Example Curriculum
Section 1 - Introduction
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PART A: A/B Testing and Experimentation
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Section 2 - Hypothesis Testing for A/B Testing
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- Game Plan for Hypothesis Testing for A/B Testing (3:32)
- What is Hypothesis Testing? (6:14)
- CASE STUDY: FashionFiesta (Briefing) (2:02)
- Python: Hypothesis Testing Exercise (10:35)
- Confidence Level (5:04)
- P-value (5:05)
- Python: Build a P-value Function with ChatGPT (4:51)
- Two Sample T-Test (7:16)
- Python: Get to Know the Data with ChatGPT (8:16)
- Python: Levene's Test (5:33)
- Python: Two Sample T-Test (8:10)
- One-Tailed Test vs. Two-Tailed Test (5:27)
- Python: Get to Know the Data (4:55)
- Python: 1-Tailed Test (3:43)
- Chi-square Test (3:13)
- Python: Get to Know the Data (5:10)
- Python: Chi-square Test (4:21)
- CASE STUDY: Google 41 Shades of Blue (3:10)
Section 3 - Introduction to A/B Testing
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- Game Plan for Introduction to A/B Testing (3:31)
- CASE STUDY: Krushing Kingdoms (Briefing) (3:42)
- Python: Libraries and Data (8:05)
- Python: EDA with ChatGPT (12:21)
- Python: Cleaning Outliers with ChatGPT (10:43)
- A/B Testing Terminology and Parameters (6:04)
- Setting Up Your A/B Test for Success (7:59)
- Randomization Techniques for A/B Testing (12:21)
- Python: Simple Randomization (3:09)
- Python: Block Randomization (5:13)
- Python: Stratified Randomization (13:08)
- Python: Clustered Randomization (11:06)
- Determining Sample Size Using Power Analysis (5:57)
- Python: Sample Size Calculator for Proportions (11:04)
- Determining A/B Test Sample Sizes for Continuous Outcomes (5:34)
- Python: Sample Size Calculator for Continuous Variables (7:00)
- Python: What if We Don't Clean the Outliers? (3:38)
- Danger of a too High Sample Size (2:11)
- Type I and Type II Error (3:15)
- Hypothesis Testing for Proportions (4:12)
- Python: Sampling Based on Optimal Sample Size (5:24)
- Python: Preparing Analysis (4:33)
- Python: Retention Test Post-Analysis. (8:15)
- Python: What if We Don't Sample? (5:24)
- Python: A/B Test Post-analysis (5:17)
- What Did You Learn in This Section? (3:03)
- CASE STUDY: How A/B Testing Helped Obama Raise Millions (4:39)
Section 4 - Mastering A/B Testing
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- Game Plan for Intermediate A/B Testing (3:57)
- CASE STUDY: Amazon's Buy Button (Briefing) (2:15)
- Python: Kick off (5:00)
- Python: EDA with ChatGPT (13:35)
- Bayesian A/B Testing (4:39)
- Python: Bayesian AB Testing Setup (5:53)
- Bayesian Statistics (8:18)
- Python: Bayesian A/B Testing with TensorFlow (14:06)
- Python: Proportions Test with ChatGPT (10:37)
- Sequential Testing and Early Stopping (4:37)
- Python: Sequential Testing and Early Stopping (14:30)
- CASE STUDY: Netflix's Wednesday Thumbnails (Briefing) (1:20)
- A/B/C Test (3:56)
- Python: EDA with ChatGPT (10:24)
- Python: Chi-square Test with ChatGPT (5:14)
- Bonferroni Method (2:25)
- Python: Bonferroni Method (11:11)
- ANOVA Test and Hukey's HSD Test (6:17)
- Python: ANOVA Test (4:58)
- Python: Hukey's HSD Test (6:01)
- Limitations and Misinterpretations in A/B Tests (5:13)
- What Did You Learn in This Section? (3:25)
- CASE STUDY: The Ethical Considerations of Testing - AI in Recruitment (2:52)
PART B: ECONOMETRICS & CAUSAL INFERENCE
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Section 5 - Google Causal Impact (Econometrics and Causal Inference)
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- Why Econometrics and Causal Inference (4:20)
- Google Causal Impact - Game Plan (1:25)
- Time Series Data (1:27)
- CASE STUDY: Bitcoin and Paypal (2:16)
- Difference-in-Differences Framework (2:58)
- Causal Impact Step-by-Step Guide (1:59)
- Python - Installing Packages and Libraries (3:22)
- Python - Defining Dates (3:58)
- Python - Loading Bitcoin Data (4:22)
- Assumptions Needed (3:33)
- Python - Loading More Data (3:49)
- Python - Data Preparation (4:38)
- Python - Preparing for Correlation Matrix (1:58)
- Correlation Recap and Stationarity (3:56)
- Python - Stationarity Test (7:24)
- Python - Correlation Matrix and Heatmap (8:21)
- Python - Google Causal Impact Setup (2:12)
- Python - Google Causal Impact (2:53)
- Interpretation of Results (4:10)
- Python - Causal Impact Results (4:49)
- CHALLENGE: Introduction (5:48)
- CHALLENGE: Solutions (19:32)
- EXERCISE: Imposter Syndrome (2:55)
Section 6 - Matching
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- Matching - Game Plan (2:45)
- What is Matching? (3:18)
- CASE STUDY: Catholic Schools & Standardized Tests (Briefing) (1:40)
- Python - Directory and Libraries (3:16)
- Python - Loading Data (2:28)
- Unconfoundedness (2:50)
- Python - Comparing Means (3:04)
- Python - T-Test (4:16)
- Python - T-Test Loop (6:04)
- Python - Chi-square Test (3:18)
- Python - Chi-square Loop (3:53)
- The Curse of Dimensionality (1:52)
- Python - Transforming Race Variable (8:22)
- Python - Transforming Education Variable (5:02)
- Python - Cleaning and Preparing Dataset (2:56)
- Common Support Region (4:30)
- Python - Logistic Regression for Common Support Region (4:20)
- Python - Visualizing Common Support Region (7:19)
- Python - Matching (6:29)
- Matching Robustness Check (1:55)
- Python - Repeated Experiment (8:31)
- Python - Removing 1 Confounder (2:34)
- CHALLENGE: Introduction (5:25)
- CHALLENGE: Solutions (14:03)
- My Experience with Matching (2:41)
PART C: DATA VISUALIZATION
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Section 7 - Distribution Charts and Plotting Basics
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- Game Plan for Distribution Charts (2:44)
- Histogram (2:57)
- CASE STUDY: Pokemon Master (Briefing) (1:46)
- Python: Libraries and Data (4:10)
- Python: Defining Chart Size (2:41)
- Python: Basic Histogram (3:17)
- Python: Customizing Histogram (4:16)
- Python: Adding Vertical Lines (3:31)
- Python: Adding Horizontal Lines (3:33)
- Box Plot (4:14)
- Python: Basic Box Plot (4:44)
- Python: Customizing Box Plot (6:15)
- Python: Defining Order of the Plot (5:01)
- Python: Swarmplot (6:29)
- Violin Plot (3:23)
- Python: Basic Violin Plot (5:34)
- Python: Customize Violin Plot (10:24)
- Ridgeline (3:06)
- Python: Prepare Data for Ridgeline (8:05)
- Python: Prepare Chart for Ridgeline (12:04)
- Python: Customize Layout (5:07)
- Python: HTML Export and Building a Cheat Sheet (5:15)
- Colours (7:47)
- What Did You Learn In This Section? (3:18)
Section 8 - Ranking Charts
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- Game Plan for Ranking Charts (2:58)
- Bar Charts (3:41)
- CASE STUDY: World Happiness (Briefing) (2:24)
- Python: Libraries and Data (3:40)
- Python: Horizontal Bar Chart (5:16)
- Python: Customizing Bar Chart (5:28)
- Python: Vertical Bar Chart (3:59)
- Python: Highlight a Bar (2:19)
- Lollipop (4:45)
- Python: Lollipop Chart (4:04)
- Python: Customizing a Lollipop Chart (3:11)
- Spider Chart (3:43)
- Python: Spider Chart Preparation (9:15)
- Python: Basic Spider Chart (5:56)
- Python: Customizing Spider Chart (10:51)
- "The Visual Display of Quantitative Information" by Edward R. Tufte (3:10)
- What Did You Learn In This Section? (2:38)
PART D: SEGMENTATION
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Section 9 - RFM (Recency, Frequency, Monetary) Analysis
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- Game Plan for RFM (0:45)
- Value Based Segmentation (2:52)
- RFM Model (4:53)
- CASE STUDY: Online Shopping (Briefing) (0:53)
- Python: Directory and Libraries (2:17)
- Python: Loading Data (2:29)
- Python: Creating Sales Variable (1:45)
- Python: Date Variable (3:33)
- Python: Customer Level Aggregation (3:49)
- Python: Monetary Variable (1:23)
- Python: Tidying up Dataframe (2:52)
- Python: Quartiles (6:34)
- Python: RFM Score (1:51)
- Python: RFM Function (4:41)
- Python: Applying RFM Function (2:09)
- Python: Results Summary (4:29)
- CHALLENGE: Introduction (3:31)
- CHALLENGE: Solutions (12:16)
Section 10 - Gaussian Mixture
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- Game Plan for Gaussian Mixture (1:10)
- Clustering (2:09)
- Gaussian Mixture Model (3:57)
- CASE STUDY: Credit Cards #1 (Briefing) (0:53)
- Python: Directory and Data (2:11)
- Python: Load Data (1:50)
- Python: Transform Character Variables (1:21)
- AIC and BIC (2:15)
- Python: Optimal Number of Clusters (6:24)
- Python: Gaussian Mixture Model (1:11)
- Python: Cluster Prediction and Assignment (2:50)
- Python: Interpretation (7:46)
- CHALLENGE: Introduction (4:35)
- CHALLENGE: Solutions (18:04)
- My Experience with Segmentation (3:15)
PART E: PREDICTIVE ANALYTICS
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Section 11 - Random Forest
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- Game Plan for Random Forest (1:05)
- Ensemble Learning and Random Forest (2:16)
- How Decision Trees Work (4:19)
- CASE STUDY: Credit Cards #2 (Briefing) (0:37)
- Python: Directory and Libraries (2:02)
- Python: Loading Data (1:50)
- Python: Transform Object into Numerical Variables (1:43)
- Python: Summary Statistics (2:21)
- Random Forest Quirks (2:30)
- Python: Isolate X and Y (1:32)
- Python: Training and Test Set (3:40)
- Python: Random Forest Model (2:59)
- Python: Predictions (1:18)
- Python: Classification Report and F1 score (3:44)
- Python: Feature Importance (4:22)
- Parameter Tuning (2:45)
- Python: Parameter Grid (3:14)
- Python: Parameter Tuning (7:10)
- CHALLENGE: Introduction (4:24)
- CHALLENGE: Solutions (Part 1) (8:29)
- CHALLENGE: Solutions (Part 2) (9:40)
Section 12 - (Facebook) Prophet
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- (Facebook) Prophet - Game Plan (1:41)
- Structural Time Series (2:37)
- (Facebook) Prophet (3:39)
- CASE STUDY: Wikipedia (Briefing) (0:59)
- Python: Directory and Libraries (2:47)
- Python: Loading and Inspecting the Data (4:50)
- Python: Formatting the Date Variable (3:15)
- Python: Renaming Variables (1:32)
- Dynamic Holidays (2:26)
- Python: Easter Holiday (4:35)
- Python: Black Friday Holidays (4:53)
- Python: Finishing Holiday Preparation (1:14)
- Training and Test Set in Time Series (1:55)
- Python: Training and Test Set (2:03)
- (Facebook) Prophet Model (2:24)
- Additive vs. Multiplicative Seasonality (2:19)
- Python: (Facebook) Prophet Model (5:52)
- Python: Regressor Coefficients (3:06)
- Python: Forecasting (6:44)
- Python: Event Assessment (6:48)
- Python: Accuracy Assessment (4:36)
- Python: Visualization (5:51)
- Cross-validation (1:15)
- Python: Cross-validation (6:02)
- Python: Cross-validation Results and Visualization (5:49)
- Parameters to Tune (1:54)
- Python: Parameter Grid (4:48)
- Python: Parameter Tuning (7:00)
- Python: Parameter Tuning Results (3:19)
- CHALLENGE: Introduction - Demand in NYC (2:02)
- CHALLENGE: Solutions (Part 1) (10:44)
- CHALLENGE: Solutions (Part 2) (15:27)
- CHALLENGE: Solutions (Part 3) (16:03)
- Forecasting at Uber (4:38)
Part F - Advanced Analytics
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Section 13 - Multivariate A/B Testing
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- Game Plan for Multivariate A/B Testing (1:47)
- CASE STUDY: Google's Homepage Experiment (Briefing) (0:59)
- Python: Libraries and Data (3:37)
- Python: EDA with ChatGPT (6:42)
- Multivariate A/B Testing (MVT) (4:00)
- Python: Full Factorial Setup (8:16)
- Python: Full Factorial Testing (9:29)
- Partial Factorial Deep Dive (3:57)
- Python: Partial Factoral Combinations (4:57)
- Python: ANOVA and Tukey's HSD Test (3:48)
- Python: Encoding Variables and Generate Combinations (8:53)
- Python: Regression Analysis Setup (2:27)
- Python: Regression Analysis (9:54)
- Python: Random Forest Model (3:29)
- Python: Random Forest Evaluation (3:22)
- Random Forest Parameter Tuning (2:25)
- Python: Parameter Tuning (8:55)
- Python: Parameter Tuning Best Model (4:09)
- Python: Inferring Untested Variants - Predicting (10:40)
- Python: Inferring Untested Variants - Comparing (13:43)
- Python: Inferring Untested Variants - Visualizing (7:06)
- What Did You Learn in This Section? (2:54)
- CASE STUDY: Coca Cola "Share a Coke" Campaign (5:06)
Where To Go From Here?
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ARCHIVED - AVAILABLE UNTIL MAY 1 2024: PART A: STATISTICS
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ARCHIVED - AVAILABLE UNTIL MAY 1 2024: Section 2 - Basic Statistics
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- Basic Statistics - Game Plan (1:06)
- Arithmetic Mean (1:56)
- CASE STUDY: Moneyball (Briefing) (0:58)
- Python - Directory, Libraries and Data (8:03)
- Python - Mean (9:16)
- EXERCISE: Python - Mean (2:20)
- Median and Mode (2:41)
- Python - Median (5:01)
- EXERCISE: Python - Median (2:57)
- Python - Mode (3:03)
- EXERCISE: Python - Mode (1:36)
- Correlation (4:16)
- Python - Correlation (8:41)
- EXERCISE: Python - Correlation (3:33)
- Standard Deviation (2:07)
- Python - Standard Deviation (2:23)
- EXERCISE: Python - Standard Deviation (1:04)
- CASE STUDY: Moneyball (3:56)
- Let's Have Some Fun (+ Free Resources)
ARCHIVED - AVAILABLE UNTIL MAY 1 2024: Section 3 - Intermediary Statistics
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- Intermediary Statistics - Game Plan (0:46)
- Normal Distribution (3:00)
- CASE STUDY: Wine Quality (Briefing) (2:22)
- Python - Preparing Script and Loading Data (5:00)
- Python - Normal Distribution Visualization (7:34)
- EXERCISE: Python - Normal Distribution (5:41)
- P-Value (5:33)
- Shapiro-Wilks Test (1:51)
- Python - Shapiro-Wilks Test (7:42)
- EXERCISE: Python - Shapiro-Wilks (2:49)
- Standard Error of the Mean (2:36)
- Python - Standard Error (4:24)
- EXERCISE: Python - Standard Error (2:10)
- Z-Score (2:40)
- Confidence Interval (5:48)
- Python - Confidence Interval (6:23)
- EXERCISE: Python - Confidence Interval (2:19)
- T-test (2:17)
- CASE STUDY: Remote Work Predictions (Briefing) (0:39)
- Python - T-test (10:20)
- EXERCISE: Python - T-test (5:22)
- Chi-square test (2:28)
- Python - Chi-square test (7:29)
- EXERCISE: Python - Chi-square (3:14)
- Powerposing and p-hacking (3:20)
- Unlimited Updates
ARCHIVED - AVAILABLE UNTIL MAY 1 2024: Section 4 - Linear Regression
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- Linear Regression - Game Plan (1:27)
- CASE STUDY: Diamonds (Briefing) (0:57)
- Linear Regression (5:11)
- Python - Preparing Script and Loading Data (4:36)
- Python - Isolate X and Y (1:47)
- Python - Adding Constant (2:43)
- Linear Regression Output (3:36)
- Python - Linear Regression Model and Summary (3:20)
- Python - Plotting Regression (4:23)
- Dummy Variable Trap (3:09)
- Python - Dummy Variable (3:35)
- EXERCISE: Python - Linear Regression (5:51)
- Course Check-In
ARCHIVED - AVAILABLE UNTIL MAY 1 2024: Section 5 - Multilinear Regression
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- Multilinear Regression - Game Plan (1:34)
- The Concept of Multilinear Regression (1:45)
- CASE STUDY: Professors' Salary (Briefing) (0:45)
- Python - Preparing Script and Loading Data (5:05)
- Python - Summary Statistics (2:59)
- Outliers (2:43)
- Python - Plotting Continuous Variables (4:54)
- Python - Correlation Matrix (2:51)
- Python - Categorical Variables (4:30)
- Python - For Loop (4:43)
- Python - Creating Dummy Variables (3:09)
- Python - Isolate X and Y (3:28)
- Python - Adding Constant (1:26)
- Under and Over Fitting (1:32)
- Training and Test Set (1:03)
- Python - Train and Test Split (2:42)
- Python - Multilinear Regression (5:01)
- Accuracy KPIs (Key Performance Indicators) (3:19)
- Python - Model Predictions (1:31)
- Python - Accuracy Assessment (5:36)
- CHALLENGE: Introduction (5:08)
- CHALLENGE: Solutions (17:37)
- Implement a New Life System
ARCHIVED - AVAILABLE UNTIL MAY 1 2024: Section 6 - Logistic Regression
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- Logistic Regression - Game Plan (1:13)
- CASE STUDY: Spam Emails (Briefing) (1:00)
- Logistic Regression (2:06)
- Python - Preparing Script and Loading Data (4:16)
- Python - Summary Statistics (3:19)
- Python - Histogram and Outlier Removal (7:02)
- Python - Correlation Matrix (2:32)
- Python - Transforming Dependent Variable (2:39)
- Python - Prepare X and Y (2:09)
- Python - Training and Test Set (2:42)
- How to Read Logistic Regression Coefficients (2:40)
- Python - Logistic Regression (2:19)
- Python - Function to Read Coefficients (8:30)
- Python - Predictions (3:06)
- Confusion Matrix (6:17)
- Python - Confusion Matrix (5:25)
- Python - Manual Accuracy Assessment (7:05)
- Python - Classification Report (2:45)
- CHALLENGE: Introduction (4:49)
- CHALLENGE: Solutions (13:39)