The Data Science Interview Study Guide

121 questions, videos and resources to help you land your data science dream job

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Data science interviews, like other technical interviews, require plenty of preparation. There are a number of subjects that need to be covered in order to ensure you are ready for back-to-back questions on statistics, programming and machine learning.

Before we get started, there’s one tip I’d like to share.

I’ve noticed that there are several types of data science interviews that companies conduct.

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 you aren’t sure which type of interview you will be facing. Some companies are very good at keeping interviews consistent, but even then, teams can 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 material.

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

Due to this variance, we’ve created a checklist to keep track of what subject areas you did study and what you still need to cover.

Data Science Study Checklist

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

Machine Learning Algorithms

1. Logistic Regression — Video

2. A/B Testing — Video

3. Decision Tree — Post

4. SVM — Post

5. How SVM — Video

6. Principal Component Analysis: PCA — Post

7. Principal Component Analysis — Video

8. Adaboost — Post

9. AdaBoost — Video

10. A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning — Post

11. Gradient Boost Part 1: Regression Main Ideas — Video

12. K-Means Clustering — The Math of Intelligence — Video

13. Bayesian Network — Post

14. Neural Network — Post

15. Dimensionality reduction algorithms — Post

16. How kNN algorithm works — Video

Probability And Statistics

At large tech companies, it is common to receive an occasional probability or statistics question. While the questions won’t necessarily require complex math, if you haven’t thought about independent and dependent probabilities in while it is good to review setting up the basic formulas.

Probability Videos

1. Dependent probability introduction

2. Independent & dependent probability

3. Independent Problems

4. Conditional Prob Article

Probability Quiz

1. Probability & Statistics — Set 6

2. Probability & Statistics — Set 2

3. Independent Probability

4. Dependent Probability

Probability Interview Questions

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

1. 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?

2. 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?

3. 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 is an equal number of males and females in the world.

4. How many ways can you split 12 people into 3 teams of 4?

Statistics Pre-Quizzes

1. 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.

1. Bias-Variance Trade-Off

2. Confusion Matrix

3. ROC curve

4. Normal Distribution

5. P-Value

6. Pearson Spearman

7. Normal distribution problem: z-scores (from ck12.org)

8. Continuous Probability Distributions

9. Standardizing Normally Distributed Random Variables (fast version)

10. Statistics 101: Simple Linear Regression, The Very Basics

11. Statistics 101: Linear Regression, Outliers, and Influential Observations

12. Statistics 101: ANOVA, A Visual Introduction

13. Statistics 101: Multiple Regression, The Very Basics

14. Statistics: Variance of a population | Probability and Statistics | Khan Academy

15. Expected Value: E(X)

16. Law of large numbers | Probability and Statistics | Khan Academy

17. Central limit theorem | Inferential statistics | Probability and Statistics | Khan Academy

18. Margin of error 1 | Inferential statistics | Probability and Statistics | Khan Academy

19. Margin of error 2 | Inferential statistics | Probability and Statistics | Khan Academy

20. Hypothesis testing and p-values | Inferential statistics | Probability and Statistics | Khan Academy

21. One-tailed and two-tailed tests | Inferential statistics | Probability and Statistics | Khan Academy

22. Type 1 errors | Inferential statistics | Probability and Statistics | Khan Academy

23. Large sample proportion hypothesis testing | Probability and Statistics | Khan Academy

24. Boosting and Bagging

Statistics Post-Quiz

1. Data Science Probability Statistics 17

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 certain metrics going the way you want them to might not always be good. The reason people are spending more time on your website might be because webpages are taking longer to load or other facing similar problems. This is why metrics are tricky and what you measure is important.

Product And Experiment Design Concepts

1. User Engagement Metrics

2. Data Scientist’s Toolbox: Experimental Design -Video

3. A/B Testing Guide

4. Multivariate Testing

5. 6 Themes Of Metrics

Product And Metrics Questions

1. An important metric goes down, how would you dig into the causes?\

2. 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, Fortnite new features, etc)

3. How do you measure the success or failure of a product/product feature

4. Google has released a new version of its 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

1. Fizz Buzz

2. Find The Kth Smallest/Largest Integer In An Array

3. Nth Fibonacci

Algorithms And Data Structures

Pre-Study Problems

Before going through the video content about data structures and algorithms, consider trying out the problems below. This will help you know what you need to focus on.

1. Sum of Even Numbers After Queries

2. Robot Return to Origin

3. N-Repeated Element in Size 2N Array

4. Balanced Binary Tree

Data Structures Videos

1. Data Structures & Algorithms #1 — What Are Data Structures?

2. Multi-dim (video)

3. Data Structures: Linked Lists

4. Core Linked Lists Vs Arrays (video)

5. Data Structures: Trees

6. Data Structures: Heaps

7. Data Structures: Hash Tables

8. Data Structures: Stacks and Queues

Algorithm Videos

1. Python Algorithms for Interviews

2. Algorithms: Graph Search, DFS and BFS

3. BFS(breadth-first search) and DFS(depth-first search) (video)

4. Algorithms: Binary Search

5. Binary Search Tree Review (video)

6. Algorithms: Recursion

7. Algorithms: Bubble Sort

8. Algorithms: Merge Sort

9. Algorithms: Quicksort

String Manipulation

1. Coding Interview Question and Answer: Longest Consecutive Characters

2. Sedgewick — Substring Search (videos)

SQL

Post-Study Problems

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

1. Bigger Is Greater

2. ZigZag Conversion

3. Reverse Integer

4. Combination Sum II

5. Multiplying Strings

6. Larry’s Array

7. Short Palindrome

8. Valid Number

9. Bigger is Greater

10. The Full Counting Sort

11. Lily’s Homework

SQL — Problems

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

1. Trips and Users

2. Human Traffic of Stadium

3. Department Top Three Salaries

4. Exchange Seats

5. Hackerrank The Report

6. Nth Highest Salary

7. Symmetric Pairs

8. Occupations

9. Placements

10. Ollivander’s Inventory

SQL — Videos

1. IQ15: 6 SQL Query Interview Questions

2. Learning about ROW_NUMBER and Analytic Functions

3. Advanced Implementation Of Analytic Functions

4. Advanced Implementation Of Analytic Functions Part 2

5. Wise Owl SQL Videos

Post SQL Problems

1. Binary Tree Nodes

2. Weather Observation Station 18

3. Challenges

4. Print Prime Numbers

5. Big Countries

6. Exchange Seats

7. SQL Interview Questions: 3 Tech Screening Exercises (For Data Analysts)

Conclusion

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!

If there is something you think we left off or you have additional resources which you think would be a benefit, please let me know. Thank you!