"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.
- Ramesh Johari Associate Professor, Management Science & Engineering
- Binary classification
- Causal inference
- Experimental design
- Machine Learning
- Statistics (frequentist, Bayesian)
- Time series modeling
- experience with R at the level of STATS195
- 1 year of college level calculus (through calculus of several variables, such as CME100 or MATH51)
- Background in statistics, experience with spreadsheets recommended.
- An undergraduate degree with a GPA of 3.0 or equivalent