SeattleDataGuy

Posted on Oct 13, 2019

Data Science Interview Study Guide

#sql #python #machinelearning #career

Data science technical interviews, like other technical interviews require plenty of preparation. There are lots of subjects that need to be covered in order to ensure you are ready for a data science interview.

Before we get started. We want to point out some tips.

One of the things we have noticed is there are several types of data science interviews.

Some data science interviews are very product and metric driven. These interviews focus more on asking product questions like what kind of metrics would you use to show what you should improve in a product. These are often paired with SQL and some Python questions.

The other type of data science interview tends to be a mix of programming and machine learning.

We recommend asking the recruiter if they don't provide you any tips. Some companies are very good at keeping interviews consistent but even then, teams sometimes deviate depending on what they are looking for. Here are some examples of what we have noticed about some companies data science interviews.

Airbnb --- Product Heavy, Metrics diagnostics, Metrics creation, A/B testing, Tons of behavioral questions and take home.

Netflix --- Product-sense questions, A/B testing, experimental design ,metric design

Microsoft --- Programming Heavy, Binary Tree Traversal, SQL ,Machine Learning

Expedia --- Product, Programming, sql, product sense, Machine learning questions about SVM, regression and decision tree

In order to keep track of your progress we have put together a study checklist to help you out!

Download The Data Science Interview Checklist

Let's first start with making sure you can explain the basic data science algorithms.

Machine Learning Algorithms

Logistic Regression --- Video
A/B Testing? --- Video
Decision Tree --- Post
SVM --- Post
How SVM --- Video
Principal Component Analysis: PCA --- post
Principal Component Analysis --- Video
Adaboost --- Post
AdaBoost --- Video
A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning --- Post
Gradient Boost Part 1: Regression Main Ideas --- Video
K-Means Clustering --- The Math of Intelligence --- Video
Bayesian Network --- Post
Neural Network --- Post
Dimensionality reduction algorithms --- Post
How kNN algorithm works --- Video

Probability And Statistics

A common question you might get at FAANG companies and other tech companies alike is the occasional probability or statistics question. The questions won't necessarily require complex math. However, if you haven't thought about independent and dependent probabilities in while. It is good to review setting up the basic formulas.

Probability Videos

Probability Quiz

Probability Interview Questions

Most of these questions are either similar to the ones we have been asked or taken directly from glassdoor.com.

A die is rolled twice. What is the probability of showing a 3 on the first roll and an odd number on the second roll?
In any 15-minute interval, there is a 20% probability that you will see at least one shooting star. What is the probability that you see at least one shooting star in the period of an hour?
Alice has 2 kids and one of them is a girl. What is the probability that the other child is also a girl? You can assume that there are an equal number of males and females in the world.
You're about to get on a plane to Seattle. You want to know
How many ways can you split 12 people into 3 teams of 4?

Statistics Pre-Quizzes

Data Science Probability Statistics 14

Statistics Concepts

Statistics is a broad concept so don't get too bogged down in the details of each of these videos. Instead, just make sure you can explain each of these concepts at the surface level.

Statistics Post-Quiz

Data Science Probability Statistics 17

Interview Prep Courses

Another great way to prep for data science interviews is to take a course on interviews. Often times these can cost somewhere in the range of $10-$200. Depending on which site you use and more importantly...it also depends on how udemy's algorithm works. Sometimes udemy charges $10 for the same course it might later charge $200

Data Science Career Guide -- Interview Preparation ($10-$200 depending on what the algorithm calculates)
Data Science Interview Preparation -- Career Guide ($10-$200 depending on what the algorithm calculates

Product And Experiment Designs

Product sense is an important skill for data scientists. Knowing what to measure on new products and why can help determine whether a product is doing well or not. The funny thing is, sometimes metrics going the way you want them to might not always be good. Sometimes the reason people are spending more time on your website is because webpages might be taking longer to load or other similar problems. This is why metrics are tricky and what you measure is important.

Product And Experiment Design Concepts

Product And Metrics Questions

An important metric goes down, how would you dig into the causes?\
What metrics would you use to quantify the success of youtube ads (this could also be extended to other products like Snapchat filters, twitter live-streaming, fort-nite new features, etc)
How do you measure the success or failure of a product/product feature
Google has released a new version of their search algorithm, for which they used A/B testing. During the testing process, engineers realized that the new algorithm was not implemented correctly and returned less relevant results. Two things happened during testing:

People in the treatment group performed more queries than the control group.
Advertising revenue was higher in the treatment group as well.

What may be the cause of people in the treatment group performing more searches than the control group? There are different possible answers here.

Question 4 borrowed from Zarantech; We really enjoyed it and thought it was a good example of how things can go wrong.

Programming

Just because data science doesn't always require heavy programming, it doesn't mean that interviewers won't ask you traverse a binary tree. So make sure you ask your interviewer what to expect. Don't be daunted by these questions. Pick a few to do just so you're not surprised in an interview.

Pre-Video Questions

Algorithms And Data Structures

Pre-Study ProblemsBefore going through the video content about data structures and algorithms. Consider trying out these problems below. See if you can answer them. This will help you know what to focus on.

Algorithms And Data Structures Videos

Data Structures

Algorithms

String Manipulation

SQL

Post-Study Problems

Now that you have studied for a bit, and watched a few videos. Let's try some more problems!

SQL --- Problems

Generally, there will be at least one interview focused on SQL. In addition, the interviewers might take you through an entire process of developing a product, a metric and then a query to measure the effectiveness of that metric.

SQL --- Videos

Post SQL Problems

Courses

If you have finished all of the questions and videos above and still feel like you need to review, then consider taking the courses below. They will help you get some new perspective. The truth is, these courses will pretty much cover the same concepts covered in the videos we have provided. However, sometimes, hearing the same information from multiple sources is helpful.

Technical interviews can be tough. Whether they are for software engineers, data engineers or data scientists. We do hope this study guide helps you keep track of your progress!

Let us know if you think a subject is missing. Thank you!

If you enjoyed this post about data science then consider these posts as well!

4 Must Have Skills For Data Scientists

How Algorithms Can Become Unethical and Biased

Using Agile Methodologies in Data Science

142 Resources for Mastering Coding Interviews

What Is A Data Scientist Solving The Balanced Bracket Problem