Data Science with R

An Introduction to Data Science and an Overview of the Course

Analytics Methodology and Problem Solving Frameworks

Statistics 1 - Descriptive Statistics

Statistics 2 - Inferential Statistics (Probability Distributions)

Statistics 3 - Hypothesis Testing

Statistics 4 - Advanced Hypothesis Testing

Case Study - Sales and Marketing effectiveness

An Introduction to R

Case Study - Creating different data structures in R and importing Excel workbook into R

Manipulating data with R

Case Study - Understanding the flights delay data

Data Visualization with R

Case Study - Extracting insights from Forbes data

Models and Algorithms

Data Pre-Processing - Data Exploration

Case Study - Exploratory Analysis of Response Model Data

Data Pre-Processing - Data Preparation

Case Study - Campaign Response Data - Exploration and Preparation

Predictive Analytics - Linear Regression Models

Case Study - Direct Marketing

Case Study - Predicting the Price of Houses in Boston City

Predictive Analytics - Logistic Regression Models

Case Study - Credit Card Spend Patterns

Machine Learning - Clustering Models

Case Study - Finding a Replacement for a Football Player in the Team using Clustering

Machine Learning - Decision Tree Models

Case Study - Building a Default Risk Scorecard Model

Case Study - Predicting Debit Card Usage Based on Historical Spends

Time Series Forecasting

Capstone Project and Course Wrap

Capstone Project - Building a Churn Scorecard for Telecom

Project Documentation and Deployment (Optional)

Best Practices (Optional)

Career Services (Optional)

Model Validation and Interpretation (Optional)

Advanced R Programming (Optional)

Advanced Data Manipulation with Excel (Optional)

Introduction to Text Mining (Optional)

Introduction to SQL (Optional)

Get Started with Kaggle (Optional)

Data Visualisation (Optional)

Multiple Quizzes