Tag: Amazon QuickSight and SPICE

In this blog, we talk about big data and data analytics; we also give you the last updated top 50 AWS Certified Data Analytics – Specialty Questions and Answers Dumps

The AWS Certified Data Analytics – Specialty (DAS-C01) examination is intended for individuals who perform in a data analytics-focused role. This exam validates an examinee’s comprehensive understanding of using AWS services to design, build, secure, and maintain analytics solutions that provide insight from data.

The AWS Certified Data Analytics – Specialty (DAS-C01) covers the following domains:

Domain 1: Collection 18%

Domain 2: Storage and Data Management 22%

Domain 3: Processing 24%

Domain 4: Analysis and Visualization 18%

Domain 5: Security 18%

Below are the Top 20 AWS Certified Data Analytics – Specialty Questions and Answers Dumps and References –

Top 100 Data Science and Data Analytics Interview Questions and Answers

Question1: What combination of services do you need for the following requirements: accelerate petabyte-scale data transfers, load streaming data, and the ability to create scalable, private connections. Select the correct answer order.

A) Snowball, Kinesis Firehose, Direct Connect

B) Data Migration Services, Kinesis Firehose, Direct Connect

C) Snowball, Data Migration Services, Direct Connect

D) Snowball, Direct Connection, Kinesis Firehose

ANSWER1: Show Answer

Notes/Hint1: Show Answer

Reference1: Big Data Analytics Options 

Question2:  A company ingests a large set of clickstream data in nested JSON format from different sources and stores it in Amazon S3. Data analysts need to analyze this data in combination with data stored in an Amazon Redshift cluster. Data analysts want to build a cost-effective and automated solution for this need.
Which solution meets these requirements?

A) Use Apache Spark SQL on Amazon EMR to convert the clickstream data to a tabular format. Use the Amazon Redshift COPY command to load the data into the Amazon Redshift cluster.

B) Use AWS Lambda to convert the data to a tabular format and write it to Amazon S3. Use the Amazon Redshift COPY command to load the data into the Amazon Redshift cluster.

C) Use the Relationalize class in an AWS Glue ETL job to transform the data and write the data back to Amazon S3. Use Amazon Redshift Spectrum to create external tables and join with the internal tables.

D) Use the Amazon Redshift COPY command to move the clickstream data directly into new tables in the Amazon Redshift cluster.

ANSWER2: Show Answer

Notes/Hint2: Show Notes

Reference1: Relationalize PySpark

Question 3: There is a five-day car rally race across Europe. The race coordinators are using a Kinesis stream and IoT sensors to monitor the movement of the cars. Each car has a sensor and data is getting back to the stream with the default stream settings. On the last day of the rally, data is sent to S3. When you go to interpret the data in S3, there is only data for the last day and nothing for the first 4 days. Which of the following is the most probable cause of this?

A) You did not have versioning enabled and would need to create individual buckets to prevent the data from being overwritten.

B) Data records are only accessible for a default of 24 hours from the time they are added to a stream.

C) One of the sensors failed, so there was no data to record.

D) You needed to use EMR to send the data to S3; Kinesis Streams are only compatible with DynamoDB.

ANSWER3: Show Answer

Notes/Hint3: Show Notes

Reference3: Kinesis Extended Reading

 

Question 4:  A publisher website captures user activity and sends clickstream data to Amazon Kinesis Data Streams. The publisher wants to design a cost-effective solution to process the data to create a timeline of user activity within a session. The solution must be able to scale depending on the number of active sessions.
Which solution meets these requirements?

A) Include a variable in the clickstream data from the publisher website to maintain a counter for the number of active user sessions. Use a timestamp for the partition key for the stream. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the counter. Deploy the consumer application on Amazon EC2 instances in an EC2 Auto Scaling group.

B) Include a variable in the clickstream to maintain a counter for each user action during their session. Use the action type as the partition key for the stream. Use the Kinesis Client Library (KCL) in the consumer application to retrieve the data from the stream and perform the processing. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the
counter. Deploy the consumer application on AWS Lambda.

C) Include a session identifier in the clickstream data from the publisher website and use as the partition key for the stream. Use the Kinesis Client Library (KCL) in the consumer application to retrieve the data from the stream and perform the processing. Deploy the consumer application on Amazon EC2 instances in an
EC2 Auto Scaling group. Use an AWS Lambda function to reshard the stream based upon Amazon CloudWatch alarms.

D) Include a variable in the clickstream data from the publisher website to maintain a counter for the number of active user sessions. Use a timestamp for the partition key for the stream. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the counter. Deploy the consumer application on AWS Lambda.

ANSWER4: Show Answer

Notes/Hint4: Show Notes

Reference4: UpdateShardCount API

Question 5: Your company has two batch processing applications that consume financial data about the day’s stock transactions. Each transaction needs to be stored durably and guarantee that a record of each application is delivered so the audit and billing batch processing applications can process the data. However, the two applications run separately and several hours apart and need access to the same transaction information. After reviewing the transaction information for the day, the information no longer needs to be stored. What is the best way to architect this application?

A) Use SQS for storing the transaction messages; when the billing batch process performs first and consumes the message, write the code in a way that does not remove the message after consumed, so it is available for the audit application several hours later. The audit application can consume the SQS message and remove it from the queue when completed.

B)  Use Kinesis to store the transaction information. The billing application will consume data from the stream and the audit application can consume the same data several hours later.

C) Store the transaction information in a DynamoDB table. The billing application can read the rows while the audit application will read the rows then remove the data.

D) Use SQS for storing the transaction messages. When the billing batch process consumes each message, have the application create an identical message and place it in a different SQS for the audit application to use several hours later.

SQS would make this more difficult because the data does not need to persist after a full day.

ANSWER5: Show Answer

Notes/Hint5: Show Notes

Reference5: Amazon Kinesis

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Question 6: A company is currently using Amazon DynamoDB as the database for a user support application. The company is developing a new version of the application that will store a PDF file for each support case ranging in size from 1–10 MB. The file should be retrievable whenever the case is accessed in the application.
How can the company store the file in the MOST cost-effective manner?

A) Store the file in Amazon DocumentDB and the document ID as an attribute in the DynamoDB table.

B) Store the file in Amazon S3 and the object key as an attribute in the DynamoDB table.

C) Split the file into smaller parts and store the parts as multiple items in a separate DynamoDB table.

D) Store the file as an attribute in the DynamoDB table using Base64 encoding.

ANSWER6: Show Answer

Notes/Hint6: Show Notes

Reference6: S3 Storage Cost –  DynamODB Storage Cost

Question 7: Your client has a web app that emits multiple events to Amazon Kinesis Streams for reporting purposes. Critical events need to be immediately captured before processing can continue, but informational events do not need to delay processing. What solution should your client use to record these types of events without unnecessarily slowing the application?

A) Log all events using the Kinesis Producer Library.

B) Log critical events using the Kinesis Producer Library, and log informational events using the PutRecords API method.

C) Log critical events using the PutRecords API method, and log informational events using the Kinesis Producer Library.

D) Log all events using the PutRecords API method.

ANSWER2: Show Answer

Notes/Hint7: Show Notes

Reference7: PutRecords API

Question 8: You work for a start-up that tracks commercial delivery trucks via GPS. You receive coordinates that are transmitted from each delivery truck once every 6 seconds. You need to process these coordinates in near real-time from multiple sources and load them into Elasticsearch without significant technical overhead to maintain. Which tool should you use to digest the data?

A) Amazon SQS

B) Amazon EMR

C) AWS Data Pipeline

D) Amazon Kinesis Firehose

ANSWER8: Show Answer

Notes/Hint8: Show Notes

Reference8: Amazon Kinesis Firehose

Question 9: A company needs to implement a near-real-time fraud prevention feature for its ecommerce site. User and order details need to be delivered to an Amazon SageMaker endpoint to flag suspected fraud. The amount of input data needed for the inference could be as much as 1.5 MB.
Which solution meets the requirements with the LOWEST overall latency?

A) Create an Amazon Managed Streaming for Kafka cluster and ingest the data for each order into a topic. Use a Kafka consumer running on Amazon EC2 instances to read these messages and invoke the Amazon SageMaker endpoint.

B) Create an Amazon Kinesis Data Streams stream and ingest the data for each order into the stream. Create an AWS Lambda function to read these messages and invoke the Amazon SageMaker endpoint.

C) Create an Amazon Kinesis Data Firehose delivery stream and ingest the data for each order into the stream. Configure Kinesis Data Firehose to deliver the data to an Amazon S3 bucket. Trigger an AWS Lambda function with an S3 event notification to read the data and invoke the Amazon SageMaker endpoint.

D) Create an Amazon SNS topic and publish the data for each order to the topic. Subscribe the Amazon SageMaker endpoint to the SNS topic.

ANSWER9: Show Answer

Notes/Hint9: Show Notes

Reference9: Amazon Managed Streaming for Kafka cluster

Question 10: You need to filter and transform incoming messages coming from a smart sensor you have connected with AWS. Once messages are received, you need to store them as time series data in DynamoDB. Which AWS service can you use?

A) IoT Device Shadow Service

B) Redshift

C) Kinesis

D) IoT Rules Engine

ANSWER10: Show Answer

Notes/Hint10: Show Notes

Reference10: The IoT rules engine

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Question 11: A media company is migrating its on-premises legacy Hadoop cluster with its associated data processing scripts and workflow to an Amazon EMR environment running the latest Hadoop release. The developers want to reuse the Java code that was written for data processing jobs for the on-premises cluster.
Which approach meets these requirements?

A) Deploy the existing Oracle Java Archive as a custom bootstrap action and run the job on the EMR cluster.

B) Compile the Java program for the desired Hadoop version and run it using a CUSTOM_JAR step on the EMR cluster.

C) Submit the Java program as an Apache Hive or Apache Spark step for the EMR cluster.

D) Use SSH to connect the master node of the EMR cluster and submit the Java program using the AWS CLI.

ANSWER11: Show Answer

Notes/Hint11: Show Notes

Reference11:  Automating analytics workflows on EMR

Question 12: You currently have databases running on-site and in another data center off-site. What service allows you to consolidate to one database in Amazon?

A) AWS Kinesis

B) AWS Database Migration Service

C) AWS Data Pipeline

D) AWS RDS Aurora

ANSWER12: Show Answer

Notes/Hint12: Show Notes

Reference12: DMS

 

Question 13:  An online retail company wants to perform analytics on data in large Amazon S3 objects using Amazon EMR. An Apache Spark job repeatedly queries the same data to populate an analytics dashboard. The analytics team wants to minimize the time to load the data and create the dashboard.
Which approaches could improve the performance? (Select TWO.)
A) Copy the source data into Amazon Redshift and rewrite the Apache Spark code to create analytical reports by querying Amazon Redshift.

B) Copy the source data from Amazon S3 into Hadoop Distributed File System (HDFS) using s3distcp.

C) Load the data into Spark DataFrames.

D) Stream the data into Amazon Kinesis and use the Kinesis Connector Library (KCL) in multiple Spark jobs to perform analytical jobs.

E) Use Amazon S3 Select to retrieve the data necessary for the dashboards from the S3 objects.

ANSWER13: Show Answer

Notes/Hint13: Show Notes

Reference13: Spark DataFrames 

 

Question 14: You have been hired as a consultant to provide a solution to integrate a client’s on-premises data center to AWS. The customer requires a 300 Mbps dedicated, private connection to their VPC. Which AWS tool do you need?

A) VPC peering

B) Data Pipeline

C) Direct Connect

D) EMR

ANSWER14: Show Answer

Notes/Hint14: Show Notes

Reference14: Direct Connect

Question 15: Your organization has a variety of different services deployed on EC2 and needs to efficiently send application logs over to a central system for processing and analysis. They’ve determined it is best to use a managed AWS service to transfer their data from the EC2 instances into Amazon S3 and they’ve decided to use a solution that will do what?

A) Installs the AWS Direct Connect client on all EC2 instances and uses it to stream the data directly to S3.

B) Leverages the Kinesis Agent to send data to Kinesis Data Streams and output that data in S3.

C) Ingests the data directly from S3 by configuring regular Amazon Snowball transactions.

D) Leverages the Kinesis Agent to send data to Kinesis Firehose and output that data in S3.

ANSWER15: Show Answer

Notes/Hint15: Show Notes

Reference15: Kinesis Firehose

 

Question 16: A data engineer needs to create a dashboard to display social media trends during the last hour of a large company event. The dashboard needs to display the associated metrics with a latency of less than 1 minute.
Which solution meets these requirements?

A) Publish the raw social media data to an Amazon Kinesis Data Firehose delivery stream. Use Kinesis Data Analytics for SQL Applications to perform a sliding window analysis to compute the metrics and output the results to a Kinesis Data Streams data stream. Configure an AWS Lambda function to save the stream data to an Amazon DynamoDB table. Deploy a real-time dashboard hosted in an Amazon S3 bucket to read and display the metrics data stored in the DynamoDB table.

B) Publish the raw social media data to an Amazon Kinesis Data Firehose delivery stream. Configure the stream to deliver the data to an Amazon Elasticsearch Service cluster with a buffer interval of 0 seconds. Use Kibana to perform the analysis and display the results.

C) Publish the raw social media data to an Amazon Kinesis Data Streams data stream. Configure an AWS Lambda function to compute the metrics on the stream data and save the results in an Amazon S3 bucket. Configure a dashboard in Amazon QuickSight to query the data using Amazon Athena and display the results.

D) Publish the raw social media data to an Amazon SNS topic. Subscribe an Amazon SQS queue to the topic. Configure Amazon EC2 instances as workers to poll the queue, compute the metrics, and save the results to an Amazon Aurora MySQL database. Configure a dashboard in Amazon QuickSight to query the data in Aurora and display the results.

ANSWER16: Show Answer

Notes/Hint16: Show Notes

Reference16: Amazon Kinesis Data Analytics can query data in a Kinesis Data Firehose delivery stream in near-real time using SQL

 

Question 17: A real estate company is receiving new property listing data from its agents through .csv files every day and storing these files in Amazon S3. The data analytics team created an Amazon QuickSight visualization report that uses a dataset imported from the S3 files. The data analytics team wants the visualization report to reflect the current data up to the previous day. How can a data analyst meet these requirements?

A) Schedule an AWS Lambda function to drop and re-create the dataset daily.

B) Configure the visualization to query the data in Amazon S3 directly without loading the data into SPICE.

C) Schedule the dataset to refresh daily.

D) Close and open the Amazon QuickSight visualization.

ANSWER17: Show Answer

Notes/Hint17: Show Notes

Reference17: Amazon QuickSight and SPICE

 

Question 18: You need to migrate data to AWS. It is estimated that the data transfer will take over a month via the current AWS Direct Connect connection your company has set up. Which AWS tool should you use?

A) Establish additional Direct Connect connections.

B) Use Data Pipeline to migrate the data in bulk to S3.

C) Use Kinesis Firehose to stream all new and existing data into S3.

D) Snowball

ANSWER18: Show Answer

Notes/Hint18: Show Notes

Reference18: Snowball

 

Question 19: You currently have an on-premises Oracle database and have decided to leverage AWS and use Aurora. You need to do this as quickly as possible. How do you achieve this?

A) It is not possible to migrate an on-premises database to AWS at this time.

B) Use AWS Data Pipeline to create a target database, migrate the database schema, set up the data replication process, initiate the full load and a subsequent change data capture and apply, and conclude with a switchover of your production environment to the new database once the target database is caught up with the source database.

C) Use AWS Database Migration Services and create a target database, migrate the database schema, set up the data replication process, initiate the full load and a subsequent change data capture and apply, and conclude with a switch-over of your production environment to the new database once the target database is caught up with the source database.

D) Use AWS Glue to crawl the on-premises database schemas and then migrate them into AWS with Data Pipeline jobs.

https://aws.amazon.com/dms/faqs/

ANSWER2: Show Answer

Notes/Hint19: Show Notes

Reference19: DMS

Question 20: A financial company uses Amazon EMR for its analytics workloads. During the company’s annual security audit, the security team determined that none of the EMR clusters’ root volumes are encrypted. The security team recommends the company encrypt its EMR clusters’ root volume as soon as possible.
Which solution would meet these requirements?

A) Enable at-rest encryption for EMR File System (EMRFS) data in Amazon S3 in a security configuration. Re-create the cluster using the newly created security configuration.

B) Specify local disk encryption in a security configuration. Re-create the cluster using the newly created security configuration.

C) Detach the Amazon EBS volumes from the master node. Encrypt the EBS volume and attach it back to the master node.

D) Re-create the EMR cluster with LZO encryption enabled on all volumes.

ANSWER20: Show Answer

Notes/Hint20: Show Notes

Reference20: EMR Cluster Local disk encryption

Question 21: A company has a clickstream analytics solution using Amazon Elasticsearch Service. The solution ingests 2 TB of data from Amazon Kinesis Data Firehose and stores the latest data collected within 24 hours in an Amazon ES cluster. The cluster is running on a single index that has 12 data nodes and 3 dedicated master nodes. The cluster is configured with 3,000 shards and each node has 3 TB of EBS storage attached. The Data Analyst noticed that the query performance of Elasticsearch is sluggish, and some intermittent errors are produced by the Kinesis Data Firehose when it tries to write to the index. Upon further investigation, there were occasional JVMMemoryPressure errors found in Amazon ES logs.

What should be done to improve the performance of the Amazon Elasticsearch Service cluster?

Djamga Data Sciences Big Data – Data Analytics Youtube Playlist

2- Prepare for Your AWS Certification Exam

3- LinuxAcademy

Big Data – Data Analytics Jobs:

 

Big Data – Data Analytics – Data Sciences Latest News:

DATA ANALYTICS Q&A:

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Clever Questions, Answers, Resources about:

• Data Sciences
• Big Data
• Data Analytics
• Data Sciences
• Databases
• Data Streams
• Large DataSets

What Is a Data Scientist?

Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician. – Josh Wills

Data scientists apply sophisticated quantitative and computer science skills to both structure and analyze massive stores or continuous streams of unstructured data, with the intent to derive insights and prescribe action. – Burtch Works Data Science Salary Survey, May 2018

More than anything, what data scientists do is make discoveries while swimming in data… In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data. – Data Scientist: The Sexiest Job of the 21st Century, Harvard Business Review

Do All Data Scientists Hold Graduate Degrees?

Data scientists are highly educated. With exceedingly rare exception, every data scientist holds at least an undergraduate degree. 91% of data scientists in 2018 held advanced degrees. The remaining 9% all held undergraduate degrees. Furthermore,

• 25% of data scientists hold a degree in statistics or mathematics,
• 20% have a computer science degree,
• an additional 20% hold a degree in the natural sciences, and
• 18% hold an engineering degree.

The remaining 17% of surveyed data scientists held degrees in business, social science, or economics.

How Are Data Scientists Different From Data Analysts?

Broadly speaking, the roles differ in scope: data analysts build reports with narrow, well-defined KPIs. Data scientists often to work on broader business problems without clear solutions. Data scientists live on the edge of the known and unknown.

We’ll leave you with a concrete example: A data analyst cares about profit margins. A data scientist at the same company cares about market share.

How Is Data Science Used in Medicine?

Data science in healthcare best translates to biostatistics. It can be quite different from data science in other industries as it usually focuses on small samples with several confounding variables.

How Is Data Science Used in Manufacturing?

Data science in manufacturing is vast; it includes everything from supply chain optimization to the assembly line.

What are data scientists paid?

Most people are attracted to data science for the salary. It’s true that data scientists garner high salaries compares to their peers. There is data to support this: The May 2018 edition of the BurtchWorks Data Science Salary Survey, annual salary statistics were

Note the above numbers do not reflect total compensation which often includes standard benefits and may include company ownership at high levels.

How will data science evolve in the next 5 years?

Will AI replace data scientists?

What is the workday like for a data scientist?

It’s common for data scientists across the US to work 40 hours weekly. While company culture does dictate different levels of work life balance, it’s rare to see data scientists who work more than they want. That’s the virtue of being an expensive resource in a competitive job market.

How do I become a Data Scientist?

The roadmap given to aspiring data scientists can be boiled down to three steps:

1. Earning an undergraduate and/or advanced degree in computer science, statistics, or mathematics,
2. Building their portfolio of SQL, Python, and R skills, and
3. Getting related work experience through technical internships.

All three require a significant time and financial commitment.

There used to be a saying around datascience: The road into a data science starts with two years of university-level math.

What Should I Learn? What Order Do I Learn Them?

This answer assumes your academic background ends with a HS diploma in the US.

1. Python
2. Differential Calculus
3. Integral Calculus
4. Multivariable Calculus
5. Linear Algebra
6. Probability
7. Statistics

Some follow up questions and answers:

Why Python first?

• Python is a general purpose language. R is used primarily by statisticians. In the likely scenario that you decide data science requires too much time, effort, and money, Python will be more valuable than your R skills. It’s preparing you to fail, sure, but in the same way a savings account is preparing you to fail.

When do I start working with data?

• You’ll start working with data when you’ve learned enough Python to do so. Whether you’ll have the tools to have any fun is a much more open-ended question.

How long will this take me?

• Assuming self-study and average intelligence, 3-5 years from start to finish.

How Do I Learn Python?

If you don’t know the first thing about programming, start with MIT’s course in the curated list.

These modules are the standard tools for data analysis in Python:

• pandas (and by extension, numpy)Check out Minimally Sufficient Pandas for style guides and best practices.
• matplotlib and seaborn See /u/rhiever’s response to How do you decide between the plotting libraries: Matplotlib, Seaborn, Bokeh?Don’t worry about bokeh or dash unless you have a personal interest in interactive visualizations.
• scipy and scikit-learnInternalize the .fit() and .predict() pattern.

Curated Threads & Resources

1. MIT’s Introduction to Computer Science and Programming in Python A free, archived course taught at MIT in the fall 2016 semester.
2. Data Scientist with Python Career Track | DataCamp The first courses are free, but unlimited access costs $29/month. Users usually report a positive experience, and it’s one of the better hands-on ways to learn Python.
3. Sentdex’s (Harrison Kinsley) Youtube Channel Related to Python Programming Tutorials
4. /r/learnpython is an active sub and very useful for learning the basics.

How Do I Learn R?

If you don’t know the first thing about programming, start with R for Data Science in the curated list.

These modules are the standard tools for data analysis in Python:

Curated Threads & Resources

1. R for Data Science by Hadley WickhamA free ebook full of succinct code examples. Terrific for learning tidyverse syntax.Folks with some math background may prefer the free alternative, Introduction to Statistical Learning.
2. Data Scientist with R Career Track | DataCamp The first courses are free, but unlimited access costs $29/month. Users usually report a positive experience, and it’s one of the few hands-on ways to learn R.
3. R Inferno Learners with a CS background will appreciate this free handbook explaining how and why R behaves the way that it does.

How Do I Learn SQL?

Prioritize the basics of SQL. i.e. when to use functions like POW, SUM, RANK; the computational complexity of the different kinds of joins.

Concepts like relational algebra, when to use clustered/non-clustered indexes, etc. are useful, but (almost) never come up in interviews.

You absolutely do not need to understand administrative concepts like managing permissions.

Finally, there are numerous query engines and therefore numerous dialects of SQL. Use whichever dialect is supported in your chosen resource. There’s not much difference between them, so it’s easy to learn another dialect after you’ve learned one.

Curated Threads & Resources

1. The SQL Tutorial for Data Analysis | Mode.com
2. Introduction to Databases A Free MOOC supported by Stanford University.
3. SQL Queries for Mere MortalsA $30 book highly recommended by /u/karmanujan

How Do I Learn Calculus?

Fortunately (or unfortunately), calculus is the lament of many students, and so resources for it are plentiful. Khan Academy mimics lectures very well, and Paul’s Online Math Notes are a terrific reference full of practice problems and solutions.

Calculus, however, is not just calculus. For those unfamiliar with US terminology,

• Calculus I is differential calculus.
• Calculus II is integral calculus.
• Calculus III is multivariable calculus.
• Calculus IV is differential equations.

Differential and integral calculus are both necessary for probability and statistics, and should be completed first.

Multivariable calculus can be paired with linear algebra, but is also required.

Differential equations is where consensus falls apart. The short it is, they’re all but necessary for mathematical modeling, but not everyone does mathematical modeling. It’s another tool in the toolbox.

Curated Threads & Resources

• Khan AcademyDifferential CalculusIntegral CalculusMultivariable CalculusDifferential Equations
• Paul’s Online Math NotesDifferential CalculusIntegral CalculusMultivariable Calculus

How Do I Learn Probability?

Probability is not friendly to beginners. Definitions are rooted in higher mathematics, notation varies from source to source, and solutions are frequently unintuitive. Probability may present the biggest barrier to entry in data science.

It’s best to pick a single primary source and a community for help. If you can spend the money, register for a university or community college course and attend in person.

The best free resource is MIT’s 18.05 Introduction to Probability and Statistics (Spring 2014). Leverage /r/learnmath, /r/learnmachinelearning, and /r/AskStatistics when you get inevitably stuck.

How Do I Learn Linear Algebra?

Curated Threads & Resources https://www.youtube.com/watch?v=fNk_zzaMoSs&index=1&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

What does the typical data science interview process look like?

For general advice, Mastering the DS Interview Loop is a terrific article. The community discussed the article here.

Briefly summarized, most companies follow a five stage process:

1. Coding Challenge: Most common at software companies and roles contributing to a digital product.
2. HR Screen
3. Technical Screen: Often in the form of a project. Less frequently, it takes the form of a whiteboarding session at the onsite.
4. Onsite: Usually the project from the technical screen is presented here, followed by a meeting with the director overseeing the team you’ll join.
5. Negotiation & Offer

• Mastering the DS Interview Loop

Preparation:

1. Practice questions on Leetcode which has both SQL and traditional data structures/algorithm questions
2. Review Brilliant for math and statistics questions.
3. SQL Zoo and Mode Analytics both offer various SQL exercises you can solve in your browser.

Tips:

1. Before you start coding, read through all the questions. This allows your unconscious mind to start working on problems in the background.
2. Start with the hardest problem first, when you hit a snag, move to the simpler problem before returning to the harder one.
3. Focus on passing all the test cases first, then worry about improving complexity and readability.
4. If you’re done and have a few minutes left, go get a drink and try to clear your head. Read through your solutions one last time, then submit.
5. It’s okay to not finish a coding challenge. Sometimes companies will create unreasonably tedious coding challenges with one-week time limits that require 5–10 hours to complete. Unless you’re desperate, you can always walk away and spend your time preparing for the next interview.

Remember, interviewing is a skill that can be learned, just like anything else. Hopefully, this article has given you some insight on what to expect in a data science interview loop.

The process also isn’t perfect and there will be times that you fail to impress an interviewer because you don’t possess some obscure piece of knowledge. However, with repeated persistence and adequate preparation, you’ll be able to land a data science job in no time!

What does the Airbnb data science interview process look like? [Coming soon]

What does the Facebook data science interview process look like? [Coming soon]

What does the Uber data science interview process look like? [Coming soon]

What does the Microsoft data science interview process look like? [Coming soon]

What does the Google data science interview process look like? [Coming soon]

What does the Netflix data science interview process look like? [Coming soon]

What does the Apple data science interview process look like? [Coming soon]

Question: How is SQL used in real data science jobs?

Real life enterprise databases are orders of magnitude more complex than the “customers, products, orders” examples used as teaching tools. SQL as a language is actually, IMO, a relatively simple language (the db administration component can get complex, but mostly data scientists aren’t doing that anyways). SQL is an incredibly important skill though for any DS role. I think when people emphasize SQL, what they really are talking about is the ability to write queries that interrogate the data and discover the nuances behind how it is collected and/or manipulated by an application before it is written to the dB. For example, is the employee’s phone number their current phone number or does the database store a history of all previous phone numbers? Critically important questions for understanding the nature of your data, and it doesn’t necessarily deal with statistics! The level of syntax required to do this is not that sophisticated, you can get pretty damn far with knowledge of all the joins, group by/analytical functions, filtering and nesting queries. In many cases, the data is too large to just select * and dump into a csv to load into pandas, so you start with SQL against the source. In my mind it’s more important for “SQL skills” to know how to generate hypotheses (that will build up to answering your business question) that can be investigated via a query than it is to be a master of SQL’s syntax. Just my two cents though!

12000 Years of Human Population Dynamic

[OC] 12,000 years of human population dynamics from dataisbeautiful

Human population density estimates based on the Hyde 3.2 model.

Capitol insurrection arrests per million people by state

[OC] Capitol insurrection arrests per million people by state from dataisbeautiful

Data Source: Made in Google Sheets using data from this USA Today article (for the number of arrests by arrestee’s home state) and this spreadsheet of the results of the 2020 Census (for the population of each state and DC in 2020, which was used as the denominator in calculating arrests/million people).