Coursera Data Scientist Interview Questions + Guide in 2024

Coursera, launched in 2012 by Stanford professors Andrew Ng and Daphne Koller, is a leading online learning platform with 142 million learners worldwide. Known for its diverse course offerings through partnerships with over 325 universities and companies, Coursera aims to make high-quality education accessible to all.

As a Data Scientist at Coursera, you’ll join a team committed to revolutionizing education through data-driven decision-making. Your role will leverage extensive user data to inform product strategies, measure impacts through experimentation, and enhance personalized learning experiences. Ideal candidates will have strong analytical skills, expertise in statistical modeling, and a passion for online education.

Explore this guide for insights into the interview process, commonly asked Coursera data scientist interview questions, and tips to excel.

Recruiter/Hiring Manager Call Screening

Once your application catches the eye of the Coursera Talent Acquisition Team, a recruiter will reach out for an initial screening. This conversation generally focuses on your background, interest in Coursera, and overall fit for the role. You might be asked to explain your experience in data science, particularly in fields such as applied math, statistics, or machine learning. Expect to spend around 30 minutes in this discussion, with potential surface-level technical and behavioral questions.

Online Assessment

If you advance past the initial screening, you will be invited to complete a timed online coding assessment hosted on Hackerrank. This assessment typically includes:

• 2 SQL questions
• 1 Python programming question
• 6 multiple choice questions on statistics, probability, and SQL

Applicants usually have 100 minutes to tackle the 7 questions, which test basic to intermediate concepts pertinent to data science.

Technical Phone Interview

Those who perform well in the online assessment are scheduled for a technical phone screen. This round, often conducted by a senior data scientist, features a mix of technical and behavioral questions. You may encounter case studies focusing on A/B testing, SQL query writing, and questions about past research experiences. It is also common to discuss how you would approach problems, such as evaluating the difficulty level of Coursera courses. This stage is crucial and typically lasts 45 minutes to an hour.

Onsite (or Virtual Onsite) Interview Rounds

Candidates who succeed in the phone screen are then invited to the final round of interviews, which can be virtual due to Coursera’s commitment to a remote-first work culture. The onsite interview loop usually comprises 6 interviews over approximately 7 hours. These sessions include technical questions, business metric discussions, causal inference, experimental design/hypothesis testing, and a significant data analysis exercise (2 hours) using a language or tool of your choice. The interviewers are generally known for their warm and supportive demeanor.

Typically, interviews at Coursera vary by role and team, but commonly data scientist interviews follow a fairly standardized process across these question topics.

1. Create a function find_bigrams to return a list of all bigrams in a sentence.

Write a function called find_bigrams that takes a sentence or paragraph of strings and returns a list of all its bigrams in order. A bigram is a pair of consecutive words.

2. Write a query to get the last transaction for each day from a table of bank transactions.

Given a table of bank transactions with columns id, transaction_value, and created_at, write a query to get the last transaction for each day. The output should include the ID of the transaction, the datetime of the transaction, and the transaction amount ordered by datetime.

3. Create a function find_change to find the minimum number of coins for a given amount.

Write a function find_change to find the minimum number of coins that make up the given amount of change cents. Assume we only have coins of value 1, 5, 10, and 25 cents.

4. Design a function to simulate drawing balls from a jar.

Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

5. Develop a function calculate_rmse to compute the root mean squared error.

Write a function calculate_rmse to calculate the root mean squared error of a regression model. The function should take in two lists, one that represents the predictions y_pred and another with the target values y_true.

6. How would you set up an A/B test for multiple changes in a sign-up funnel?

A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

7. Would you suspect anything unusual about an A/B test with 20 variants where one is significant?

Your manager ran an A/B test with 20 different variants and found one significant result. Would you find anything suspicious about these results?

8. Why might the average number of comments per user decrease despite user growth?

A social media company sees a slow decrease in the average number of comments per user from January to March in a new city, despite consistent user growth. What could be the reasons, and what metrics would you investigate?

9. What metrics would you use to determine the value of each marketing channel?

Given all the different marketing channels and their respective costs at a company selling B2B analytics dashboards, what metrics would you use to assess the value of each channel?

10. How would you locate a mouse in a 4x4 grid using the fewest scans?

You have a 4x4 grid with a mouse trapped in one cell. You can scan subsets of cells to know if the mouse is within that subset. How would you determine the mouse’s location using the fewest number of scans?

11. What is the expected number of good ads rated by different types of raters?

1. Suppose we have 100 raters, each rating one ad independently. What’s the expected number of good ads?
2. Now, suppose we have 1 rater rating 100 ads. What’s the expected number of good ads?
3. Suppose we have 1 ad rated as bad. What’s the probability the rater was lazy?

11. Write a function to simulate coin tosses with a given probability of heads.

Create a function that takes the number of tosses and the probability of heads as input and returns a list of randomly generated results (‘H’ for heads, ’T’ for tails) equal in length to the number of tosses.

12. How do you calculate the sample variance of a list of integers?

Write a function that takes a list of integers as input and outputs the sample variance, rounded to 2 decimal places.

13. What is the probability of rolling at least one 3 with dice?

1. What’s the probability of rolling at least one 3 with 2 dice?
2. What’s the probability of rolling at least one 3 given (N) dice?

14. What is the probability of finding an item on Amazon’s website given its availability in warehouses?

Given that the probability of item X being available at warehouse A is 0.6 and at warehouse B is 0.8, what is the probability that item X would be found on Amazon’s website?

15. What’s the difference between Lasso and Ridge Regression?

Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle feature selection and coefficients.

16. What kind of model did the co-worker develop for loan approval?

Identify the type of model used for determining loan approval based on customer inputs.

17. How would you compare two credit risk models for predicting loan defaults?

Since personal loans are monthly installments, describe how you would measure the difference between two credit risk models over a specific timeframe.

18. What metrics would you track to measure the success of a new credit risk model?

List and explain the metrics you would use to evaluate the performance and success of a new credit risk model.

19. How would you evaluate the suitability of a decision tree for predicting loan repayment?

Describe the criteria and methods you would use to determine if a decision tree algorithm is appropriate for predicting loan repayment.

20. How would you evaluate the performance of a decision tree model before and after deployment?

Explain the steps and metrics you would use to assess the performance of a decision tree model both before deployment and after it is in use.

21. How does random forest generate the forest and why use it over logistic regression?

Describe how a random forest algorithm generates its forest of trees and explain the advantages of using random forest over logistic regression.

22. How would you interpret coefficients of logistic regression for categorical and boolean variables?

Explain the interpretation of logistic regression coefficients when dealing with categorical and boolean variables.

Here are some quick tips to help you navigate through Coursera’s data scientist interview process smoothly:

• Preparation for Technical Assessments: Coursera’s initial technical assessments are crucial. Brush up on SQL, Python, probability, and statistics.

• Showcase Analytical Prowess: If you make it to the case study stage, focus on clear problem-solving, specifying analysis methods, and conveying your thought process.

• Cultural Fit and Communication: Coursera values strong communication and the ability to explain complex ideas to non-technical audiences. Prepare to discuss your experiences clearly and concisely by practicing through our peer-to-peer mock interviews.

What is the average salary for a Data Scientist at Coursera?

How does Coursera ensure the interview process is efficient and organized?

Coursera’s interview process aims to be swift and efficient, especially in the early stages. After applying, candidates are usually contacted within a few days for an online assessment. Feedback is provided promptly, but delays can occasionally occur in later interview stages.

What sets Coursera’s Data Science team apart?

Coursera’s Data Science team is dedicated to transforming education through data-driven insights and decision-making. The team focuses on personalized learning experiences and employs various analytical and statistical techniques to drive product and business decisions.

As Coursera continues to redefine the educational landscape, the company is looking for dynamic and innovative Data Scientists to join its mission-driven team.

By focusing on your skills in SQL, Python, and statistical modeling, aligning your experience with their product-oriented insights, and demonstrating your passion for expanding online education access, you can distinguish yourself in the interview process.

Good luck with your interview!