• Data collection, analysis and inference
• Data classification to identify key traits and customers
• Conditional Probability-How to judge the probability of an event, based on certain conditions
• How to use Bayesian modeling and inference for forecasting and studying public opinion
• Basics of Linear Regression
• Data Visualization: How to create use data to create compelling graphics

Week 1 – Introduction to Data Science

Week 2 – Statistical Thinking

• Examples of Statistical Thinking
• Numerical Data, Summary Statistics
• From Population to Sampled Data
• Different Types of Biases
• Introduction to Probability
• Introduction to Statistical Inference

Week 3 – Statistical Thinking 2

• Association and Dependence
• Association and Causation
• Conditional Probability and Bayes Rule
• Simpsons Paradox, Confounding
• Introduction to Linear Regression
• Special Regression Models

Week 4 – Exploratory Data Analysis and Visualization

• Goals of statistical graphics and data visualization
• Graphs of Data
• Graphs of Fitted Models
• Graphs to Check Fitted Models
• What makes a good graph?
• Principles of graphics

Week 5 – Introduction to Bayesian Modeling

• Bayesian inference: combining models and data in a forecasting problem
• Bayesian hierarchical modeling for studying public opinion
• Bayesian modeling for Big Data