Kimberly Cook

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Interview Study Guide for Data Science To Get Job Quickly

By Kimberly Cook |Email | Aug 2, 2019 | 14793 Views

121 best resources to help you land your data science dream job. 


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.

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

Machine Learning Algorithms


A/B Testing - Video
SVM - Post
How SVM - Video
Adaboost - Post
AdaBoost - Video
Gradient Boost Part 1: Regression Main Ideas - Video
K-Means Clustering - The Math of Intelligence - 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

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.
  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

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

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

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, Fortnite new features, etc)
How do you measure the success or failure of a product/product feature
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
Fizz Buzz

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.


Data Structures Videos

Algorithm Videos
Algorithms: Graph Search, DFS and BFS

String Manipulation
Coding Interview Question and Answer: Longest Consecutive Characters
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!
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.
SQL - Videos

Post SQL Problems
Binary Tree Nodes

Source: HOB
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