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School Research for MS Stat Ranking: 综合排名： UCLA-25 Gatech-35 UCSD-37 Ranking 对比： USNEWS 的统计专业排名为 1 Stanford University Stanford, CA University of California--Berkeley Berkeley, CA 3 University of North Carolina--Chapel Hill Chapel Hill, NC 4 Harvard University Cambridge, MA 5 Iowa State University Ames, IA 6 Duke University Durham, NC University of Chicago Chicago, IL University of Washington Seattle, WA 9 University of Florida Gainesville, FL， 可以看到三所学校都不在其中。就综合排名来讲似乎 UCLA 更占优势，不过 GaTech 的理工 科在全美有很高的地位，所以猜想他家的 STAT 项目也很不错。 不过对于想回国就业的话，综合排名显然更重要，这三所学校严格意义上都不算 Top Tier， 所以对于国内的公司可能他们也没什么区分性。 Location: UCSD – San Diego, CA UCLA – Los Angeles, CA Gatech – Atlanta, GA Location 对比： UCLA、UCSD 均在加州，一个在洛杉矶，一个在圣地亚哥；而 Gatech 则处在亚特兰大。这三 个城市在美国均为相对发达的城市，大公司也都比较多；作为没有特别 focus 的统计专业的 同学，我三个学校的 location 都还算不错。 Admission condition: UCLA-statistics Admissions Data Average Number of Applicants 163 Average Number of Admits 50 Percent of Applicants Admitted 31% Average Number of New Registrants 19 Percent of Admits Registered 38% Applications, Admits, and New Registrants data are the averages for Fall 2004 - Fall 2008. Percent of Applicants Admitted = number of admits divided by number of applications. Percent of Admits Registered = number of new registrants divided by the number of admits. Enrollment Data Average Number of Registrants Fall Term 69 Percent Women 41% Percent Underrepresented Minorities 6% Percent International Students 38% Percent with a Master's Degree Objective 40% Percent with a Doctoral Degree Objective 59% Average Number of Registrants Fall Term = Average number enrolled for Fall 2004 - Fall 2008. This is adjusted for those programs that have not been in existence for the entire five years. Underrepresented Minorities includes domestic students identified as American Indian/Native American, African American/Black, Mexican American/Chicano, Latino/Other Hispanic, and Filipino. International Students are those on temporary visas. They include all students who are not US citizens, Permanent Residents, Immigrants, or Refugees. Combined Master's & Doctoral Degree Objective percentages may appear lower than 100% if registrants are pursuing other degree objectives including JD, MD, or DDS degrees. Degrees Awarded Total Number of Master's Degrees Awarded 95 Average Time to Master's Degree (In Years) 2 Total Number of Doctoral Degrees Awarded 26 Average Time to Doctoral Degree (In Years) 5 Total masters and doctoral degree data are the number of degrees awarded from 2003-04 through 2007-08. Average time-to-degree is calculated by counting and then aggregating the total number of elapsed terms for students whom were in graduate standing under the specific program's major code from point of entry through degree completion. Faculty researches: UCSD: Algebra Algebraic Geometry Bioinformatics Combinatorics Complex Variables Differential Equations Functional Analysis / Operator Theory Geometric Analysis Geometry and Topology Logic and Computational Complexity Mathematical Physics Mathematics Education Number Theory Numerical Analysis Numerical PDE / Scientific Computation Probability Theory Representation Theory Statistics UCLA: Albert Gifi's Homepage Celebrating the work of Albert Gifi and his many co-workers. Jan de Leeuw Center for Image and Vision Science Statistical and computational theory underlying visual perception and learning. Song Chun Zhu and Alan Yuille Center for the Teaching of Statistics Research an projects related to the teaching of statistics at all levels. Robert Gould Center for Statistical Computing Research in computationally intensive statistical problems. Mark Hansen Fire Hazard Estimation Fire hazard estimation using point process methods. Rick Paik Schoenberg Gradient Projection Algorithms Algorithms for rotation in Factor Analysis. Coen Bernaards and Robert Jennrich gSCAD/iSCAN Cluster computing with Mac OS X on the PowerPC architecture. Jan de Leeuw High Performance Cluster Computing Multivariate analyses with large datasets. Vanessa Beddo and Coen Bernaards Hyperemesis Gravidarum Survey and Information Website devoted to hyperemesis gravidarum. Rick Paik Schoenberg Mathematical Principles for Visual Computation Probability modeling and stochastic computing in vision. Ying-Nian Wu Statistical Analysis of Earthquake Occurrance Data Statistical evaluation of earthquake occurrance data using point process techniques Rick Paik Schoenberg Studio of Bio-data Refining and Dimension Reduction Bio-data Refining and dimension reduction research Ker-chau Li GaTech: Georgia Tech's statistics program emphasizes applications for engineering and the physical sciences. Current research interests of the faculty are listed below: Dave Goldsman - Comparisons via stochastic simulation; Statistical ranking and selection (Professor, Ph. D., Cornell University) Serge Guillas - Functional data analysis; Nonparametric statistics; Time series; Environmental statistics; Spatial statistics (Assistant Professor, Ph. D., University Paris VI) Tony Hayter - Multiple comparison and selection procedures; Engineering statistics (Associate Professor, Ph.D., Cornell University) Russ Heikes - Statistical control procedures; Design of experiments; Statistical model building (Professor Emeritus, Ph.D., Texas Technological University) Christian Houdré - Nonparametric statistics; Statistical methods in finance and bioinformatics (Professor, Ph.D., McGill University) Xiaoming Huo - Multiscale statistical methods, Data mining(Assistant Professor, Ph.D., Stanford University) Vladimir Koltchinskii - Probability theory; mathematical statistics (Professor, Ph.D., Kiev University) Paul Kvam - Reliability; Applied engineering statistics; Nonparametric estimation (Associate Professor, Ph.D., University of California at Davis) J. C. Lu - Statistics for manufacturing; Reliability; Degradation modeling (Professor, Ph.D., University of Wisconsin) Liang Peng - Limit theorems; Extreme value theory and its applications; Boundary estimation; Heavy tailed and long-range dependent time series; Smoothed distribution and quantile estimations; Edgeworth expansions; Empirical likelihood methods (Assistant Professor, Ph.D., Erasmus University) Alex Shapiro - Mathematical programming and statistics; Sensitivity analysis (Professor, Ph.D., Ben-Gurion University of Negev) Carl Spruill - Mathematical statistics and probability (Professor Emeritus, Ph.D., Purdue University) Yung Tong - Mathematical statistics; Multivariate statistical analysis; Reliability theory; Stochastic inequalities; Operations research and engineering statistics; Multiple decision problems; Statistical computing (Professor Emeritus, Ph.D., University of Minnesota) Brani Vidakovic - Multiscale methods; Statistical methods in geophysics; Turbulence; Bayesian decision theory (Professor, Ph.D. Purdue University) Jeff Wu - Design and analysis of experiments; Quality engineering; Product/process improvement; Bioinformatics (Ph.D. University of California - Berkeley) Faculty 对比： 在这三所学校的 faculty research focus 里面，最偏重应用的明显是 GaTech；几乎所有教授的 研究方向都与应用统计，以及统计相关工作里最热门的金融、生物统计、数据挖掘、模拟有 关，而 GaTech MS Stat 的 program description 里就有说道他们想培养应用型的统计人才，这 与申请的目的也是一致的；UCLA 也有很多教授在研究应用统计相关，不过我发现比较牛的 professor 还是偏重在统计理论方面的；而 UCSD 比 UCLA 更偏理论一些。 从学习应用统计的角度来讲，GaTech 是比较好的选择。 Job Resources: GaTech: Job Opportunity Center for Graduate students of department of Mathematics, GaTech Employees: National Security Agency IBM Shell Oil Company United States Olympic Committee Scottrade, Inc. Corporate Resource Group of Mid America Mountbatten Institute BMW Manufacturing Co., LLC UCLA: UCLA 的 Alumni 档案很不全，基本都没有 current employment，猜测可能这部分的 open information 不够多吧 UCSD: UCSD Mathematics : Internship Resources Career Services: The Career Services Center's Internship Supersite can help with career exploration, resume development, interview skill building and development of personal statements. They maintain an on-line listing of internships through Port Triton. Contact CSC at 858/534-3750 for more information. Academic Internship Program: The Academic Internship Program (AIP) features internships for which you can earn college credit. AIP will handle paperwork for research projects on campus. They will also help train you in how to write a successful resume. Call AIP at 534-4355 for more information. Associated Students Internship Office: The Associated Students Internship Office (ASIO) handles both volunteer and paid internships. It provides books listing past student internships and evaluations of the internship sites. They provide mock interviews to prepare the student for the real interview. ASIO is located in the Price Center and can be contacted at 534-4689. Job Resources 对比： 从可以获得的信息来看，GaTech 和 UCSD 都有比较好的就业指导，GaTech 还专门为数学学 院的同学开设了一个就业网站，post 在上面的工作也都是很不错的、跟统计专业很挂钩的工 作，包括了咨询、数据分析、金融、财务等等，很适合不同 career goal 的学生。 而 UCLA 的网站关于就业情况信息很少，很多 Alumni 的联系方式都没有，如果没有内部的 一些非公开信息的话，UCLA 在就业资源的方面是比较弱的。 Graduate Courses: UCSD: GRADUATE COURSES (Fall 2009) MATH 200A A00 Small, Lance Algebra MATH 201A A00 Zelmanov, Efim Basic Topics in Algebra MATH 202A A00 Gill, Philip Applied Algebra I MATH 203A A00 Oprea, Dragos Algebraic Geometry MATH 205 A00 Stark, Harold Topics in Algebraic Number Theory MATH 209 A00 Stark, Harold Sem / Number Theory MATH 210A 00 Li, Bo Mathematical Methods of Physics & Engineering MATH 220A A00 Ebenfelt, Peter Complex Analysis MATH 231A A00 Sterbenz, Jacob Partial Differential Equations MATH 240A A00 Okikiolu, Kate Real Analysis MATH 248 A00 Baouendi, Salah Seminar in Real Analysis MATH 250A A00 Weinkove, Ben Differential Geometry MATH 257A 00 Topics in Differerntial Geometry Ni, Lei MATH 261A A00 Remmel, Jeff Probabilistic Combinatorics & Algorithms I A00 Remmel, Jeff Seminar in Combinatorics MATH 270A A00 Bank, Randy Numerical Linear Algebra MATH 271A A00 Gill, Philip Numerical Optimization MATH 273A A00 Leok, Melvin Applied Analysis for Computational Science I MATH 269 MATH 274 A00 Cheng, Li-Tien Numerical Methods for Physical Modeling MATH 278A A00 Holst, Mike Seminar in Computational Mathematics MATH 278B A00 Holst, Mike Seminar in Mathematical Physics/PDE MATH 280A A00 Driver, Bruce Probability Theory MATH 281A A00 Xu, Lily Mathematical Statistics MATH 282A A00 Politis, Dimitris Applied Statistics MATH 286 A00 Schweinsberg, Jason Stochastic Differential Equations MATH 288 A00 Driver, Bruce Seminar in Probability & Statistics B00 Abramson, Ian MATH 290A A00 Roberts, Justin Topology MATH 292 A00 Roberts, Justin Seminar in Topology MATH 295 A00 Li, Bo Special Topics in Mathematics MATH 296 A00 Tesler, Glenn Student Colloquium UCLA: Graduate Courses 200A. Applied Probability. (4) 200B. Theoretical Statistics. (4) 200C. Large Sample Theory, Including Resampling. (4) 201A. Research Design, Sampling, and Analysis. (4) 201B. Regression Analysis: Model Building, Fitting, and Criticism. (4) 201C. Advanced Modeling and Inference. (4) 202A. Statistics Programming. (4) 202B. Matrix Algebra and Optimization. (4) 202C. Monte Carlo Methods for Optimization. (4) 204. Nonparametric Function Estimation and Modeling. (4) M211. Analysis of Data with Qualitative and Limited Dependent Variables. (4) 212. Program Evaluation and Policy Analysis. (4) M213. Applied Event History Analysis. (4) C216. Social Statistics. (4) 218. Generalized Linear Models. (4) M221. Time-Series Analysis. (4) M222. Spatial Statistics. (4) C225. Experimental Design. (4) C226. Bootstrap, Jackknife, and Resampling Methods. (4) M230. Statistical Computing. (4) M231. Pattern Recognition and Machine Learning. (4) M232A. Statistical Modeling and Learning in Vision and Science. (4) M232B. Statistical Computing and Inference in Vision and Image Science. (4) 233. Statistical Methods in Biomedical Imaging. (4) 234. Statistics and Information Theory. (4) C235. Data Management. (4) C236. Introduction to Bayesian Statistics. (4) M237. Data and Media Arts. (4) 238. Vision as Bayesian Inference. (4) 239. Probabilistic Models of Cognition. (4) 240. Multivariate Analysis. (4) M241. Causal Inference. (4) M242. Multivariate Analysis with Latent Variables. (4) M243. Logic, Causation, and Probability. (4) M244. Statistical Analysis with Latent Variables. (4) M245. History of Statistics. (4) M250. Statistical Methods for Epidemiology. (4) M251. Statistical Methods for Life Sciences. (4) CM252. Statistical Methods for Physical Sciences. (4) 253. Statistical Methods for Ecology and Population Biology. (4) M254. Statistical Methods in Computational Biology. (4) 257. Design, Analysis, and Modeling for Embedded Sensing. (4) C260. Site-Specifics Topics. (4) C261. Introduction to Pattern Recognition and Machine Learning. (4) C273. Applied Geostatistics. (4) C283. Statistical Models in Finance. (4) 285. Seminar: Computing for Statistics. (2 to 4) M286. Seminar: Statistical Problem Solving for Population Biology. (2) 287. Seminar: Gene Expression and Systems Biology. (2) 290. Current Literature in Statistics. (2) 291. Statistics Consulting Seminar. (4) 292. Graduate Student Statistical Packages Seminar. (1 to 2) 293. Graduate Student Research Seminar. (2) C294. Scientific Writing. (2) C295. Fundamentals of Scientific Writing. (2) 296. Participating Seminar: Statistics. (1 to 2) 370. Teaching of Statistics. (4) 375. Teaching Apprentice Practicum. (1 to 4) 495A. Teaching College Statistics. (2) 495B. Teaching College Statistics. (2) 495C. Evaluation of Teaching Assistants. (2) 596. Directed Individual Study or Research. (2 to 8) 598. M.S. Thesis Research. (2 to 12) 599. Ph.D. Dissertation Research. (2 to 12) GaTech: Advanced Courses ISyE 7441 Theory of Linear Models or MATH 6266 Linear Statistical Models MATH 6262 Statistical Estimation MATH 6263 Testing Statistical Hypotheses MATH 6267 Multivariate Statistical Analysis Math/ISyE 6761 Stochastic Processes I Math/ISyE 6762 Stochastic Processes II Math/ISyE 6781 Reliability Theory Methods Courses ISyE 6402 Time Series ISyE 6404 Nonparametric Data Analysis ISyE 6405 Response Surfaces ISyE 7400 Advanced Design of Experiments ISyE 7401 Advanced Statistical Modeling ISyE 7405 Multivariate Data Analysis (see also ISyE 6413 and 6805) Electives (A non-comprehensive and brief list ) ISyE 6650 Probabilistic Models and their Applications ISyE 6644 Simulation ISyE 6656 Queueing Theory Course 对比： 从三所学校的 Advanced Graduate Courses，可以明显看出，GaTech 的课程很偏重应用性，UCLA 提供了很多的课程供不同方向的同学选择，而 UCSD 理论性稍强。联系之前的 Faculty 情况， GaTech 和 UCLA 应该更适合毕业后就工作的统计 master 同学。 总结： 通过上面关于 Location、Ranking、Faculty Research、Job Resource、Courses 几个方面的对比， GaTech 在 location/ranking/financial aid 方面没有明显劣势，而在 Faculty/Job Resource/Courses 方面表现出了很适合希望毕业就工作的偏重应用统计的同学的特点，所以排序选择是 GaTech，UCLA，UCSD.