宾夕法尼亚大学数据科学硕士专业

随着移动互联网和智能终端的普及，引发了数据迅速增长，而海量的数据蕴含着巨大的生产力和商机，而国内的数据分析人才的供给指数最低，仅仅为0.05，属于高度稀缺，而美国很多大学都开设了数据科学专业，其中最出色的就是宾夕法尼亚大学，下面，小编就带大家深入了解宾夕法尼亚大学数据科学硕士专业，希望对大家有所帮助：

MSE IN DATA SCIENCE

数据科学理学硕士(MSE)为学生准备了广泛的以数据为中心的职业，无论是在技术和工程、咨询、科学、决策，或理解文学、艺术或传播的模式。

课程设置：

基础课程

Programming Languages & Techniques (PL): Programming Languages & Techniques (CIT 590) or Introduction to Software Development (CIT 591)

Linear Algebra (CIS 515) OR Computational Linear Algebra (Math 513)

核心课程

Statistics for Data Science (ESE 542)

Big Data Analytics: Big Data Analytics (CIS 545)

Mining and Learning: Intro to Machine Learning (CIS 519) or Machine Learning (CIS 520) or Modern Data Mining (STAT 571) or Data-driven Modeling and Probabilistic Scientific Computing (ENM 531) or Data Mining: Learning from Massive Datasets (ESE 545)

技术和深度领域选修课

Applications

A. TitleThesis/Practicum

B. Biomedicine

Brain-Computer Interfaces (BE 521)

Network Neuroscience (BE 566)

Introduction to Computational Biology and Biological Modeling (CIS 536)

Biomedical Image Analysis (CIS 537)

Theoretical and Computational Neuroscience (PHYS 585)

C. Social/Network Science

Econometrics I- Fundamentals (ECON 705)

Econometrics III: Advanced Techniques of Cross-Section Econometrics (ECON 721)

Econometrics IV: Advanced Techniques of Time-Series Econometrics (ECON 722)

Applied Probability Models in Marketing (MKTG 776)

Methods

D. Data-centric Programming

Software Systems (CIS 505)

Databases (CIS 550)

Advanced Programming (CIS 552)

Internet and Web Systems (CIS 555)

Programming and Problem Solving (CIS 559)

Software Engineering (CIS 573)

Computer Systems Programming (CIT 595)

E. Surveys and Statistical Methods

Forecasting Methods for Management (STAT 535)

Accelerated Regression Analysis (STAT 621)

Predictive Analytics for Business (STAT 722)

Sample Survey Methods (STAT 920)

Observational Studies (STAT 921)

Modern Regression for the Social, Behavioral and Biological Science (STAT 974)

F . Data Analysis, Artificial Intelligence

Artificial Intelligence (CIS 521)

Deep Learning for Data Science (CIS 522)

Computational Linguistics (CIS 530)

Machine Perception (CIS 580)

Computer Vision (CIS 581)

Advanced Topics in ML (CIS 620)

Advanced Topics in Computer Vision (CIS 680)

Principles of Deep Learning (ESE 546)

Learning in Robotics (ESE 650)

Modern Data Mining (STAT 571)

G. Simulation Methods for Natural Science/Engineering

Molecular Modeling and Simulations (CBE 525)

Computational Science of Energy and Chemical Transformations (CBE 544)

Multi Modeling of Biological Systems (CBE 559)

Finite Element Analysis (MEAM 527)

Computational Mechanics (MEAM 646)

Atomic Modeling in Materials Science (MSE 561)

H. Mathematical and Algorithmic Foundations

Advanced Linear Algebra (AMCS 514)

Algorithms (CIS 502)

Computational Learning Theory (CIS 625)

Randomized Algorithms (CIS 677)

Algorithms & Computation (CIT 596)

Numerical Methods (ENM 502)

Data-driven Modeling and Probabilistic Scientific Computing (ENM531)

Introduction to Optimization Theory (ESE 504)

Data Mining: Learning from Massive Datasets (ESE 545)

Simulation Modeling and Analysis (ESE 503)

Convex Optimization (ESE 605)

Information Theory (ESE 674)

Bayesian Statistical Theory and Methods (STAT 927)

申请条件：

在宾夕法尼亚大学本科或研究生阶段通过以下课程完成两门课程(概率论和编程)并取得a或更好的成绩:

1.概率论(概率论与统计ENM 503或概率论STAT 510或高级概率论数学546)。

2. 编程(编程语言与技术CIT 590或软件开发导论CIT 591)。

3.完成大数据分析CIS 545或机器学习导论CIS 519且成绩至少a。招生委员会也会考虑申请人在数学(如线性代数、数学建模、优化)和编程方面的任何额外背景。在申请人原硕士课程第一学期所修课程的成绩公布后，转学申请将不被考虑。对于直接申请数据科学项目的候选人，申请的评估标准与此相同，即候选人应该有相同的课程背景、学术证书、考试成绩等。

个人陈述必须清楚地解释候选人为什么希望转入数据科学专业，以及他们的背景如何帮助他们做好准备。

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