Example Curriculum
Section 1 - Introduction
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PART A: STATISTICS
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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)
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)
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)
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 (15:59)
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)
PART B: ECONOMETRICS & CAUSAL INFERENCE
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Section 7 - Google Causal Impact (Econometrics and Causal Inference)
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- Why Econometrics and Causal Inference (4:20)
- Google Causal Impact - Game Plan (1:20)
- Time Series Data (1:30)
- CASE STUDY: Bitcoin Pricing (Briefing) (2:28)
- Difference-in-Differences Framework (2:21)
- Causal Impact Step-by-Step (2:20)
- Python - Installing and Importing Libraries (3:54)
- Python - Defining Dates (3:34)
- Python - Bitcoin Price loading (5:12)
- Assumptions (2:54)
- Python - Load Control Groups (3:59)
- Python - Preparing DataFrame (6:00)
- Python - Preparing for Correlation Matrix (2:42)
- Correlation Recap and Stationarity (4:16)
- Python - Stationarity (8:05)
- Python - Correlation (3:22)
- Python - Google Causal Impact Setup (2:41)
- Python - Google Causal Impact (3:23)
- Interpretation of Results (4:17)
- Python - Impact Results (5:04)
- CHALLENGE: Introduction (7:14)
- CHALLENGE: Solutions (13:13)
- EXERCISE: Imposter Syndrome (2:55)
Section 8 - Matching
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- Matching - Game Plan (2:50)
- Matching (2:51)
- CASE STUDY: Catholic Schools & Standardized Tests (Briefing) (1:00)
- Python - Directory and Libraries (2:53)
- Python - Loading Data (2:23)
- Unconfoundedness (2:16)
- Python - Comparing Means (2:42)
- Python - T-Test (4:09)
- Python - T-Test Loop (4:37)
- Python - Chi-square Test (3:27)
- Python - Chi-square Loop (4:26)
- Python - Other Variables (1:49)
- The Curse of Dimensionality (1:40)
- Python - Race Variable Transformation (6:59)
- Python - Education Variables (5:30)
- Python - Cleaning and Preparing Dataset (3:31)
- Common Support Region (4:04)
- Python - Logistic Regression and Debugging (7:22)
- Python - Preparing for Common Support Region (5:39)
- Python - Common Support Region Visualization (1:41)
- Python - Matching (4:51)
- Robustness Checks (2:13)
- Python - Robustness Check - Repeated experiments (7:00)
- Python - Outcome Visualization (1:55)
- Python - Robustness Check - Removing 1 confounder (3:38)
- CHALLENGE: Introduction (5:25)
- CHALLENGE: Solutions (14:03)
- My Experience with Matching (2:41)
PART C: SEGMENTATION
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Section 9 - RFM (Recency, Frequency, Monetary) Analysis
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- RFM - Game Plan (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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- Gaussian Mixture - Game Plan (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 D: PREDICTIVE ANALYTICS
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Section 11 - Random Forest
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- Random Forest - Game Plan (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 (1:20)
- Structural Time Series (2:25)
- Facebook Prophet (3:37)
- CASE STUDY: Wikipedia (Briefing) (0:51)
- Python - Directory and Libraries (2:05)
- Python - Loading Data (2:34)
- Python - Transforming Date Variable (2:48)
- Python - Renaming Variables (1:31)
- Dynamic Holidays (2:10)
- Python - Easter Holidays (5:16)
- Python - Black Friday (2:50)
- Python - Combining Events and Preparing Dataframe (2:33)
- Training and Test Set (2:12)
- Python - Training and Test Set (3:17)
- Facebook Prophet Parameters (2:13)
- Additive vs. Multiplicative Seasonality (2:37)
- Facebook Prophet Model (4:44)
- Python - Regressor Coefficients (1:49)
- Python - Future Dataframe (4:37)
- Python - Forecasting (2:19)
- Python - Accuracy Assessment (3:41)
- Python - Visualization (5:40)
- Cross-validation (1:07)
- Python - Cross-validation (7:59)
- Parameters to tune (1:22)
- Python - Parameter Grid (4:03)
- Python - Parameter Tuning (7:28)
- CHALLENGE: Introduction (4:47)
- CHALLENGE: Solutions (Part 1) (9:17)
- CHALLENGE: Solutions (Part 2) (11:07)
- CHALLENGE: Solutions (Part 3) (8:08)
- Forecasting at Uber (4:38)
Where To Go From Here?
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