你将学到什么
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
课程概况
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.
课程大纲
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
预备知识
High School Math. Some exposure to computer programming.
