Introduction
What is Predictive Analytics?
Shedding Light with Predictive Analytics
Features of Predictive Analytics Models
Big Data Considerations and Sources
Time Series, Uplift, and Logistic Models
Time Series, Uplift, and Logistic Models
Predictive Analytics vs. Traditional BI
Practical Exercise
Applications of Predictive Analytics
Applied Predictive Modeling
Overview of Predictive Modeling
Exploratory Data Analysis
Overview of Regression Methods
Linear Regression in Python
Logistic Regression in Python
Overview of Clustering Methods
Hierarchical Clustering in Python
K-Means Clustering in Python
Overview of Decision Trees and Random Forests
Decision Trees in Python
Random Forests in Python
Exercise: Apply Predictive Models
Process & Technology
Predictive Analytics Project
Identifying Project Stakeholders and Roles
Project Requirements and Considerations
Collecting and Preparing Data
Building and Training a Predictive Model
Predictive Analytics Implementation
Monitoring Model Usefulness and Applying Knowledge
Practical Exercise
Statistical Concepts
Predictive Analytics and Statistics
Types of Data
Probability Overview and Probabilistic Events
Minimizing the Margin of Error
CI for Hypothesis Testing
Testing for Differences
Practical Exercise
Correlation & Regression
Overview of Correlation
Correlation and Predictive Analytics
Statistical Significance of Correlation
Introduction to Regression Analysis
Best Fit and Residual Analysis
Logistic Regression for Predictive Analytics
Practical Exercise
Data Collection
Choosing Predictive Data
Extract, Transform, and Load Data
Data Warehousing
Relational Database Management System and Hadoop
Data Collection Considerations
Data Collection Strategy
Data Exploration Objectives
Practical Exercise
Data Preprocessing
Need to Clean Messy Data
Outlier Identification and Handling
Transforming, Normalizing, and Scaling Data
Variable Partitioning
Dummy Variables and Variable Removal
Approaches for Handling Missing Data
Practical Exercise
Data Mining and Analytics
Descriptive Data Analytics
Prescriptive Data Analytics
What Is Data Mining?
Data Mining Concepts and Techniques
Methods for Data Mining
Distributions and the Probability Density Function
Binomial and Poisson Distributions
Introduction to Hypothesis Testing
Practical Exercise
A/B Testing
Overview of A/B Testing
A/B Testing Features
Implementing A/B Testing
Naïve Bayes and Bayesian Belief Networks
Support Vector Machines
Practical Exercise
Linear and Logistic Regression
Linear Regression Overview
Ordinary Least Squares (OLS)
Logistic Regression Overview
Logit Transformation and the Likelihood Function
Interpreting Results and Testing Significance
Odds Ratio and Relative Risk
Considerations for Logistic Regression
Practical Exercise
Machine Learning
Machine Learning Overview
Machine Learning Tools and Process
Deep Learning
Supervised vs. Unsupervised Methods
Ensemble Techniques for Machine Learning
Segmentation Modeling
Data Visualization
What is Data Visualization
Design
Choosing the Type of Visualization for the Data
Identifying Interactivity in the Data Visualizations
Counter Examples of Data Visualizations
Illustrations and Icons
Scale Usage
Charts
Build a Design
Practice: Design Data Visualizations
R Programming
Introduction
Debugging
Data Handling
Basic Statistics
Visualizing Data
Practice: Introduction to R Programming
