Course curriculum

  • 1

    Guide

    • Introduction

    • What is a data science case problem?

    • Responding to a case effectively

    • How does an interviewer evaluate a case response?

    • Preparation strategy

    • Lyft case example
  • 2

    Technical Case Frameworks

    • [Applied Statistics] Survey Weighting

    • [Marketing] Attribution Modeling

    • [Marketing] Marketing Mix Modeling - Part 1

    • [Marketing] Marketing Mix Modeling - Part 2

    • [Machine Learning] Regression Modeling Framework

    • [Marketing] Measuring Marketing Performance
  • 3

    Product Cases - Applied Statistics

    • [Meta] Distribution of Comments

    • [Meta] Distribution of False Positives

    • [Amazon] Evaluate Variance

    • [Meta] Revenue Analytics on Facebook Games

    • [Meta] Parents on Facebook
  • 4

    Product Cases - Metrics

    • [Google] Search Metric Shifts

    • [Instacart] Measure Active Users

    • [Meta] Measuring Spam on Facebook

    • [Meta] P-Value > Significance Level (Alpha)

    • [Meta] Facebook Groups

    • [Meta] Decreasing User Retention After Sign-Ups

    • [Meta] Measuring Performance of News Feed

    • [Instagram] Measure Success on Stories

    • [Netflix] Long-Term Metric

    • [Netflix] Improve Conversions

    • [LinkedIn] Estimating Business Travelers

    • [LinkedIn] Engagement Increased, but Revenue Decreased

    • [Uber] Rideshare Engagement

    • [Lyft] Measuring Driver Satisfaction
  • 5

    Product Cases - Experimentation

    • [Amazon] AB Testing Multiple Changes

    • [Amazon] Change Declined After Launch

    • [LinkedIn] Premium Subscription Funnel

    • [LinkedIn] Should LinkedIn Have Videos?

    • [Uber Eats] Measure Long-Term Effects

    • [Google] How would you improve Google Maps?

    • [Google] Peeking at the P-Value

    • [Instacart] Statistical Parameters in AB Testing

    • [Meta] How would you improve Facebook Messenger?

    • [Netflix] Video Preview

    • [Uber] Incentivizing Inactive Users on Uber Eats

    • [Hotel Tonight] Metric Change
  • 6

    Product Cases - Modeling

    • [Google] Forecast Airline Ticket Prices

    • [Amazon] Estimate Customer Lifetime Value

    • [Amazon] Sales Forecasting

    • [Amazon] Detect Fraudulent Users

    • [Meta] Optimize Cold Sales Calls

    • [Meta] Fraud Features

    • [LinkedIn] Optimize Email Campaigns

    • [LinkedIn] Salary Estimation

    • [Apple] Meaningful Customer Segmentation

    • [Redfin] Pricing Optimization
  • 7

    Product Cases - Opportunity Sizing

    • [Instacart] Free Delivery Opportunity Sizing

    • [Meta] Portal Launch Opportunity Sizing
  • 8

    Marketing Cases

    • [LinkedIn] Email Conversions

    • [Uber] Measure the Impact of Super Bowl Ads

    • [CVS] Marketing Outreach Experiment

    • [Apple] Marketing Mix Modeling on iPhone Sales
  • 9

    Consulting Cases

    • [McKinsey] Employee Attrition

    • [AWS] Propensity Modeling

    • [BCG Gamma] Customer Satisfaction Survey