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Course Curriculum
Product Sense via Machine Learning
Available in
days
days after you enroll
Start
Intro to Insights
Start
Regressions
Start
Decision Trees
Start
Partial Dependence Plots - Theory
Start
Partial Dependence Plots - Practice
Start
Rulefit - Theory
Start
Rulefit - Practice
Start
Parting Notes
Start
Exercise
Product and Metrics - Case Studies
Available in
days
days after you enroll
Start
How to improve a given metric
Start
Marketplace - Focusing on supply or demand?
Start
Actionable insights vs spurious relationships
Start
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?
Start
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
Personalization
Available in
days
days after you enroll
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Personalization - Full project
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Personalization from a probabilistic standpoint
Unbalanced Classes
Available in
days
days after you enroll
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Cut-off point optimization
Start
Changing class weights
Start
Exercise
Missing data
Available in
days
days after you enroll
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Missing Data - Full Project
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Missing Data - Uber example
Fraud and Machine Learning - Case Studies
Available in
days
days after you enroll
Start
Fake Listings
Start
Random Forest - Key Params
Start
False Positives vs False Negatives
Start
Training/Test set split
Start
Fraud - Two step authentication
Start
Fraud - Fake Profiles
Start
Supervised vs Unsupervised ML
A/B Testing - Practice
Available in
days
days after you enroll
Start
Overview
Start
Sample Size Estimation
Start
Randomization
Start
Randomization - Exercise
Start
Novelty Effect
Start
General Guidelines
A/B testing - Case Studies
Available in
days
days after you enroll
Start
Test by Market
Start
Opportunity costs
Start
Testing long term metrics
Start
Novelty Effect
Start
Test by market - Drawbacks
Start
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
Start
Statistically sound A/B test
Start
When to rerun the same test again
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When not to run an A/B test
New Product Case Studies - Miscellaneous
Available in
days
days after you enroll
Start
INSIGHTS: Should users be asked for their CC info when starting a free trial?
Start
INSIGHTS: How much would you charge for a new product?
Start
INSIGHTS: What was the data-driven hypothesis behind testing FB stories?
Start
INSIGHTS: Trade-off between revenue and user experience metrics
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INSIGHTS: What did you do when you got unexpected results?
Start
INSIGHTS: How would you place ads on a page?
Start
INSIGHTS: Would you test a feature allowing users to easily switch account?
Start
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?
Start
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?
Start
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/)?
Start
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?
Start
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?
Start
METRICS: What kind of analysis is required after a product-related bug?
Start
METRICS: In most metrics, you expect the average to be larger or smaller than the median?
Projects with Solutions
Available in
days
days after you enroll
Start
Project: How to improve conversion rate
Start
Project: Predicting fraud
Start
Project: Predicting employee retention
Collection of tech company blog posts/case studies
Available in
days
days after you enroll
Start
A/B testing overview from Netflix, Scribd, MS
Start
Theory behind A/B test from Google, StitchFix, Stackoverflow
Start
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
Data Challenges with Solutions
Available in
days
days after you enroll
Start
Introduction
Start
INSIGHTS: Workplace Diversity Analysis
Start
Solution: Workplace Diversity Analysis
Start
INSIGHTS: Funnel Analysis
Start
Solution: Funnel Analysis
Start
INSIGHTS: Subscription Retention Rate
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Solution: Subscription Retention Rate
Start
INSIGHTS: Sessionize user activity
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Solution: Sessionize user activity
Start
INSIGHTS: Video Sharing Analysis
Start
Solution: Video Sharing Analysis
Start
METRICS: Ads Analysis
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Solution: Ads Analysis
Start
METRICS: Hotel Search Data
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Solution: Hotel Search Data
Start
A/B Testing: User Referral Program
Start
Solution: User Referral Program
Start
A/B TESTING: Pricing Test
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Solution: Pricing Test
Start
ML: Applying for a loan
Start
Solution: Applying for a loan
Start
ML: Optimization of Employee Shuttle Stops
Start
Solution: Optimization of Employee Shuttle Stops
Start
ML: Clustering Grocery Items
Start
Solution: Clustering Grocery Items
Start
ML: Credit Card Transactions
Start
Solution: Credit Card Transactions
Start
ML: Song Recommendation
Start
Solution: Song Recommendation
Metrics - SQL Exercises
Available in
days
days after you enroll
Start
Introduction
Start
Metrics: Time delta between consecutive events
Start
SOLUTION: Time delta between consecutive events
Start
Metrics: Segment by mobile, web, and cross-device users
Start
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
Start
Metrics: Summary stats - Custom implementation of the median vs average
Start
SOLUTION: Summary stats - Custom implementation of the median vs average
Start
Metrics : Rank users within groups
Start
SOLUTION: Rank users within groups
Get started now!
2 Monthly Installments
Coupon Discount
2 payments of $1,625/month
Single Payment
Coupon Discount
$3,250
Enroll