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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.
- 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
Prerequisite
High School Math. Some exposure to computer programming.
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
Instructors
Andrew Gelman
Professor of Statistics and Political Science
Columbia University
David Madigan
Executive Vice President and Dean of Faculty of Arts and Sciences
Columbia University
Lauren Hannah
Assistant Professor in the Department of Statistics
Columbia University
Eva Ascarza
Assistant Professor of Marketing at Columbia Business School
Columbia University
James Curley
Assistant Professor of Psychology
Columbia University
Tian Zheng
Series Creator
Columbia University
Content Designer
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J'ai beaucoup aimé ce MOOC Il est à la fois accessible et permet d'acquérir des notions assez pointues. Il demande suffisamment de travail pour que l'obtention du certificat soit perçu comme une victoire. Et ça faisait longtemps que j'avais pas éprouvé autant de plaisir à en terminer un ! Sur le contenu, il s'agit surtout de l'exploration de quelques champs d'applications des statistiques modernes. L'analyse de texte, la génomique, le big data évidemment, et d'autres domaines tout aussi passionnants. Compter environ 3 heures par semaine pour des techophiles maîtrisant l'anglais. Le double pour les autres. Mais le jeu en vaut la chandelle !
J'ai beaucoup aimé ce MOOC Il est à la fois accessible et permet d'acquérir des notions assez pointues. Il demande suffisamment de travail pour que l'obtention du certificat soit perçu comme une victoire. Et ça faisait longtemps que j'avais pas éprouvé autant de plaisir à en terminer un ! Sur le contenu, il s'agit surtout de l'exploration de quelques champs d'applications des statistiques modernes. L'analyse de texte, la génomique, le big data évidemment, et d'autres domaines tout aussi passionnants. Compter environ 3 heures par semaine pour des techophiles maîtrisant l'anglais. Le double pour les autres. Mais le jeu en vaut la chandelle !
