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EEB does not have many course requirements so you can concentrate on your research, which is more important. Below are the required courses, which are generally taken in the first two years. Additional courses may be required if you are in the PEI-STEP program or on a fellowship from a training grant. Also, your committee might recommend that you take or audit additional courses (or do guided or independent reading) to fill gaps in your background, and you are welcome to take or audit any courses that interest you as long as you maintain the focus on your research.
- Fundamentals (EEB504)
- This course involves reading and discussing the papers fundamental to the development of evolution, ecology, and behavior, as chosen by the faculty. Each week the course is led by a different faculty member who assigns 3-5 key papers in their area of expertise, which you have to read and summarize/synthesize in a short essay. These papers help to focus what you need to know for the general knowledge component of your general exam.
- Journal Club (EEB507)
- You will take this one-semester class in the fall semester of your first year. You will read and discuss current literature with Henry Horn. The goal is to become familiar with key journals and current research in your cohort’s areas of interest.
- Tropical Ecology (EEB521)
- This course usually alternates between the neotropics and Kenya and sometimes Australia, where you will spend three and a half weeks doing a field project. The trip happens in January, and there are sporadic organizational meetings during fall semester. A research paper and/or presentation are due in the spring. Students are required to pay their own airfare, but on-the-ground expenses are covered by the department. Payment plans for the airfare can be arranged if necessary.
- Professional Topics
- The first part of this focuses on grant-writing. You’ll do a Sigma Xi (or similar-scale) grant application if you’re a first year, or start preparing your thesis proposal (which could eventually become an NSF DDIG application – see General Exam section) if you’re a second year. In some years a discussion of professional topics like authorship, ethics, etc. follows. Note: This material has been covered as part of an EEB course (EEB505) in the past, but it is currently under revision for the 2012-2013 academic year, and may be covered in a different format.
- Colloquium/Seminar (EEB522)
- Everyone is expected to attend the EEB seminars, which are on Thursdays from 4:30-5:30 in Guyot 10. All grad students who have not passed generals must register for EEB522. Having the speaker in town creates the following opportunities:
- Half-hour individual meetings with the speaker: you can set these up through the administrator organizing the seminar (watch for emails). Even if you are a first year and don’t know what you want to research yet, it can still be useful and fun to meet with the visiting speaker.
- Student lunch: Due to the seminar migrating to an afternoon slot from its traditional lunchtime slot, alternate plans for student lunch with the speaker are currently in the works (as of September 2012).
- Faculty-hosted dinner: For some talks, the host may have the speaker and the entire department over at his/her house for dinner. This is another great opportunity to interact with the speaker and the faculty in a casual setting.
Advanced Statistics Courses
As many of you know, or are soon to find out, the EEB department does not offer a graduate level course in statistics. While some entering graduate students may have taken such courses, many others have not. For those of us in the latter category, here is a partial listing of statistics courses that go beyond your standard, first-semester introductory material.
Courses in Regression, Probability, and/or Multivariate Methods:
- EEB 355/MOL 355 Introduction to Statistics for Biology (John D. Storey/Peter Andolfatto)
- An introduction to statistical models, methods, and concepts with a particular focus on applications in biology. Real data sets will be analyzed using the R software package in order to gain an understanding of how statistics is used in practice. Topics to be covered include probability, experimental design, point estimation, hypothesis testing, Bayesian statistics, and the extension of these topics to modern biological studies. SPRING
- GEO 422 Data, Models, and Uncertainty in the Natural Sciences
- This one looks like it's got some of the introductory stuff, but goes way beyond that…
- No more being puzzled by dots on a graph! This course is for those who want to turn observations into models and subsequently evaluate their uniqueness and uncertainty. Three main topics are elementary statistics, heuristic time series analysis, and model parameter estimation via matrix inverse methods. While the instructors and textbook examples will be derived mostly from the geosciences, students are encouraged to bring their own data sets for classroom discussion and in-depth analysis as part of their term papers. Problem sets and computer exercises form integral parts of the course. Contents may be tailored to meet student demands. FALL
- ORF 309/MAT 309 Probability and Stochastic Systems
- An introduction to probability and its applications. Random variables, expectation, independence. Poisson processes, Markov chains, and Brownian motion. Stochastic models of queues, population dynamics, and reliability. FALL
- PSY 504 Experimental Design and Analysis in Psychological Research
- This course will provide students with a broad overview of multivariate statistics. Topics covered will include multiple regression, analysis of covariance, multivariate analysis of variance, discriminant function analysis, logistic regression, principal components analysis, factor analysis, path analysis, and structural equation modeling. SPRING (not offered every year)
- SOC 504 Social Statistics
- This course provides a thorough examination of linear regression from a data analytic point of view. Sociological applications are strongly emphasized. Topics include: (a) a review of the linear model; (b) regression diagnostics for outliers and collinearity; (c) smoothers; (d) robust regression; and (e) resampling methods. Students taking the course should have completed an introductory course in probability and statistics. SPRING
- WWS 507C Quantitative Analysis (Advanced)
- Data analysis techniques, stressing application to public policy. The course includes measurement, descriptive statistics, data collection, probability, exploratory data analysis, hypothesis testing, simple and multiple regression, correlation, and graphical procedures. Some training is offered in the use of computers. No previous training in statistics is required. The course is divided into separate sections according to the student's level of mathematical sophistication. The advanced level assumes a fluency in calculus. (GRADUATE STUDENTS ONLY) FALL
- WWS 509 / ECO 509 Generalized Linear Statistical Models
- The analysis of survey data using generalized linear statistical models. The course begins with a review of linear models for continuous responses and then considers logistic regression models for binary data and log-linear models for count data, including rates and contingency tables and hazard models for duration data. Attention is given to the logical and mathematical foundations of the techniques, but the main emphasis is on the applications, including computer usage. (GRADUATE STUDENTS ONLY) FALL
- See more courses here -- you can also search by keyword (e.g. "statistics")
Courses on Time Series:
- ECO 513 Advanced Econometrics: Time Series Models
- Concepts and methods of time series analysis and their applications to economics. Time series models to be studied include simultaneous stochastic equations, VAR, ARIMA, and state-space models. Methods to analyze trends, second-moment properties via the auto covariance function and the spectral density function, methods of estimation and hypothesis testing and of model selection will be presented. Kalman filter and applications as well as unit roots, cointegration, ARCH, and structural breaks models are also studied. FALL
- GEO422 see above.
- ORF 405 Regression and Applied Time Series
- Statistical Analysis of financial data: Density estimation, heavy tail distributions and dependence. Regression: linear, nonlinear, nonparametric. Time series analysis: classical models (AR, MA, ARMA, ..), state space systems and filtering, and stochastic volatility models (ARCH, GARCH, ....). FALL
- ORF 505/FIN 505 Modern Regression and Time Series
- Linear and mixed effect models. Nonlinear regression. Nonparametric regression and classification. Time series analysis: stationarity and classical linear models (AR, MA, ARMA, ..). Nonlinear and nonstationary time series models. State space systems, hidden Markov models and filtering. SPRING (not offered every year)