Classes

Overview

The structure of the Ph.D. was radically revamped in 2004. In particular, the decades-old preliminary exams were deprecated in favor of more holistic assessment methods.

The core courses are 205A and 205B (probability theory), 210A and 210B (theoretical statistics) and 215A and 215B (applied statistics). In addition, 204 is a probability course that does not require measure theory. Ph.D. candidates will usually choose their four first year courses from these seven - often both parts of two of the three series 205, 210 and 215. There can be good reasons for not following this pattern:

• If you are interested in interdisciplinary work, you may wish to include a course from another department.

• If you have done graduate work at another university, the material in the "A" courses may already be familiar to you (or it may not.) Other courses may be more appropriate.

However, you will need to consult with the graduate advisor to receive approval.

All of the core courses require a large commitment of time, particularly with the homework. Taking more than two of them per semester (eight units) would be a herculean task. Some students may be required to take twelve units per semester, e.g. GSIs are required by the university to be enrolled in 12 units. The department also offers courses beyond the core that are more research oriented. They complement the core courses and typically focus on reading and presenting papers, rather than weekly homework. Also, bear in mind that your thesis topic does not need to come from a general area covered by the courses you take in first year: some students who take 205 and 215 in their first year end up working on theoretical statistics, and so on.

Computing courses

In an applied course like STAT 215, there is a large computing component, and though the course GSI will give an introduction to the software required, students with limited statistical computing experience often find it difficult to pick up the software skills at the same time as the course material. In particular, if you have not used R (or S-PLUS) before, you may wish to familiarize yourself with the software before you arrive. R can be downloaded for free from the R Project website, in Windows, Mac and UNIX versions. Many tutorials are available on the web.

STAT 296 gives a brief overview of common software and when they might be useful. Stat 243 is a more intensive course, designed to help students pick up some relevant programming skills.

Assessment

Each course has its own assessment, through homework as well as take-home and/or in-class midterms and final exams.

At the end of each semester, each of your instructor will provide a short paragraph on your progress, as well as a numerical score from 0 to 100, where 85 is satisfactory, 80 is marginal, and less than 80 is unsatisfactory. The graduate committee will then meet to discuss your progress.

Choosing your courses

Your first point of contact should be the Graduate or Masters advisor; see the contacts page on the department site for up-to-date information. But you should probably talk to other faculty, and definitely talk to current students, who tend to be more accurate judges of workloads than are professors.

Core Statistics Courses

Stat 204: Probability for Applications

Offered in the Fall semester only. Covers much of the same material as 205A, but without measure theory and hence with less rigor. Still pretty hard. Students intending to pursue probability should take the 205 series; other students should prefer this, unless they really enjoy a challenge.

Stat 205: Probability Theory

Rigorous and theoretical; a suitable course if your mathematical background is strong. Some knowledge of measure theory is extremely helpful: the basics of measure theory are covered in the first few weeks of lectures, but students who haven't seen it before often find this isn't enough. The homeworks given in this course are time-consuming and hard. Students often form study groups to share ideas.

205A: Measure theoretical approach to probability, conditional expectation, martingales. 205B: Markov processes, limit theorems, characteristic functions, ergodic theory, brownian motion.

Stat 210: Theoretical Statistics

210A contains essential material for the understanding of statistics. 210B contains more specialized material. The 210 courses are less mathematically rigorous than the 205 courses. 210A is easier than 205A; 210B is very difficult conceptually, though in practice it's easy to perform adequately. Homework is time-consuming.

210A: The frequentist approach to statistics with comparison to Bayesian and decision theory alternatives, estimation, model assessment, testing, confidence regions, some asymptotic theory. 210B: Depends on the lecturer, but focus is usually asymptotic theory.

Stat 215: Statistical Models: Theory and Application

Requires weekly or bi-weekly computer assignments as well as readings; sometimes there is additional homework. These courses tend to be somewhat polarizing.

215A: Depends a lot on the lecturer: sometimes focuses on exploratory techniques; sometimes focuses on correct and incorrect inference. Methodology includes regression and testing. 215B: Depends on the lecturer: usually advanced regression - theory of linear models, GLMs, GAMs.

Other Regular Courses

• STAT 200 Introduction to Probability and Statistics at an Advanced Level
• STAT 230 Linear Models
• STAT 240 Nonparametric and Robust Methods
• STAT 241A Statistical Learning Theory
• STAT 241B Advanced Topics in Learning and Decision Making
• STAT 243 Introduction to Statistical Computing
• STAT 244 Statistical Computing
• STAT 246 Statistical Genetics
• STAT 251 Stochastic Analysis with Applications to Mathematical Finance
• STAT 261 Quantitative/Statistical Research Methods in Social Sciences
• STAT 272 Statistical Consulting
• STAT 278 Statistical Research Seminar
• STAT 296 Resources for Statistical Computing
• STAT 300 Professional Preparation: Teaching of Probability and Statistics

Advanced Topic Courses

These courses are not taught regularly, and the content varies from semester to semester and by instructor. Topic announcements are usually made at the end of the preceding semester.

• Stat 206: Stochastic Processes
• Stat 212: Topics in Theoretical Statistics
• Stat 260: Topics in Probability and Statistics

In addition, various individual study courses may be taken under the supervision of a faculty member.

Courses in Other Departments

Depending on your research interests, you may find courses outside the department to be beneficial.

I took a computational neuroscience class (MCB260) because I am interested in applications of statistical methodology to neuronal data. -- VinceVu - 07 Mar 2007