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Business Analytics Bootcamp (with Python): Zero to Mastery
Section 1 - Introduction
Business Analytics Bootcamp (with Python): Zero to Mastery (2:34)
Introduction (3:50)
Exercise: Meet Your Classmates and Instructor
Get The Course Materials
The Value of a Business Analyst (4:45)
Understanding Your Video Player (notes, video speed, subtitles + more)
Set Your Learning Streak Goal
PART A: A/B Testing and Experimentation
What Is A/B Testing And Why It Is Important?
Section 2 - Hypothesis Testing for A/B Testing
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
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
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
What are Econometrics & Causal Inference and why are they important?
Section 5 - Google Causal Impact (Econometrics and Causal Inference)
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
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
What Is Data Visualization And Why It Is Important?
Section 7 - Distribution Charts and Plotting Basics
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
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
What is Segmentation and why is it important?
Section 9 - RFM (Recency, Frequency, Monetary) Analysis
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
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
What are Predictive Analytics and why are they important?
Section 11 - Random Forest
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
(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
What is Advanced Analytics and why it is so important?
Section 13 - Multivariate A/B Testing
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?
Thank You! (1:17)
Review This Course!
Become An Alumni
Learning Guideline
ZTM Events Every Month
LinkedIn Endorsements
ARCHIVED - AVAILABLE UNTIL MAY 1 2024: PART A: STATISTICS
What are Statistics and why are they important?
ARCHIVED - AVAILABLE UNTIL MAY 1 2024: 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)
Let's Have Some Fun (+ Free Resources)
ARCHIVED - AVAILABLE UNTIL MAY 1 2024: 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)
Unlimited Updates
ARCHIVED - AVAILABLE UNTIL MAY 1 2024: 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)
Course Check-In
ARCHIVED - AVAILABLE UNTIL MAY 1 2024: 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)
Implement a New Life System
ARCHIVED - AVAILABLE UNTIL MAY 1 2024: 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)
CASE STUDY: The Ethical Considerations of Testing - AI in Recruitment
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