Rice MCSE vs Georgetown Analytics

本来已经决定从Rice的，可是来了Georgetown 的Analytics 项目。 感觉课程更对口。 两个项目都没有就业信息。 坛子里的信息也比较少。 求过来人多多分享下这两个项目的信息~~
下面贴出这两个项目的课程，各人感觉Gtown的课程更贴近data science. 大家看看呢？

[align="left"]Rice[/align][align="left"]Requirements[/align][align="left"]· BA or BS in an engineering or science discipline, with trainingin engineering mathematics, statistical foundations and programming methodology.[/align][align="left"]· Requirements for Professional Degrees http://ga.rice.edu/GR_degrees/[/align][align="left"]· 30 hours of approved advanced study:[/align][align="left"]o -3 corecourses (up to 10 credits), student to chose one out of each group[/align][align="left"]Group 1:[/align][align="left"]CAAM 519 (3) Computational Science I[/align][align="left"]CAAM 536 (3) Numerical Methods for Partial DifferentialEquations[/align][align="left"]CAAM 553 (3) Numerical Analysis 1[/align][align="left"]CAAM 571 (3) Linear and Integer Programming[/align][align="left"]CAAM 564 (3) Numerical Optimization[/align][align="left"] Group 2:[/align][align="left"]COMP 539 (4) Software Engineering Methodology[/align][align="left"]COMP 522 (3) Multi-Core Computing[/align][align="left"]COMP 530 (3) Introduction to Database Systems[/align][align="left"] Group 3:[/align][align="left"]STAT 518 (3) Probability[/align][align="left"]STAT 519 (3) Statistical Inferences[/align][align="left"]STAT 541 (3) Multivariate Analysis[/align][align="left"]STAT 615 (3) Regression and Linear Models[/align][align="left"] [/align][align="left"]-7 electives, at least one must be from Communication,Leadership, Management and Ethics Group.[/align][align="left"] [/align][align="left"]Computationaland Applied Math[/align][align="left"]CAAM 519 (3) Computational Science I[/align][align="left"]CAAM 436 (3) Partial Differential Equations of MathematicalPhysics[/align][align="left"]CAAM 536 (3) Numerical Methods for Partial DifferentialEquations[/align][align="left"]…and other500 level or above courses listed on the Computationaland Applied Math department[/align][align="left"] [/align][align="left"]ComputerScience[/align][align="left"]COMP 536 (3) High Performance Computer Architecture[/align][align="left"]COMP 560 (4) Computer Graphics and Geometric Modeling[/align][align="left"]COMP 528 (4) Computer Systems and Performance Analysis[/align][align="left"]…and other500 level or above courses listed on the Computer Science department[/align][align="left"] [/align][align="left"]Statistics:[/align][align="left"]STAT 605 (3) Statistical Computing and Graphics[/align][align="left"]STAT 615 (3) Regression and Linear Models[/align][align="left"]STAT 616 (3) Advanced Statistical Methods[/align][align="left"]STAT 502 Neural Machine Learning[/align][align="left"]…and other500 level or above courses listed on the Statisticsdepartment[/align][align="left"] [/align][align="left"]Communication,Leadership, Management and Ethics[/align][align="left"]ENGI 610 (3) Management for Science and Engineering[/align][align="left"]ENGI 510 (3) Technical and Managerial Communication[/align][align="left"]ENGI 529 (3) Ethics and Engineering Leadership[/align][align="left"] ENGI 505(3) Engineering Project Development and Management[/align][align="left"]ENGI 528 (3) Engineering Economics[/align][align="left"]NSCI 521 (3) Writing and Publishing Science[/align][align="left"]ENGI/LEAD 545 Structured Problem Solving[/align][align="left"]ENGI 614 (3) Learning how to innovate[/align][align="left"]ENGI 615 (3) Leadership Coaching for Engineers[/align][align="left"]UNIV 594 (3) Responsible Conduct of Research[/align][align="left"]
[/align][align="left"]Georgetown:[/align][align="left"]CORE COURSES(REQUIRED)[/align][align="left"]Thefive-course core is designed to give students an overview of the massivedata landscape (15 credits).[/align][align="left"]· Introduction to Data Analytics (ANLY-501)[/align][align="left"]· Massive Data Fundamentals (ANLY-502)[/align][align="left"]· Scientific and Analytical Visualization (ANLY-503) [/align][align="left"]· Probabilistic Modeling and Statistical Computing (ANLY-511)[/align][align="left"]· Statistical Learning for Analytics (ANLY-512)[/align][align="left"]ELECTIVES[/align][align="left"]Thefollowing courses have been pre-approved by the program and willsatisfy MS-DS Program elective requirements.Additional coursework may be approved upon request,and at the discretion of the program (15 credits).[/align][align="left"]· Effective Written and Oral Presentation forTechnology & Science (ANLY-520)[/align][align="left"]· Databases (ANLY-531)[/align][align="left"]· Optimization (ANLY-561)[/align][align="left"]· Computational Methods for Data Analytics (ANLY-562)[/align][align="left"]· ANLY Internship (ANLY-905)[/align][align="left"]· Bioinformatics for Omics Data (BIST-532)[/align][align="left"]· Intro to Social Network Analysis (CCTP-696)[/align][align="left"]· Image Processing (COSC-455)[/align][align="left"]· Information Retrieval (COSC-488)[/align][align="left"]· Data Privacy (COSC-531)[/align][align="left"]· Advanced Algorithms (COSC-540)[/align][align="left"]· Natural Language Processing (COSC-572)[/align][align="left"]· Statistical Machine Learning (COSC-578)[/align][align="left"]· Knowledge Discovery and Data Mining (COSC-585)[/align][align="left"]· Text Mining and Analysis (COSC-586)[/align][align="left"]· Web Search and Sense-making (COSC-589)[/align][align="left"]· Mathematics of Climate (MATH-412)[/align][align="left"]· Mathematics of Social Networks (MATH-442)[/align][align="left"]· Deterministic Mathematical Models (MATH-502)[/align][align="left"]· Stochastic Simulation (MATH-611)[/align][align="left"]· Introduction to Operations Research (MATH-615)[/align][align="left"]· Sparse Repre & Random Sampling (MATH-623)[/align][align="left"]· Bayesian Statistics (MATH-640)[/align][align="left"]· Time Series Analysis (MATH-645)[/align][align="left"]· Regression Methods & Generalized Linear Models (MATH-651)[/align][align="left"]· Data Mining (MATH-656)[/align]
[align="left"]· Categorical Data Analysis (MATH-657)[/align]