of drug-drug similarities, and 3 types of target-target similarities. Drug-drug similarities include Chemical-based, Ligand-based, Expression-based, Side-effect-based, and Annotation-based similarities. Target-target similarities include Sequence-based, Protein-protein interaction network-based, and Gene Ontology-based similarities. The original task on the dataset is to predict new interactions between drugs and targets based on different types of similarities in the network. Download link
PharmGKB data and knowledge is available as downloads. It is often critical to check with their curators at feedback@pharmgkb.org before embarking on a large project using these data, to be sure that the files and data they make available are being interpreted correctly. PharmGKB generally does NOT need to be a co-author on such analyses; They just want to make sure that there is a correct understanding of our data before lots of resources are spent.
The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification. Documentation
This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. Documentation
DAiSEE is the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration “in the wild”. The dataset has four levels of labels namely – very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. Download it here.
NatureServe Explorer provides conservation status, taxonomy, distribution, and life history information for more than 95,000 plants and animals in the United States and Canada, and more than 10,000 vegetation communities and ecological systems in the Western Hemisphere.
The data available through NatureServe Explorer represents data managed in the NatureServe Central Databases. These databases are dynamic, being continually enhanced and refined through the input of hundreds of natural heritage program scientists and other collaborators. NatureServe Explorer is updated from these central databases to reflect information from new field surveys, the latest taxonomic treatments and other scientific publications, and new conservation status assessments. Explore Data here
FlightAware.com has data but you need to pay for a full dataset.
The anyflights package supplies a set of functions to generate air travel data (and data packages!) similar to nycflights13. With a user-defined year and airport, the anyflights function will grab data on:
flights: all flights that departed a given airport in a given year and month
weather: hourly meterological data for a given airport in a given year and month
airports: airport names, FAA codes, and locations
airlines: translation between two letter carrier (airline) codes and names
planes: construction information about each plane found in flights
The U.S. Department of Transportation’s (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT’s monthly Air Travel Consumer Report, published about 30 days after the month’s end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released. Access it here
Flightera.net seems to have a lot of good data for free. It has in-depth data on flights and doesn’t seem limited by date. I can’t speak on the validity of the data though.
flightradar24.com has lots of data, also historically, they might be willing to help you get it in a nice format.
Measurements of the normal (i.e. non-superconducting) state magnetoresistance (change in resistance with magnetic field) in several single crystalline samples of copper-oxide high-temperature superconductors. The measurements were performed predominantly at the High Field Magnet Laboratory (HFML) in Nijmegen, the Netherlands, and the Pulsed Magnetic Field Facility (LNCMI-T) in Toulouse, France. Complete Zip Download
Collection of multimodal raw data captured from a manned all-terrain vehicle in the course of two realistic outdoor search and rescue (SAR) exercises for actual emergency responders conducted in Málaga (Spain) in 2018 and 2019: the UMA-SAR dataset. Full Dataset.
Child mortality numbers caused by malaria by country
Number of deaths of infants, neonatal, and children up to 4 years old caused by malaria by country from 2000 to 2015. Originator: World Health Organization
The dataset will give anyone the opportunity to train and test models of semantic equivalence, based on actual Quora data. 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line truly contains a duplicate pair. Access it here.
MIMIC Critical Care Database
MIMIC is an openly available dataset developed by the MIT Lab for Computational Physiology, comprising deidentified health data associated with ~60,000 intensive care unit admissions. It includes demographics, vital signs, laboratory tests, medications, and more. Access it here.
Data.Gov: The home of the U.S. Government’s open data
Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more. Search over 280000 Datasets.
Art that does not attempt to represent an accurate depiction of a visual reality but instead use shapes, colours, forms and gestural marks to achieve its effect
5000+ classical abstract art here, real artists with annotation. You can download them in very high resolution, however you would have to crawl them first with this scraper.
Interactive map of indigenous people around the world
Native-Land.ca is a website run by the nonprofit organization Native Land Digital. Access it here.
I took the data from IHME’s Global Burden of Disease 2019 study (2019 all-ages prevalence of drug use disorders among both men and women for all countries and territories) and plotted it using R.
Also, what is going on in the US exactly? 3.3% of the population there is addicted and it’s the worst rate in the world.
File POP/1-1: Total population (both sexes combined) by region, subregion and country, annually for 1950-2100 (thousands)Medium fertility variant, 2020 – 2100
Conducted by the Federal Highway Administration (FHWA), the NHTS is the authoritative source on the travel behavior of the American public. It is the only source of national data that allows one to analyze trends in personal and household travel. It includes daily non-commercial travel by all modes, including characteristics of the people traveling, their household, and their vehicles. Access it here.
Statistics and data about the National Travel Survey, based on a household survey to monitor trends in personal travel.
The survey collects information on how, why, when and where people travel as well as factors affecting travel (e.g. car availability and driving license holding).
NeTEx is the official format for public transport data in Norway and is the most complete in terms of available data. GTFS is a downstream format with only a limited subset of the total data, but we generate datasets for it anyway since GTFS can be easier to use and has a wider distribution among international public transport solutions. GTFS sets come in “extended” and “basic” versions. Access here.
A subset of the field data collected on temporary NFI plots can be downloaded in Excel format from this web site. The file includes a Read_me sheet and a sheet with field data from temporary plots on forest land1 collected from 2007 to 2019. Note that plots located on boundaries (for example boundaries between forest stands, or different land use classes) are not included in the dataset. The dataset is primarily intended to be used as reference data and validation data in remote sensing applications. It cannot be used to derive estimates of totals or mean values for a geographic area of any size. Download the dataset here
Large data sets from finance and economics applicable in related fields studying the human condition
CIA: The world Factbook provides basic intelligence on the history, people, government, economy, energy, geography, environment, communications, transportation, military, terrorism, and transnational issues for 266 world entities.
Consumer Price Index: The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available.
International Historical Statistics is a compendium of national and international socio-economic data from 1750 to 2010. Data are available in both Excel and PDF tabular formats. IHS is structured in three broad geographical divisions and ten themes: Africa / Asia / Oceania; The Americas and Europe. The database is structured in ten categories: Population and vital statistics; Labour force; Agriculture; Industry; External trade; Transport and communications; Finance; Commodity prices; Education and National accounts. Access here
World Input-Output Tables and underlying data. World Input-Output Tables and underlying data, covering 43 countries, and a model for the rest of the world for the period 2000-2014. Data for 56 sectors are classified according to the International Standard Industrial Classification revision 4 (ISIC Rev. 4).
Data: Real and PPP-adjusted GDP in US millions of dollars, national accounts (household consumption, investment, government consumption, exports and imports), exchange rates and population figures.
COW seeks to facilitate the collection, dissemination, and use of accurate and reliable quantitative data in international relations. Key principles of the project include a commitment to standard scientific principles of replication, data reliability, documentation, review, and the transparency of data collection procedures
Data: Total national trade and bilateral trade flows between states. Total imports and exports of each country in current US millions of dollars and bilateral flows in current US millions of dollars
Geographical coverage: Single countries around the world
The WTO provides quantitative information in relation to economic and trade policy issues. Its data-bases and publications provide access to data on trade flows, tariffs, non-tariff measures (NTMs) and trade in value added.
The Subaru-Mitaka-Okayama-Kiso Archive, holds about 15 TB of astronomical data from facilities run by the National Astronomical Observatory of Japan. All data becomes publicly available after an embargo period of 12-24 months (to give the original observers time to publish their papers).
Graph Datasets
Web crawl graph with 3.5 billion web pages and 128 billion hyperlinks
Many web and social graphs with up to 95 billion edges. While this data collection seems to be very comprehensive, it is not trivially accessible without external tool.
The Multi-Domain Sentiment Dataset contains product reviews taken from Amazon.com from many product types (domains). Some domains (books and dvds) have hundreds of thousands of reviews. Others (musical instruments) have only a few hundred. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed. Access it here.
Supported by Google Jigsaw, the GDELT Project monitors the world’s broadcast, print, and web news from nearly every corner of every country in over 100 languages and identifies the people, locations, organizations, themes, sources, emotions, counts, quotes, images and events driving our global society every second of every day, creating a free open platform for computing on the entire world.
This dataset represents a snapshot of the Yahoo! Music community’s preferences for various musical artists. The dataset contains over ten million ratings of musical artists given by Yahoo! Music users over the course of a one month period sometime prior to March 2004. Users are represented as meaningless anonymous numbers so that no identifying information is revealed. The dataset may be used by researchers to validate recommender systems or collaborative filtering algorithms. The dataset may serve as a testbed for matrix and graph algorithms including PCA and clustering algorithms. The size of this dataset is 423 MB.
This dataset contains a small sample of the Yahoo! Movies community’s preferences for various movies, rated on a scale from A+ to F. Users are represented as meaningless anonymous numbers so that no identifying information is revealed. The dataset also contains a large amount of descriptive information about many movies released prior to November 2003, including cast, crew, synopsis, genre, average ratings, awards, etc. The dataset may be used by researchers to validate recommender systems or collaborative filtering algorithms, including hybrid content and collaborative filtering algorithms. The dataset may serve as a testbed for relational learning and data mining algorithms as well as matrix and graph algorithms including PCA and clustering algorithms. The size of this dataset is 23 MB.
The dataset is a collection of 964 hours (22K videos) of news broadcast videos that appeared on Yahoo news website’s properties, e.g., World News, US News, Sports, Finance, and a mobile application during August 2017. The videos were either part of an article or displayed standalone in a news property. Many of the videos served in this platform lack important metadata, such as an exhaustive list of topics associated with the video. We label each of the videos in the dataset using a collection of 336 tags based on a news taxonomy designed by in-house editors. In the taxonomy, the closer the tag is to the root, the more generic (topically) it is.
The Internet Archive is making an 80 TB web crawl available for research
The TREC conference made the ClueWeb09 [3] dataset available a few years back. You’ll have to sign an agreement and pay a nontrivial fee (up to $610) to cover the sneakernet data transfer. The data is about 5 TB compressed.
ClueWeb12 is now available, as are the Freebase annotations, FACC1
CNetS at Indiana University makes a 2.5 TB click dataset available
ICWSM made a large corpus of blog posts available for their 2011 conference. You’ll have to register (an actual form, not an online form), but it’s free. It’s about 2.1 TB compressed. The dataset consists of over 386 million blog posts, news articles, classifieds, forum posts and social media content between January 13th and February 14th. It spans events such as the Tunisian revolution and the Egyptian protests (see http://en.wikipedia.org/wiki/January_2011 for a more detailed list of events spanning the dataset’s time period). Access it here
The Yahoo News Feed dataset is 1.5 TB compressed, 13.5 TB uncompressed
The Proteome Commons makes several large datasets available. The largest, the Personal Genome Project , is 1.1 TB in size. There are several others over 100 GB in size.
The MOBIO dataset is about 135 GB of video and audio data
The Yahoo! Webscope program makes several 1 GB+ datasets available to academic researchers, including an 83 GB data set of Flickr image features and the dataset used for the 2020 KDD Cup , from Yahoo! Music, which is a bit over 1 GB.
Freebase makes regular data dumps available. The largest is their Quad dump , which is about 3.6 GB compressed.
The Research and Innovative Technology Administration (RITA) has made available a dataset about the on-time performance of domestic flights operated by large carriers. The ASA compressed this dataset and makes it available for download.
The wiki-links data made available by Google is about 1.75 GB total.
Google Research released a large 24GB n-gram data set back in 2006 based on processing 10^12 words of text and published counts of all sequences up to 5 words in length.
These data are intended to be used by researchers and other professionals working in power and energy related areas and requiring data for design, development, test, and validation purposes. These data should not be used for commercial purposes.
A dataset and open-ended challenge for music recommendation research ( RecSys Challenge 2018). Sampled from the over 4 billion public playlists on Spotify, this dataset of 1 million playlists consist of over 2 million unique tracks by nearly 300,000 artists, and represents the largest public dataset of music playlists in the world. Access it here
How much each of 20 most popular artists earns from Spotify.
Needless to say, the United States absolutely dominates this list more than any other country. 9 of the top 10 are Americans, you’d have to combine the next 5 countries after the US to match their output of 33 among the top 80, and you’d have to combined every other country not named China on this graph to equal the USA.
To break things down based on region:
– The Americas has 34 individuals on this list with USA (33) and Mexico (1)
– Asia-Pacific has 28 individuals on this list with China (14), India (5), Hong Kong (4), Japan (3), and Australia (2)
– Europe has 18 individuals on this list with France (5), Russia (5), Germany (3), Italy (2), UK (1), Ireland (1), and Spain (1)
The National Health and Nutrition Examination Survey (NHANES) is conducted every two years by the National Center for Health Statistics and funded by the Centers for Disease Control and Prevention. The survey measures obesity rates among people ages 2 and older. Find the latest national data and trends over time, including by age group, sex, and race. Data are available through 2017-2018, with the exception of obesity rates for children by race, which are available through 2015-2016. Access here
NCEI first developed the Global Historical Climatology Network-Monthly (GHCN-M) temperature dataset in the early 1990s. Subsequent iterations include version 2 in 1997, version 3 in May 2011, and version 4 in October 2018.
The Human Development Index (HDI) is a statistic composite index of life expectancy, education (mean years of schooling completed and expected years of schooling upon entering the education system), and per capita income indicators, which are used to rank countries into four tiers of human development.
Numbers like these are a quick reminder that not every athlete is LeBron James or Roger Federer who can play their sport at such high levels for their entire young adulthood while becoming billionaires in the process. Many careers are short lived and end abruptly while the athlete is still very young and some don’t really have a plan B.
NFL being at the bottom here doesn’t surprise me though as most positions (with the exception of QB and kicker) in US Football is lowkey bodily suicide.
The data comes from the Global Power Plant Database. The Global Power Plant Database is a comprehensive, open source database of power plants around the world. It centralizes power plant data to make it easier to navigate, compare and draw insights for one’s own analysis. The database covers approximately 30,000 power plants from 164 countries and includes thermal plants (e.g. coal, gas, oil, nuclear, biomass, waste, geothermal) and renewables (e.g. hydro, wind, solar). Each power plant is geolocated and entries contain information on plant capacity, generation, ownership, and fuel type. It will be continuously updated as data becomes available.
The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images.
The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.
It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. Access it here.
MMID is a large-scale, massively multilingual dataset of images paired with the words they represent collected at the University of Pennsylvania. The dataset is doubly parallel: for each language, words are stored parallel to images that represent the word, and parallel to the word’s translation into English (and corresponding images.) . Dcumentation.
HDI is calculated by the UN every year to measure a country’s development using average life expectancy, education level, and gross national income per capita (PPP). The EU has a collective HDI of 0.911.
Data collected from a series of rushing and passing statistics for NFL Quarterbacks from 2015-2020 and performed a machine learning algorithm called clustering, which automatically sorts observations into groups based on shared common characteristics using a mathematical “distance metric.”
The idea was to use machine learning to determine NFL Quarterback Archetype to agnostically determine which quarterbacks were truly “mobile” quarterbacks, and which were “pocket passers” that relied more on passing. I used a number of metrics in my actual clustering analysis, but they can be effectively summarized across two dimensions: passing and rushing, which can be further roughly summarized across two metrics: passer rating and rushing yards per year. Plotting the quarterbacks along these dimensions and plotting the groups chosen by the clustering methodology shows how cleanly the methodology selected the groups.
Read this blog article on the process for more information if you’re interested, or just check out this blog in general if you found this interesting!
Intraday Stock Data (1 min) – S&P 500 – 2008-21: 12 years of 1 minute bars for data science / machine learning.
Granular stock bar data for research is difficult to find and expensive to buy. The author has compiled this library from a variety of sources and is making it available for free.
One compressed CSV file with 9 columns and 2.07 million rows worth of 1 minute SPY bars. Access it here
Datasets: A live version of the vaccination dataset and documentation are available in a public GitHub repository here. These data can be downloaded in CSV and JSON formats. PDF.