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1 - 5 of 5 results for: donoho

STATS 285: Massive Computational Experiments, Painlessly

Ambitious Data Science requires massive computational experimentation; the entry ticket for a solid PhD in some fields is now to conduct experiments involving 1 Million CPU hours. Recently several groups have created efficient computational environments that make it painless to run such massive experiments. This course reviews state-of-the-art practices for doing massive computational experiments on compute clusters in a painless and reproducible manner. Students will learn how to automate their computing experiments first of all using nuts-and-bolts tools such as Perl and Bash, and later using available comprehensive frameworks such as ClusterJob and CodaLab, which enables them to take on ambitious Data Science projects. The course also features few guest lectures by renowned scientists in the field of Data Science. Students should have a familiarity with computational experiments and be facile in some high-level computer language such as R, Matlab, or Python.
Terms: Spr | Units: 2
Instructors: Donoho, D. (PI)

STATS 398: Industrial Research for Statisticians

Doctoral research as in 399, but must be conducted for an off-campus employer. A final report acceptable to the advisor outlining work activity, problems investigated, key results, and any follow-up projects they expect to perform is required. The report is due at the end of the quarter in which the course is taken. May be repeated for credit. Prerequisite: Statistics Ph.D. candidate.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit

STATS 399: Research

Research work as distinguished from independent study of nonresearch character listed in 199. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit

STATS 801: TGR Project

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit

STATS 802: TGR Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit
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