"Small" Data: Prediction, Inference, Causality
Fee may apply
"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.
- 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
- Binary classification
- Causal inference
- Experimental design
- Machine Learning
- Statistics (frequentist, Bayesian)
- Time series modeling
Note on Course Availability
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.
|Dates:||September 24 - December 7, 2018|
|Days:||Mon, Wed, Fri|
|Times:||10:30 am - 12:20 pm|
Provides Stanford University credit that may later be applied towards a graduate degree or certificate. Includes access to online course materials and videos for the duration of the academic quarter. Starting Autumn 2016 there is a $100 fee per course for courses dropped before the drop deadline. Click here for more information about our Registration Policies.