Fundamentals of Data Science: Prediction, Inference, Causality
"Small" data are datasets that allow interaction, visualization, exploration and analysis on a local machine to drive business intelligence. This course explores the difference between "small" data and big data and provides an introduction to applied data analysis, with an emphasis on a conceptual framework for thinking about data from both statistical and machine learning perspectives. Class lectures will be supplemented by data-driven problem sets and a project.
Topics Include
- Binary classification
- Bootstrapping
- Causal inference
- Experimental design
- Machine Learning
- Regression
- Statistics (frequentist, Bayesian)
- Multiple hypothesis testing