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Course Curriculum

Product Sense via Machine Learning
Intro to Insights
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Regressions
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Decision Trees
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Partial Dependence Plots - Theory
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Partial Dependence Plots - Practice
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Rulefit - Theory
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Rulefit - Practice
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Parting Notes
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Exercise
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Product and Metrics - Case Studies
How to improve a given metric
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Marketplace - Focusing on supply or demand?
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Actionable insights vs spurious relationships
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Behavioral vs demographic variables
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How to create key growth metrics
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How to come up with new feature ideas?
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How to estimate user LifeTime Value?
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User Similarity
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Design metrics for customer service
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Product Development - Should we test this new feature?
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Metrics - Understanding what drives them
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Metrics - How to find bugs
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How to predict user behavior?
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Metrics - How to create an effective metric
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Metrics - Self-selection bias
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Metrics - Trade-off between short term vs long term effects
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How to improve revenue from ads
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Metrics - How to create an effective metric for gaming apps
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Personalization
Personalization - Full project
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Personalization from a probabilistic standpoint
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Unbalanced Classes
Cut-off point optimization
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Changing class weights
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Exercise
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Missing data
Missing Data - Full Project
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Missing Data - Uber example
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Fraud and Machine Learning - Case Studies
Fake Listings
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Random Forest - Key Params
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False Positives vs False Negatives
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Training/Test set split
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Fraud - Two step authentication
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Fraud - Fake Profiles
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Supervised vs Unsupervised ML
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A/B Testing - Practice
Overview
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Sample Size Estimation
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Randomization
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Randomization - Exercise
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Novelty Effect
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General Guidelines
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A/B testing - Case Studies
Test by Market
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Opportunity costs
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Testing long term metrics
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Novelty Effect
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Test by market - Drawbacks
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Multiple A/B tests at the same time
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Network Effect
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Sample Size
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Test first version of a data product
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Statistically sound A/B test
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When to rerun the same test again
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When not to run an A/B test
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New Product Case Studies - Miscellaneous
INSIGHTS: Should users be asked for their CC info when starting a free trial?
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INSIGHTS: How much would you charge for a new product?
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INSIGHTS: What was the data-driven hypothesis behind testing FB stories?
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INSIGHTS: Trade-off between revenue and user experience metrics
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INSIGHTS: What did you do when you got unexpected results?
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INSIGHTS: How would you place ads on a page?
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INSIGHTS: Would you test a feature allowing users to easily switch account?
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INSIGHTS: Would you merge two similar products or run them separately?
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INSIGHTS: Categorical variables in regressions. Better one-hot-encoding or multiple regressions?
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INSIGHTS: How would you make Airbnb booking process smoother?
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INSIGHTS: Can a bug actually improve your metric? What to do if that happens?
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INSIGHTS: How to improve a marketplace?
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A/B TESTING: Can you do a t-test on a dummy variable like conversion (0/1/)?
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A/B TESTING: How would you test the success of a new ad campaign?
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A/B TESTING: Define test statistical significance in layman’s terms. Why 0.05?
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A/B TESTING: What would you do if test p-value is slightly above signficance level?
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A/B TESTING: What to do if after a test some metrics are up and some down?
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A/B TESTING: How to test different prices?
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A/B TESTING: When would you design an A/B test as a sequence of multiple tests?
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A/B TESTING: How can you halve the width of a t-test confidence interval?
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A/B TESTING: How to check if a metric is actually up after a successful test?
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A/B TESTING: How to avoid the A/B test issue of over-optimizing for the current user base?
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A/B TESTING: When would you increase a test significance level?
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METRICS: Conversion rate down, but absolute number of conversions up. Is it good or bad?
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METRICS: How choosing an average-based metric vs a percentile one would impact product development?
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METRICS: When to run a test on multiple metrics?
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METRICS: When is accuracy a bad metric for a model?
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METRICS: What kind of analysis is required after a logging-related bug?
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METRICS: What kind of analysis is required after a product-related bug?
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METRICS: In most metrics, you expect the average to be larger or smaller than the median?
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Live Assistant App
App example with video recording
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Projects with Solutions
Project: How to improve conversion rate
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Project: Predicting fraud
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Project: Predicting employee retention
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Collection of tech company blog posts/case studies
A/B testing overview from Netflix, Scribd, MS
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Theory behind A/B test from Google, StitchFix, Stackoverflow
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A/B testing with connected users from Google, Instacart
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Common Issues in A/B testing from Wallmart Labs, Google
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Product Development from Airbnb, FB, Linkedin, Google, Netflix
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Data Challenges with Solutions
Introduction
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INSIGHTS: Workplace Diversity Analysis
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Solution: Workplace Diversity Analysis
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INSIGHTS: Funnel Analysis
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Solution: Funnel Analysis
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INSIGHTS: Subscription Retention Rate
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Solution: Subscription Retention Rate
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INSIGHTS: Sessionize user activity
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Solution: Sessionize user activity
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INSIGHTS: Video Sharing Analysis
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Solution: Video Sharing Analysis
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METRICS: Ads Analysis
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Solution: Ads Analysis
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METRICS: Hotel Search Data
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Solution: Hotel Search Data
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A/B Testing: User Referral Program
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Solution: User Referral Program
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A/B TESTING: Pricing Test
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Solution: Pricing Test
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ML: Applying for a loan
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Solution: Applying for a loan
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ML: Optimization of Employee Shuttle Stops
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Solution: Optimization of Employee Shuttle Stops
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ML: Clustering Grocery Items
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Solution: Clustering Grocery Items
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ML: Credit Card Transactions
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Solution: Credit Card Transactions
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ML: Song Recommendation
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Solution: Song Recommendation
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Metrics - SQL Exercises
New Coding Course
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Metrics: Time delta between consecutive events
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SOLUTION: Time delta between consecutive events
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Metrics: Segment by mobile, web, and cross-device users
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SOLUTION: Segment by mobile, web, and cross-device users
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Metrics: Identify power users based on their history
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SOLUTION: Identify power users based on their history
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Metrics: Estimate total and running values
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SOLUTION: Estimate total and running values
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Metrics: Summary stats - Custom implementation of the median vs average
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SOLUTION: Summary stats - Custom implementation of the median vs average
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Metrics : Rank users within groups
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SOLUTION: Rank users within groups
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