Statistical Thinking for Data Science and Analytics

Learn how statistics plays a central role in the data science approach.

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Created by: Eva Ascarza

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Course Description

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.
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

Beginner Level

Time: 5 Weeks - 1-2 hours per week

Instructor Details

Eva Ascarza

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Eva Ascarza is an Assistant Professor of Marketing at Columbia Business School. She is a marketing modeler who uses tools from statistics and economics to answer marketing questions. Her main research areas are customer analytics and pricing in the context of subscription businesses. She specializes in understanding and predicting changes in customer behavior, such as customer retention and usage. Another stream of her research focuses on developing statistical methodologies to be used by marketing practitioners. She received her PhD from London Business School (UK) and a MS in Economics and Finance from Universidad de Navarra (Spain).