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Python for Business Data Analytics & Intelligence
Section 1 - Introduction
Python for Business Analytics & Intelligence (2:34)
Introduction (1:55)
Exercise: Meet Your Classmates and Instructor
Setting up the Course Material (9:40)
The Modern Day Business Analyst (5:00)
How-to's: Speed up videos, Downloading videos, Subtitles
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PART A: STATISTICS
What are Statistics and why are they important?
Section 2 - Basic Statistics
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
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
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
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)
Section 6 - Logistic Regression
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
What are Econometrics & Causal Inference and why are they important?
Section 7 - Google Causal Impact (Econometrics and Causal Inference)
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
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
What is Segmentation and why is it important?
Section 9 - RFM (Recency, Frequency, Monetary) Analysis
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
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
What are Predictive Analytics and why are they important?
Section 11 - Random Forest
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
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?
Thank You! (1:17)
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CHALLENGE: Solutions (Part 3)
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