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Rice MCSE vs Georgetown Analytics

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jessicazka123 发表于 2016-5-11 12:26:01 | 显示全部楼层 |阅读模式
[16Fall]
  • [MS.AD无奖][DataScience/Analytics@Rice MCSE]
  • [MS.AD无奖][DateScience/Analytics@Georgetown University]
  • [.][@]
  • [.][@]
  • 个人其他信息:
  • 你对学校的要求: 将来希望从事Data science 方面的工作
  • 你对求比较学校了解的情况(请贴出具体信息): 地理位置都不错,就业数据都没有
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    x
    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? . 涓浜-涓夊垎-鍦帮紝鐙鍙戝竷


    Rice
    Requirements
    ·        BA or BS in an engineering or science discipline, with trainingin engineering mathematics, statistical foundations and programming methodology.
    ·        Requirements for Professional Degrees   http://ga.rice.edu/GR_degrees/
    ·        30 hours of approved advanced study:
    o    -3 corecourses (up to 10 credits), student to chose one out of each group
    Group 1:
    CAAM 519 (3) Computational Science I
    CAAM 536 (3) Numerical Methods for Partial DifferentialEquations
    CAAM 553 (3) Numerical Analysis 1
    CAAM 571 (3)  Linear and Integer Programming
    CAAM 564 (3) Numerical optimization
    Group 2:
    COMP 539 (4) Software Engineering Methodology
    COMP 522 (3) Multi-Core Computing
    COMP 530 (3) Introduction to Database Systems
    Group 3:
    STAT 518 (3) Probability
    STAT 519 (3) Statistical Inferences
    STAT 541 (3) Multivariate Analysis
    STAT 615 (3) Regression and Linear Models
    -7 electives, at least one must be from Communication,Leadership, Management and Ethics Group.
    Computationaland Applied Math
    CAAM 519 (3) Computational Science I
    CAAM 436 (3) Partial Differential Equations of MathematicalPhysics
    CAAM 536 (3) Numerical Methods for Partial DifferentialEquations
    …and other500 level or above courses listed on the Computationaland Applied Math department
    ComputerScience
    COMP 536 (3) High Performance Computer Architecture
    COMP 560 (4) Computer Graphics and Geometric Modeling
    COMP 528 (4) Computer Systems and Performance Analysis
    …and other500 level or above courses listed on the Computer Science department
    Statistics:
    STAT 605 (3) Statistical Computing and Graphics
    STAT 615 (3) Regression and Linear Models
    STAT 616 (3) Advanced Statistical Methods
    STAT 502 Neural Machine Learning
    …and other500 level or above courses listed on the Statisticsdepartment
    Communication,Leadership, Management and Ethics
    ENGI 610 (3) Management for Science and Engineering
    ENGI 510 (3) Technical and Managerial Communication
    ENGI 529 (3) Ethics and Engineering Leadership
                   ENGI 505(3) Engineering Project Development and Management
    ENGI 528 (3) Engineering Economics
    NSCI 521 (3) Writing and Publishing Science
    ENGI/LEAD 545 Structured Problem Solving
    ENGI 614 (3) Learning how to innovate
    ENGI 615 (3) Leadership Coaching for Engineers
    UNIV 594 (3) Responsible Conduct of Research
    . 涓浜-涓夊垎-鍦帮紝鐙鍙戝竷
    Georgetown:
    CORE COURSES(REQUIRED)
    Thefive-course core is designed to give students an overview of the massivedata landscape (15 credits).
    ·        Introduction to Data Analytics  (ANLY-501)
    ·        Massive Data Fundamentals  (ANLY-502)
    ·        Scientific and Analytical Visualization  (ANLY-503)
    ·        Probabilistic Modeling and Statistical Computing  (ANLY-511)
    ·        Statistical Learning for Analytics  (ANLY-512)
    ELECTIVES
    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).
    ·        Effective Written and Oral Presentation forTechnology & Science  (ANLY-520)
    ·        Databases  (ANLY-531)
    ·        Optimization  (ANLY-561)
    ·        Computational Methods for Data Analytics  (ANLY-562)
    ·        ANLY Internship  (ANLY-905)
    ·        Bioinformatics for Omics Data  (BIST-532)
    ·        Intro to Social Network Analysis  (CCTP-696)
    ·        Image Processing  (COSC-455)
    ·        Information Retrieval  (COSC-488)
    ·        Data Privacy  (COSC-531)
    ·        Advanced Algorithms  (COSC-540)
    ·        Natural Language Processing  (COSC-572)
    ·        Statistical Machine Learning  (COSC-578)
    ·        Knowledge Discovery and Data Mining  (COSC-585)
    ·        Text Mining and Analysis  (COSC-586)
    ·        Web Search and Sense-making  (COSC-589)
    ·        Mathematics of Climate  (MATH-412)
    ·        Mathematics of Social Networks  (MATH-442)
    ·        Deterministic Mathematical Models  (MATH-502)
    ·        Stochastic Simulation  (MATH-611)
    ·        Introduction to Operations Research  (MATH-615)
    ·        Sparse Repre & Random Sampling  (MATH-623)
    ·        Bayesian Statistics  (MATH-640)
    ·        Time Series Analysis  (MATH-645)
    ·        Regression Methods & Generalized Linear Models (MATH-651)
    ·        Data Mining  (MATH-656)
    .1point3acres缃
    ·        Categorical Data Analysis  (MATH-657)


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