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Data Science Interview Prep

Data Scientist Interviews Introduction

Welcome to Exponent’s Data Science Interview Course!Types of Data Science Roles The Data Science Interview Loop

Statistics and Experimentation Questions

Introduction to Statistics and Experimentation Questions Rubric for Statistics and Experimentation Questions

How to Answer Data Preprocessing and Quality Questions Descriptive Statistics Data Cleaning Data Transformation Sampling Bias Handling Outliers Normalization vs. Standardization

Introduction to Probability Questions How to Answer Probability Questions Probability Concepts What is a P-value?Calculate Expected Cost of Coupon Usage Calculate Conditional Probability (Bayes Theorem)Calculate Probability of a Fair Coin

Introduction to Regression Questions Linear Regression Concepts Logistic Regression Concepts How to Answer Regression Questions Select Input for Modeling When Handling Multicollinearity Gradient Descent and Model Optimization Advantages and Limitations of Linear Regression Modeling Linear Regression R-Squared, Multicollinearity, and Statistical Tests

Introduction to Hypothesis Tests and Confidence Intervals Questions How to Answer Hypothesis Test Questions How to Calculate Confidence Intervals Define Confidence Level in Confidence Interval

Introduction to Power Analysis and Impact Sizing How to Answer Impact Sizing Questions How to Answer Power Analysis Questions Determine Sample Size Calculate Revenue from New Vertical Expansion

Introduction to Experimentation Questions How to Answer A/B Testing Questions Alternatives to A/B Testing Design Campaign Experiment and Success Metrics Design A/B test for New Campaign Design A/B Testing Alternatives Design Test for Stories Reaction Feature Design Test for Driver Matching Algorithm Increase Sales through A/B Testing Adjust P-value Threshold Faster A/B Testing and Statistical Significance A/B Testing Email Campaign Analyze A/B Test Results for Checkout T-test Assumptions and Alternatives

Standard Deviation vs Range Power vs. Confidence Level Expectation of Variance Checking Random Assignment in A/B Testing What is A/B testing Statistical Background behind Power Type I vs. Type II Error Multiple t-tests Covariance vs. Correlation Common Pitfalls of A/B Testing Explain Confidence Interval Central Limit Theorem and its Usefulness Z and T-tests Hypothesis Testing and p-values Hypothesis Testing for Coin Flip Hypothesis Testing for Amazon Distribution of Daily Minutes on Facebook Search Distribution Correlation and Outliers False Positive Distribution Biased vs Unbiased Estimator Statistical Significance vs Practical Significant Sampling Distribution in Inferential Statistics Novelty and Primary Effects in Experiments Non-Normal Distribution Network Effects in A/B Testing What is A/B testing and when should it be used?Verifying Random A/B Testing Groups

Data Communications Questions

How to Present Past Projects Present a Past Project 1 Present a Past Project 2 Present a Past Project 3

Introduction to the Take-home Assignment How to Create a Take-home for Defined Tasks How to Create a Take-home for Open-ended Tasks

Python Coding Questions for Data Scientists

Lowest Earning Employees

Unsold Products

Top Product Lines

Revenue by Customer City

Rank Salary by Department

Average Distance Between Cities

Overstretched Employees

Time Between Two Events

Improving Students

Total Outfit Combinations

Climbing Stairs

Biased Coin Flip Histogram

Predict Results from a Fair Coin

Handle Missing Data

Interpolate Data

Convert Biased Coin to Fair Coin

Find Statistical Evidence for Conversion Rate

Session Data Analysis

SQL Interviews

Introduction to SQL and Its History How to Answer SQL Interview Questions Relationships and Relational Database Concepts

How to Prep SQL Interviews Fast

SQL Interview Patterns

SQL Interview Test Questions

Joins & Duplicate Control

Window Functions Essentials

Grouping, Having, Conditional Aggregation & NULL-Safe Metrics

Subqueries & CTEs

Dates & Bucketing

How to Perform in a SQL Interview

Basic SQL Syntax The WHERE Clause Logical operators: AND, OR, NOT Finding similar results with LIKE and Wildcards Querying Missing Values with IS NULL and IS NOT NULL Sorting data with ORDER BY Using LIMIT and OFFSET IN and BETWEEN

Introduction to SQL Aggregations GROUP BY and HAVING Counting with COUNT and COUNT(DISTINCT)SUM Calculating Average, Min, and Max with SQL Conditional values with CASE WHEN ... ELSE Working with Date and Time: DATE_TRUNC, DATEDIFF, and more

Joins Inner Joins LEFT and RIGHT Joins Full Outer Joins Unions Cross Joins Subqueries and Derived tables Common Table Expressions (CTEs)

Introduction to Window Functions Window functions: RANK and DENSE RANK Window functions: ROW_NUMBER Window functions: LAG

Introduction to SQL Practice Questions Top Earning Employees

Monthly Post Success Analysis

High Volume Low Success

Marketing Campaign Duration

Find Average Purchase Value

Survey Sampling

Lyft Ride Requests

E-commerce: Units Ordered Yesterday

E-commerce: Units Ordered Last Week

E-commerce: Earliest Order by Customer

Total Transaction Volume

Top Salaries by Department

Employee Hierarchy

Post Success After Failure

Find Top Customer by Year

Find Monthly Revenue Growth

Initial Contact Attribution

Ranking Salary Deviations

Game Leaderboard

Amazon Order Status

Duolingo Leaderboards

Validate Bitcoin Transactions

E-commerce: Second Earliest Order

SQL Stored Procedures

Product Sense and Case Studies

Introduction to Analytical Questions How to Answer A/B Testing Questions How to Answer Metrics Questions Rubric for Analytical Interviews Pick YouTube's Key Metrics A/B Test Google's Homepage Build Uber's Passenger Pickup Instagram Reels Success Metrics LinkedIn Events Success Metrics A/B Test Google Maps Measure Success for Slack Connect Measure Success for Instagram Discovery A/B Testing Email Campaign Improving Facebook’s DAU Diagnosing Instagram DAU Drop More Analytical Question Practice

Introduction to Product Design Questions How to Answer Product Design Questions

Design Facebook Movies Design Audio Product for Meta

Introduction to the Case Study Interview How to Answer Case Study Interview Questions Investigate Sudden Viewer Drop at Meta Integrate Data in Netflix License Renewal Balance Ads vs Follower Instagram Posts Improve Snap Camera Speed Instagram Messaging with 3rd Party Facebook Video for Individuals with Hearing Disabilities Evaluating Facebook’s Emotion Scale Identifying Conversations in Comments Measuring Product-Market Fit for Meta Video

ML Coding Questions for Data Scientists

Introduction to ML Coding Interviews How to Answer ML Coding Interview Questions Rubric for ML Coding Interviews

Implement the KNN Algorithm

Implement K-Means Clustering

Optimal Value of K in K-Means Implement a 2D Convolutional Filter

Predict User App Deletion

Predict Harmful Text

Split Dataset for Training, Evaluation, Testing

ML Concepts Questions for Data Scientists

Introduction to ML Concepts Interviews How to Answer ML Data Handling Questions How to Answer ML Model Questions How to Answer ML Evaluation Questions How to Answer ML Production Questions Rubric for ML Concepts Interviews ML Interviews Glossary

Linear Regression Logistic Regression Decision Trees Linear SVM K Nearest Neighbors Neural Network K-Means Clustering Density-Based Spatial Clustering (DBSCAN)Supervised Model Evaluation

Describe Linear Regression Explain the Bias-Variance Tradeoff Explain “Training" and “Testing” Data Discuss Batch, Mini-Batch, Stochastic Gradient Descent Explain Feature Scaling and Normalization Explain Classification vs Regression Identify When Model Needs Refresh Handle an Exploding Gradient Build Fraud Detection Model Describe How Decision Trees are Created Measure Algorithm Success Compare Forecast Models to Other ML Models Build Recommendation System for Online Courses

Unlock full course

Data Science Interview Prep

Data Scientist Interviews Introduction

Welcome to Exponent’s Data Science Interview Course!Types of Data Science Roles The Data Science Interview Loop

Statistics and Experimentation Questions

Introduction to Statistics and Experimentation Questions Rubric for Statistics and Experimentation Questions

How to Answer Data Preprocessing and Quality Questions Descriptive Statistics Data Cleaning Data Transformation Sampling Bias Handling Outliers Normalization vs. Standardization

Introduction to Probability Questions How to Answer Probability Questions Probability Concepts What is a P-value?Calculate Expected Cost of Coupon Usage Calculate Conditional Probability (Bayes Theorem)Calculate Probability of a Fair Coin

Introduction to Regression Questions Linear Regression Concepts Logistic Regression Concepts How to Answer Regression Questions Select Input for Modeling When Handling Multicollinearity Gradient Descent and Model Optimization Advantages and Limitations of Linear Regression Modeling Linear Regression R-Squared, Multicollinearity, and Statistical Tests

Introduction to Hypothesis Tests and Confidence Intervals Questions How to Answer Hypothesis Test Questions How to Calculate Confidence Intervals Define Confidence Level in Confidence Interval

Introduction to Power Analysis and Impact Sizing How to Answer Impact Sizing Questions How to Answer Power Analysis Questions Determine Sample Size Calculate Revenue from New Vertical Expansion

Introduction to Experimentation Questions How to Answer A/B Testing Questions Alternatives to A/B Testing Design Campaign Experiment and Success Metrics Design A/B test for New Campaign Design A/B Testing Alternatives Design Test for Stories Reaction Feature Design Test for Driver Matching Algorithm Increase Sales through A/B Testing Adjust P-value Threshold Faster A/B Testing and Statistical Significance A/B Testing Email Campaign Analyze A/B Test Results for Checkout T-test Assumptions and Alternatives

Standard Deviation vs Range Power vs. Confidence Level Expectation of Variance Checking Random Assignment in A/B Testing What is A/B testing Statistical Background behind Power Type I vs. Type II Error Multiple t-tests Covariance vs. Correlation Common Pitfalls of A/B Testing Explain Confidence Interval Central Limit Theorem and its Usefulness Z and T-tests Hypothesis Testing and p-values Hypothesis Testing for Coin Flip Hypothesis Testing for Amazon Distribution of Daily Minutes on Facebook Search Distribution Correlation and Outliers False Positive Distribution Biased vs Unbiased Estimator Statistical Significance vs Practical Significant Sampling Distribution in Inferential Statistics Novelty and Primary Effects in Experiments Non-Normal Distribution Network Effects in A/B Testing What is A/B testing and when should it be used?Verifying Random A/B Testing Groups

Data Communications Questions

How to Present Past Projects Present a Past Project 1 Present a Past Project 2 Present a Past Project 3

Introduction to the Take-home Assignment How to Create a Take-home for Defined Tasks How to Create a Take-home for Open-ended Tasks

Python Coding Questions for Data Scientists

Lowest Earning Employees

Unsold Products

Top Product Lines

Revenue by Customer City

Rank Salary by Department

Average Distance Between Cities

Overstretched Employees

Time Between Two Events

Improving Students

Total Outfit Combinations

Climbing Stairs

Biased Coin Flip Histogram

Predict Results from a Fair Coin

Handle Missing Data

Interpolate Data

Convert Biased Coin to Fair Coin

Find Statistical Evidence for Conversion Rate

Session Data Analysis

SQL Interviews

Introduction to SQL and Its History How to Answer SQL Interview Questions Relationships and Relational Database Concepts

How to Prep SQL Interviews Fast

SQL Interview Patterns

SQL Interview Test Questions

Joins & Duplicate Control

Window Functions Essentials

Grouping, Having, Conditional Aggregation & NULL-Safe Metrics

Subqueries & CTEs

Dates & Bucketing

How to Perform in a SQL Interview

Basic SQL Syntax The WHERE Clause Logical operators: AND, OR, NOT Finding similar results with LIKE and Wildcards Querying Missing Values with IS NULL and IS NOT NULL Sorting data with ORDER BY Using LIMIT and OFFSET IN and BETWEEN

Introduction to SQL Aggregations GROUP BY and HAVING Counting with COUNT and COUNT(DISTINCT)SUM Calculating Average, Min, and Max with SQL Conditional values with CASE WHEN ... ELSE Working with Date and Time: DATE_TRUNC, DATEDIFF, and more

Joins Inner Joins LEFT and RIGHT Joins Full Outer Joins Unions Cross Joins Subqueries and Derived tables Common Table Expressions (CTEs)

Introduction to Window Functions Window functions: RANK and DENSE RANK Window functions: ROW_NUMBER Window functions: LAG

Introduction to SQL Practice Questions Top Earning Employees

Monthly Post Success Analysis

High Volume Low Success

Marketing Campaign Duration

Find Average Purchase Value

Survey Sampling

Lyft Ride Requests

E-commerce: Units Ordered Yesterday

E-commerce: Units Ordered Last Week

E-commerce: Earliest Order by Customer

Total Transaction Volume

Top Salaries by Department

Employee Hierarchy

Post Success After Failure

Find Top Customer by Year

Find Monthly Revenue Growth

Initial Contact Attribution

Ranking Salary Deviations

Game Leaderboard

Amazon Order Status

Duolingo Leaderboards

Validate Bitcoin Transactions

E-commerce: Second Earliest Order

SQL Stored Procedures

Product Sense and Case Studies

Introduction to Analytical Questions How to Answer A/B Testing Questions How to Answer Metrics Questions Rubric for Analytical Interviews Pick YouTube's Key Metrics A/B Test Google's Homepage Build Uber's Passenger Pickup Instagram Reels Success Metrics LinkedIn Events Success Metrics A/B Test Google Maps Measure Success for Slack Connect Measure Success for Instagram Discovery A/B Testing Email Campaign Improving Facebook’s DAU Diagnosing Instagram DAU Drop More Analytical Question Practice

Introduction to Product Design Questions How to Answer Product Design Questions

Design Facebook Movies Design Audio Product for Meta

Introduction to the Case Study Interview How to Answer Case Study Interview Questions Investigate Sudden Viewer Drop at Meta Integrate Data in Netflix License Renewal Balance Ads vs Follower Instagram Posts Improve Snap Camera Speed Instagram Messaging with 3rd Party Facebook Video for Individuals with Hearing Disabilities Evaluating Facebook’s Emotion Scale Identifying Conversations in Comments Measuring Product-Market Fit for Meta Video

ML Coding Questions for Data Scientists

Introduction to ML Coding Interviews How to Answer ML Coding Interview Questions Rubric for ML Coding Interviews

Implement the KNN Algorithm

Implement K-Means Clustering

Optimal Value of K in K-Means Implement a 2D Convolutional Filter

Predict User App Deletion

Predict Harmful Text

Split Dataset for Training, Evaluation, Testing

ML Concepts Questions for Data Scientists

Introduction to ML Concepts Interviews How to Answer ML Data Handling Questions How to Answer ML Model Questions How to Answer ML Evaluation Questions How to Answer ML Production Questions Rubric for ML Concepts Interviews ML Interviews Glossary

Linear Regression Logistic Regression Decision Trees Linear SVM K Nearest Neighbors Neural Network K-Means Clustering Density-Based Spatial Clustering (DBSCAN)Supervised Model Evaluation

Describe Linear Regression Explain the Bias-Variance Tradeoff Explain “Training" and “Testing” Data Discuss Batch, Mini-Batch, Stochastic Gradient Descent Explain Feature Scaling and Normalization Explain Classification vs Regression Identify When Model Needs Refresh Handle an Exploding Gradient Build Fraud Detection Model Describe How Decision Trees are Created Measure Algorithm Success Compare Forecast Models to Other ML Models Build Recommendation System for Online Courses

Unlock full course

Gradient Descent and Model Optimization

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In this mock interview, Ben answers the question, "How is gradient descent and model optimization used in linear regression?"

Ben answers the question in three parts:

What is gradient descent?
How does gradient descent play a role in model optimization?
Are there other methods for optimizing a model for a specific metric?

For a deeper dive on gradient descent, check out this machine learning mock interview that explains batch, mini-batch, and stochastic gradient descent.