MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
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
This course is part of the Data Science for Executives Professional Certificate.
Course 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
- 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
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.