Coursera Data Scientist Interview Questions + Guide in 2025

Coursera is an online learning platform that connects learners with educational institutions and organizations to offer courses, specializations, and degrees across a wide range of subjects.

The Data Scientist role at Coursera is pivotal for leveraging data-driven insights to enhance the learning experience and optimize course offerings. Key responsibilities include analyzing large datasets to identify trends and patterns, developing predictive models to inform strategic decisions, and conducting experiments such as A/B testing to test hypotheses around course effectiveness. Candidates are expected to have a strong foundation in statistical analysis, machine learning, and proficiency in programming languages such as Python and SQL. A successful Data Scientist at Coursera should possess excellent problem-solving skills, the ability to communicate complex data insights to non-technical stakeholders, and a passion for education and continuous learning.

This guide will equip you with tailored insights to prepare for your interview, helping you to confidently showcase your expertise and alignment with Coursera's mission.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Coursera. The interview process will assess your technical skills in data analysis, machine learning, and statistical methods, as well as your ability to apply these skills to real-world problems. Be prepared to demonstrate your knowledge of SQL, Python, and statistical concepts, as well as your experience with A/B testing and data-driven decision-making.

Technical Skills

1. Can you explain the assumptions of linear regression?

Understanding the assumptions behind linear regression is crucial for any data scientist, as it impacts the validity of your model.

How to Answer

Discuss the key assumptions such as linearity, independence, homoscedasticity, and normality of residuals. Provide examples of how you would check these assumptions in practice.

Example

“The assumptions of linear regression include linearity, which means the relationship between the independent and dependent variables should be linear. Independence implies that the residuals should not be correlated. Homoscedasticity means that the variance of residuals should be constant across all levels of the independent variable. Lastly, the residuals should be normally distributed. I typically use diagnostic plots to check these assumptions before finalizing my model.”

2. Describe a time you conducted an A/B test. What was your approach?

A/B testing is a common method for evaluating the effectiveness of changes in a product or service.

How to Answer

Outline the steps you took in designing the A/B test, including hypothesis formulation, sample size determination, and metrics for success.

Example

“In my previous role, I conducted an A/B test to evaluate the impact of a new course layout on user engagement. I formulated the hypothesis that the new layout would increase course completion rates. I determined the sample size using power analysis and tracked metrics such as completion rates and user feedback. After analyzing the results, I found a significant increase in engagement, which led to the implementation of the new layout across all courses.”

3. How would you approach a problem where you need to evaluate the difficulty level of Coursera courses?

This question assesses your analytical thinking and ability to apply data science techniques to real-world scenarios.

How to Answer

Discuss the data sources you would use, the metrics you would analyze, and any statistical methods you would apply.

Example

“To evaluate the difficulty level of Coursera courses, I would start by analyzing user feedback and completion rates. I would gather data on course assessments, average time spent on each module, and dropout rates. I could use clustering techniques to categorize courses based on these metrics and apply statistical tests to determine if there are significant differences in user performance across different courses.”

Statistics & Probability

4. What is the difference between Type I and Type II errors?

Understanding these concepts is fundamental in hypothesis testing.

How to Answer

Define both types of errors and provide examples of their implications in a data-driven context.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, in an A/B test, a Type I error would mean concluding that a new feature improves user engagement when it actually does not. Conversely, a Type II error would mean failing to detect an actual improvement when it exists.”

5. Can you explain the concept of p-value?

P-values are a critical component of hypothesis testing and statistical inference.

How to Answer

Explain what a p-value represents and how it is used to make decisions in statistical tests.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis. For example, in an A/B test, if the p-value is less than 0.05, we might conclude that the new feature has a statistically significant effect on user engagement.”

SQL & Data Manipulation

6. Write a SQL query to calculate the running total of registered users by day.

This question tests your SQL skills and ability to manipulate data.

How to Answer

Describe the SQL functions you would use and the logic behind your query.

Example

“I would use the SUM() function along with the OVER() clause to calculate the running total. The query would look something like this: SELECT date, SUM(users) OVER (ORDER BY date) AS running_total FROM registrations; This would give me a cumulative count of registered users for each day.”

7. How would you write a SQL query to get the total count of enrollments and the count of a specific track in one query?

This question assesses your ability to write complex SQL queries.

How to Answer

Explain how you would use aggregation and conditional counting in your SQL query.

Example

“I would use a CASE statement within the COUNT() function to differentiate between the total enrollments and those in a specific track. The query would look like this: SELECT COUNT(*) AS total_enrollments, COUNT(CASE WHEN track = 'specific_track' THEN 1 END) AS specific_track_count FROM enrollments; This allows me to get both counts in a single query.”

Machine Learning

8. What are some common metrics used to evaluate the performance of a classification model?

This question tests your understanding of model evaluation.

How to Answer

Discuss various metrics and when to use them based on the context of the problem.

Example

“Common metrics for evaluating classification models include accuracy, precision, recall, F1 score, and ROC-AUC. Accuracy is useful when classes are balanced, but in cases of class imbalance, precision and recall become more important. The F1 score provides a balance between precision and recall, while ROC-AUC gives insight into the model's performance across different thresholds.”

9. Can you explain the concept of overfitting and how to prevent it?

Overfitting is a critical concept in machine learning that can significantly impact model performance.

How to Answer

Define overfitting and discuss techniques to mitigate it.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor generalization on unseen data. To prevent overfitting, I would use techniques such as cross-validation, regularization, and pruning for decision trees. Additionally, simplifying the model or using dropout in neural networks can also help reduce overfitting.”

Here are some tips to help you excel in your interview.

Prepare for the Online Assessment

The initial step in the interview process typically involves a timed online assessment on HackerRank. This assessment often includes SQL, Python, and statistics questions, so it's crucial to brush up on these skills. Focus on writing efficient SQL queries, understanding statistical concepts, and practicing Python coding problems. Familiarize yourself with common data manipulation tasks and statistical analyses, as these are frequently tested. Additionally, practice under timed conditions to simulate the actual assessment environment.

Understand A/B Testing and Case Studies

A significant part of the interview process may involve A/B testing case studies and related questions. Be prepared to discuss how you would design experiments, analyze results, and interpret data. Familiarize yourself with key metrics and how they relate to user engagement and course effectiveness. Having a few examples from your past experience where you successfully conducted A/B tests or similar analyses will help you demonstrate your practical knowledge.

Showcase Your Communication Skills

During interviews, especially in technical discussions, clear communication is vital. Interviewers appreciate candidates who can articulate their thought processes and explain complex concepts in an understandable way. Practice explaining your past projects and analyses in a concise manner, focusing on the impact of your work. Be ready to discuss not just the "how" but also the "why" behind your decisions and methodologies.

Engage with the Interviewers

The interviewers at Coursera are known for being supportive and friendly. Use this to your advantage by engaging them in conversation. Ask clarifying questions if you don’t understand something, and don’t hesitate to share your thoughts and ideas. This not only shows your interest in the role but also helps build rapport with your interviewers.

Be Ready for Behavioral Questions

In addition to technical skills, be prepared for behavioral questions that assess your fit within the company culture. Coursera values collaboration, innovation, and a passion for education. Reflect on your past experiences and be ready to discuss how you embody these values. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and relevant examples.

Familiarize Yourself with Coursera’s Mission and Products

Understanding Coursera’s mission to provide accessible education and its various offerings will help you align your answers with the company’s goals. Be prepared to discuss how your skills and experiences can contribute to their mission. This knowledge will not only help you answer questions more effectively but also demonstrate your genuine interest in the company.

Follow Up with Gratitude

After your interviews, send a thoughtful thank-you email to your interviewers. Express your appreciation for their time and reiterate your enthusiasm for the role. This small gesture can leave a positive impression and reinforce your interest in joining the Coursera team.

By following these tips and preparing thoroughly, you can approach your interview with confidence and increase your chances of success. Good luck!

The interview process for a Data Scientist role at Coursera is structured and thorough, designed to assess both technical skills and cultural fit within the team. The process typically unfolds in several key stages:

1. Application and Online Assessment

Candidates begin by submitting their application, often through platforms like LinkedIn. Following this, they are invited to complete a timed online assessment via HackerRank. This assessment generally includes a mix of SQL, Python programming, and statistical questions, testing candidates on their coding abilities and understanding of data science concepts. The assessment is designed to be completed within a set time limit, usually around 100 minutes, and may consist of multiple-choice questions alongside coding tasks.

2. Technical Phone Screen

After successfully completing the online assessment, candidates typically move on to a technical phone interview. This round is often conducted by a hiring manager or a senior data scientist and focuses on discussing the candidate's past experiences, technical knowledge, and problem-solving abilities. Expect to engage in case study discussions, where you may be asked to analyze data scenarios or explain your approach to statistical problems. This round may also include questions about A/B testing and experimental design.

3. Onsite Interviews

Candidates who perform well in the phone screen are usually invited for an onsite interview, which can be quite extensive, often lasting several hours. This stage typically includes multiple one-on-one interviews with various team members, covering both technical and behavioral aspects. Interviewers may delve into case studies, requiring candidates to demonstrate their analytical thinking and data interpretation skills. Additionally, there may be a practical exercise where candidates are asked to conduct a data analysis using their preferred tools or programming languages.

4. Final Discussions and Feedback

Following the onsite interviews, candidates can expect a prompt response from the Coursera team regarding their application status. The interviewers are known for being supportive and communicative, providing feedback on performance and next steps in the hiring process. This final stage may also involve discussions about team dynamics and cultural fit, ensuring that candidates align with Coursera's values and mission.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may arise during each stage of the process.

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!