- Platform
- edX
- Provider
- Columbia University
- Effort
- 7-10 hours/week
- Length
- 5 weeks
- Language
- English
- Credentials
- Paid Certificate Available
- Part of
-
Professional Certificate: Data Science for Executives
- Course Link
Overview
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 Will Learn
Taught by
Eva Ascarza , James Curley , Andrew Gelman , Lauren Hannah, David Madigan and Tian Zheng
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 Will 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
Week 1 – Introduction to Data Science
Week 2 – Statistical Thinking
Week 5 – Introduction to Bayesian Modeling
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
- Association and Dependence
- Association and Causation
- Conditional Probability and Bayes Rule
- Simpsons Paradox, Confounding
- Introduction to Linear Regression
- Special Regression Models
- 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
Taught by
Eva Ascarza , James Curley , Andrew Gelman , Lauren Hannah, David Madigan and Tian Zheng