• Length:
5 Weeks
• Effort:
7–10 hours per week
• Price:

FREE
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• Institution
• Subject:
• Level:
Introductory
• Language:
English
• Video Transcript:
English
• Course Type:

## Prerequisites

High School Math. Some exposure to computer programming.

This statistics and data analysis course will pave the statistical foundation for our discussion on data science.

You will learn how data scientists exercise statistical thinking in designing data collection, derive insights from visualizing data, obtain supporting evidence for data-based decisions and construct models for predicting future trends from data.

### What you'll learn

Skip What you'll learn
• 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

### Syllabus

Skip Syllabus

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
• 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

Andrew Gelman
Professor of Statistics and Political Science
Columbia University
Executive Vice President and Dean of Faculty of Arts and Sciences
Columbia University
Lauren Hannah
Assistant Professor in the Department of Statistics
Columbia University
Eva Ascarza
Assistant Professor of Marketing at Columbia Business School
Columbia University

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