Autoplay
Autocomplete
Previous Lesson
Complete and Continue
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)
Understanding Your Video Player (notes, video speed, subtitles + more)
Set Your Learning Streak Goal
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)
Let's Have Some Fun (+ Free Resources)
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
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
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
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 (1:25)
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 8 - 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 (2:36)
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 (5:25)
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: 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 (1:41)
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)
Where To Go From Here?
Thank You! (1:17)
Review This Course!
Become An Alumni
Become a ZTM Ambassador ➡ Refer new students. Earn cash.
Become a ZTM Ambassador ➡ Refer new students. Earn cash.
This lecture is available exclusively for ZTM Academy members.
If you're already a member,
you'll need to login
.
Join ZTM To Unlock All Lectures