Statistical Thinking for Data Science and Analytics

edX Statistical Thinking for Data Science and Analytics

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
  • 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
  • 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

Taught by
Eva Ascarza , James Curley , Andrew Gelman , Lauren Hannah, David Madigan and Tian Zheng
Author
edX
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