I work in a small data team, and I'm the only one working on my project. So I've to look after everything from data collection, processing, dashboarding, quite a bit of software engineering, testing, CI/CD and deployment. So even though my primary work is data science, there's a requirement for end-to-end work. That got me thinking (not in a disrespectful way), what do data scientists who work at larger organizations spend time on? By larger organizations, I mean places where there are specialised teams for each of the steps I mentioned above. submitted by /u/Mission-Language8789 [link] [comments]

I recently acquired my bachelors in Data Science. We covered the usual stuff (Python ,stat, ML, DL, SQL, R, Visualization etc) but I'm finding it difficult to get hired. My CVs don't seem to be getting anywhere. Any suggestions on how I can improve my employability? submitted by /u/PlayfulCobbler1497 [link] [comments]

I was told that I'm moving onto final round interviews, and it will consist of a coding portion using either SQL/R/Python, A/B testing and Statistics, and behavioral questions. I only have 1 week to prepare and am scared of messing this chance up. I don't know how to really prepare for A/B testing in the sense of an interview. submitted by /u/asmalltowngirlie [link] [comments]

I was looking on my schools catalog and saw a data analytics certification offered. After some research I found that the mathematics depart offers it. I feel like the classes in the certification sound better than most classes require of my data science program. Could anyone offer insight if the data science program covers the classes? Trying to figure out if I should take the data analytics certification or not. submitted by /u/dameis [link] [comments]

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Hi everyone, I would like to share my experience. I am currently working as a Data Scientist for a company (part of the R&D team). I have been with this company for about two years, and initially, everything seemed to be going well. At a certain point, the organization (both in terms of projects and the team) started to fall apart: pointless meetings, lack of clarity, and much more. After several months, I realized that the main problem is the manager of the R&D department, who, frankly speaking, does not understand anything about the technical side (starting from the programming language used and more). This has caused me a sense of discomfort that makes me consider leaving. The last straw was the assignment of a training course: initially, we were asked to choose a topic for a course (not necessarily related to the role, and in my case, I chose concepts of Data Engineering and frameworks like Airflow). Without saying anything, this manager assigned a course to all of us, Data Scientists, and what did she assign us? A course on Data Science! What's the point! In fact, I'm completely skipping every single video as I already know all the concepts. Having said that, can you give me some advice on how to handle this situation? Even better, can you suggest companies that have fully remote positions for Data Scientists? Thanks everyone submitted by /u/SupertrampDFenx [link] [comments]

I'm quoting directly from a Reddit user named funbike. This is the rule you should abide by in organizations. I also made the same mistake when I joined a company, attempting to prove myself. " After being a fool in my early career trying too hard to impress, this is how I handle this kind of thing these days: Document EVERYTHING. Follow-up verbal conversations with summary email. When things go south, I'll be able to prove I warned them. Give realistic estimates on how long things will take. Whatever I say is usually twice how long I actually think it will take, because things never go like you think. Make it clear that that longer-term estimates will be less accurate the farther out they are, because software is notoriously difficult to estimate. Tell them to their face that we will not make the unrealistic dates they've set, and to prevent in future to always consult first. I will not work overtime due to artificial deadlines. I'll do O/T for extreme exceptional cases only, such as a one-time short-term crisis or for a regulatory-mandated deadline. By 6pm I'll be at my house. Explain quality should never be abandoned for speed. It will violently backfire in the end, with the opposite effect. I stand my ground. I can make them mildly unhappy now, or furiously disappointed in our results in the future. I'll take the first one please. Even if you were to heroically meet their unreasonable date, they'll just expect more next time. You'll burn out and maybe the next time you'll have an embarrassing failure even with crazy overtime. They'll say "tsk, tsk" and blame you. Don't fall into this trap" submitted by /u/Excellent_Cost170 [link] [comments]

To anyone who taught themselves data science and then achieved employment in a data science role, how long did it take you to learn in hours per day? And additionally, how long did it take you after you stopped learning to find a job and keep a job? If you did not self learn or hold a job afterwards please do not reply with any speculations. submitted by /u/Remarkable-Floor-351 [link] [comments]

I have noticed some industries or domains are lacking in integrated data architecture (data warehouse, let alone data lake). One example that comes to mind is marketing where the different levels in data sources makes it difficult for integrated data architecture, hence, also difficult for cross-source analysis for the KPIs. And also on the vice versa which domains are leading in this? submitted by /u/Brief-Living-5083 [link] [comments]

I try to understand what are the benefits to convert DICOM images to PNG's. Context: I have DICOM images which I already extracted the useful meta-data I want to use. Those images are for a task, classification-detection pipeline of some disease. So as I already asked, what are the benefits of converting those DICOM files to PNG's rather then just using pydicom and the dicom pixel_array? Reason I ask this is because I saw many top 5 users on kaggle do this when dealing with DICOM images. If I understand how networks actually works, they get as input an array of pixels as floating point numbers no? So what's the differences between DICOM pixel_array to PNG's pixel array and numpy array or tensor? both are eventually will be fed to the network as a tensor of floating numbers. Is the reason is because PNG's are usually faster to train? Is the reason is because PNG's have more libraries support for preprocessing / augmentation / etc. ? Is the reason is because PNG's are the format many pre-trained models expect to? (I write this knowing it's 99% not true, as mentioned the tensor thing) Thanks in Advance, and Please, forgive my English (I could use AI tools to fix it but I feel addicted already) submitted by /u/01jasper [link] [comments]

Hello everyone! As the title explains, I graduated with a MS in Data Science just recently in July, and in mid august I started my job as a system analyst (essentially a business analyst but for technology). It has been only 5 weeks in this job, but I have yet to implement the skills I gained in my Master's except for some project/process management (my MS was specializing in business), along with some web scraping and data manipulation in Python for an internal project. Because of this, I have grown pretty desperate to apply and practice all those skills I heavily enjoy, and this feeling has been exasperated by a pretty decent amount of downtime since I'm new. This is all to say that I am going to use the leverage of working in a small and very lax company to meet with a higher-up and ask for any work available either internally or in any present projects that intersect the area of data analytics and/or machine learning. But other than that, I wanted to know what other structured activities you all recommend to continue building on my career and skills that I can commit to during my down time. For example, I have been looking into an AWS certification path towards their ML certification, but I wanted to know what other suggestions you have? Thanks! submitted by /u/drunkmute [link] [comments]

Are their previous work in Cpp language in data science, please consider sharing , also any context related to use of cpp in data science. submitted by /u/DickSmithismydad [link] [comments]

What are some of the biggest mistakes you guys have made and how do you handle them? Especially when there is a time crunch. I’m a quality data analyst for a steel company and have been in this position for almost 2 years. I finished my masters in data analytics this past May, so this job has been my only real experience in the world of data. I want to transition to data science in this next year. In my free time, I take Codecademy courses to learn Python and SQL and I will eventually dive into Java as well. I take what I learn and I try to apply it to my job. We’re a legacy steel mill, so there is no fancy automation, the business and production systems don’t communicate very well, data can only be gathered through exporting reports from these systems in csv files. So I’ve been able to sort of make my own database using the tools I’ve been approved to download (basically just anaconda and power bi). As the only data analyst in my mill, with no previous steel making background, my company relies heavily on my data analytics to make business decisions both small and large and sometimes is overwhelming pressure to be precise. Luckily I haven’t had any major mistakes. The downside is I’m the only person doing the job I do and there isn’t a whole lot of computer literacy in the management, so unless my conclusions appear extremely illogical to them, they just roll with it. I’ve definitely made mistakes along the way but have caught them myself, sometimes working through the night so I can hurry and send emails out to disregard my previous work and look at the revised stuff. This just made me wonder how others handle mistakes both when they catch it and when they don’t catch it. I understand larger companies probably have a team of people doing the same projects or can lend a hand to be a 2nd pair of eyes. Maybe I’m just overdue to make my first big mistake lol. I feel like I make a lot of decisions day-to-day that I have to cross my fingers on. submitted by /u/SuitableElk7382 [link] [comments]

Hello everyone!! I have come up with my second project and I am very excited to share here. I have done this work with a day of learning web scraping. please review my project and give feedbacks, suggestions and do not hesitate to leave brutal comments. Also, i request to help me with my next steps on web scraping. I would like to thank this community for letting to share my projects!! project title: Nepali Beverage Seller data web scraping https://www.kaggle.com/code/aadeshpradhan/nepali-alcohol-seller-data-web-scraping-cheers/notebook submitted by /u/Alarming_Scene126 [link] [comments]

I recently transferred from a state school to a tech school. Previously I was CS but now I am math. I still have until the end of this semester to decide what I want to do but I am debating math vs CS. My career goals involve working with big data such as an analyst or data engineer, but working as a SWE is also a dream. At my school, I can major in math, minor in computational data analysis and have a concentration in data science or I can major in CS and do threads in mod and sims, and info networks. Which is better and why? Also I know that I want to get a masters so what would be better. submitted by /u/QueasyCold5787 [link] [comments]

I have around an year's experience as a tech consultant in one of the Big 4 firms after my undegrad in CS. Having seen many threads on this sub regarding DS programs as cash grabs, I am genuinely confused whether to pursue a Masters in Data Science or a Masters in Statistics with a data science track. I am preferably looking forward to get into the Quant industry after my masters or am even open for data roles depending on the job situation then. submitted by /u/Akshath_memeLoRd [link] [comments]

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I’m about to start my Masters wondering if I should be interning concurrently or be doing side projects or both? Coming from a non-cs background (chemistry) but i did do a bootcamp full-stack and familiar with python,sql, and data science. submitted by /u/snmstyle [link] [comments]

This paper seems to say that an LLM trained on text can somehow be good at compressing not just text but images and audio. Intuitively this seems improbable to me. How does this work? https://arxiv.org/pdf/2309.10668.pdf submitted by /u/axlrosen [link] [comments]

First, thanks to everyone that answered my previous post ! So, following this previous post (https://www.reddit.com/r/datascience/comments/16tgojm/what_kind_of_algorithm_do_you_use_the_most_as_a/), I'm giving here a quick review of the most common answers. Here it is: Gradient boosted machines (22): XGBoost (12), Light GBM (8), Catboost(2) Linear methods (19): Linear Regression (9), Logistic Regression (5), GAM (3), OLS (2) Random Forest (or tree based algo) (9) DBScan (4) DNN / CNN / GNN (4) Clustering algo / K-means (3) ARIMA (2) ​ The number gives the number of time an answer appeared. I put here only answer that appeared at least 2 times.Also, I tried to gather some answers, but I don't know all the algorithms or tools your are using. So please forgive me if I did some mistakes or approximations in the way I gathered answers. submitted by /u/Choweeez [link] [comments]