About STAT 6870 A
Advanced data analysis, collection, and filtering; statistical modeling, monte carlo statistical methods, and in particular Bayesian data analysis, including necessary probabilistic background material; a practical focus on real datasets and developing good habits for rigorous and reproducible computational science. Prerequisites: STAT 5870, CS 5870, CSYS 5870, or Instructor permission. Cross-listed with: CS 6870, CSYS 6870.
Notes
Prereqs enforced by the system: STAT 3870 or CS 3870 or CSYS 5870; Open to Degree and PACE Students; Cross listed with CS 6870 A and CSYS 6990 A; Total combined enrollment: 40
Section Description
Extracting meaning from data remains one of the biggest tasks of science. The Internet and modern computers have given us vast amounts of data, so it is more important than ever to understand how to collect, process, and analyze these data while maintaining reproducibility with data provenance or "chain of custody" of the data. In this course students will learn: 1. scientific computing pipelines, software testing, “defensive” data analysis, and revision control, 2. practical implementations of advanced statistical analyses, 3. how to deal with large-scale datasets, remote computing, and "big data"-ready pipelines, 4. ethical and privacy implications of collecting and analyzing big data 5. to explore the literature of cutting-edge data analytics 6. to communicate data-driven results. As with Data Science I, particular emphasis will be placed on nontraditional (non-numeric) data such as networks, text corpora, etc. and on developing good habits for rigorous and reproducible computational science.
Section Expectation
The best way to learn is by doing. Lectures will be used for guidance, but students will directly develop their own computer programs and workflows. Students should expect an average of 6-8 hours of work outside of class per week, depending on skill level and experience entering the course. No textbook is required. Course Prerequisites: STAT/CS 287 Data Science I.
Evaluation
Grades will be based on homework assignments, readings and in-class discussions, and a final research project and presentation.
Important Dates
Note: These dates may change before registration begins.
Note: These dates may not be accurate for select courses during the Summer Session.
| Last Day to Add | |
|---|---|
| Last Day to Drop | |
| Last Day to Withdraw with 50% Refund | |
| Last Day to Withdraw with 25% Refund | |
| Last Day to Withdraw |
Resources
There are no courses that meet this criteria.
Interest Form
STAT 6870 A is closed to new enrollment.
But we can remind you a few days before the next term opens. You can also see what terms are enrolling currently.