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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
Start
How to create key growth metrics
Start
How to come up with new feature ideas?
Start
How to estimate user LifeTime Value?
Start
User Similarity
Start
Design metrics for customer service
Start
Product Development - Should we test this new feature?
Start
Metrics - Understanding what drives them
Start
Metrics - How to find bugs
Start
How to predict user behavior?
Start
Metrics - How to create an effective metric
Start
Metrics - Self-selection bias
Start
Metrics - Trade-off between short term vs long term effects
Start
How to improve revenue from ads
Start
Metrics - How to create an effective metric for gaming apps
Personalization
Available in
days
days after you enroll
Start
Personalization - Full project
Start
Personalization from a probabilistic standpoint
Unbalanced Classes
Available in
days
days after you enroll
Start
Cut-off point optimization
Start
Changing class weights
Start
Exercise
Missing data
Available in
days
days after you enroll
Start
Missing Data - Full Project
Start
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
Start
Network Effect
Start
Sample Size
Start
Test first version of a data product
Start
Statistically sound A/B test
Start
When to rerun the same test again
Start
When not to run an A/B test
New Product Case Studies - Miscellaneous
Available in
days
days after you enroll
Preview
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
Start
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?
Start
INSIGHTS: Categorical variables in regressions. Better one-hot-encoding or multiple regressions?
Start
INSIGHTS: How would you make Airbnb booking process smoother?
Start
INSIGHTS: Can a bug actually improve your metric? What to do if that happens?
Start
INSIGHTS: How to improve a marketplace?
Start
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?
Start
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?
Start
A/B TESTING: What to do if after a test some metrics are up and some down?
Start
A/B TESTING: How to test different prices?
Start
A/B TESTING: When would you design an A/B test as a sequence of multiple tests?
Start
A/B TESTING: How can you halve the width of a t-test confidence interval?
Start
A/B TESTING: How to check if a metric is actually up after a successful test?
Start
A/B TESTING: How to avoid the A/B test issue of over-optimizing for the current user base?
Start
A/B TESTING: When would you increase a test significance level?
Start
METRICS: Conversion rate down, but absolute number of conversions up. Is it good or bad?
Preview
METRICS: How choosing an average-based metric vs a percentile one would impact product development?
Start
METRICS: When to run a test on multiple metrics?
Start
METRICS: When is accuracy a bad metric for a model?
Start
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
Start
Common Issues in A/B testing from Wallmart Labs, Google
Start
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
Start
Solution: Subscription Retention Rate
Start
INSIGHTS: Sessionize user activity
Start
Solution: Sessionize user activity
Preview
INSIGHTS: Video Sharing Analysis
Start
Solution: Video Sharing Analysis
Start
METRICS: Ads Analysis
Start
Solution: Ads Analysis
Start
METRICS: Hotel Search Data
Start
Solution: Hotel Search Data
Preview
A/B Testing: User Referral Program
Start
Solution: User Referral Program
Start
A/B TESTING: Pricing Test
Start
Solution: Pricing Test
Start
ML: Applying for a loan
Start
Solution: Applying for a loan
Preview
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
New Coding Course
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
Start
Metrics: Identify power users based on their history
Start
SOLUTION: Identify power users based on their history
Start
Metrics: Estimate total and running values
Start
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
or pay in installments:
2 payments of $1,625/month
Single Payment
Coupon Discount
or pay in installments:
$3,250
Enroll