美国密歇根大学安娜堡分校数据科学硕士专业 带你挖掘大数据背后的商业价值!
在大数据时代之下，各行各业都产生了大量的数据，据统计，2023年我国大数据市场规模超过了8000亿元，我国有望成为世界第一数据资源大国，但国内数据人才培养十分匮乏，而美国很多大学都相继开设了数据科学专业，这不美国密歇根大学安娜堡分校就开设了数据科学硕士专业，下面，就随小编来看看吧，希望对大家有所帮助：
MS in Data Science
学习该课程学生将能够:识别相关数据集，对数据集应用适当的统计和计算工具，以回答个人、组织或政府机构提出的问题，设计和评估适合于数据的分析程序，并在多计算机环境中有效地实现这些大型异构数据集。
课程设置：
核心课程
MATH 403: Introduction to Discrete Mathematics
EECS 402: Programming for Scientists and Engineers
EECS 403: Data Structures for Scientists and Engineers
下列选一项
BIOSTATS 601: Probability and Distribution
STATS 425: Introduction to Probability
STATS 510: Probability and Distribution
下列选一项
BIOSTATS 602: Biostatistical Inference
STATS 426: Introduction to Theoretical Statistics
STATS 511: Statistical Inference
所有学生必须修习以下核心课程:
EECS 409: Data Science Colloquium
数据管理和操作
下列选一项
EECS 484: Database Management Systems
EECS 584: Advanced Database Systems
下列选一项
EECS 485: Web Systems
EECS 486: Information Retrieval and Web Search
EECS 549/SI 650: Information Retrieval
SI 618: Data Manipulation Analysis
STATS 507: Data Science Analytics using Python
数据科学技术
下列选一项
BIOSTAT 650: Applied Statistics I: Linear Regression
STATS 500: Statistical Learning I: Linear Regression
STATS 513: Regression and Data Analysis
下列选一项
STATS 415: Data Mining and Statistical Learning
STATS 503: Statistical Learning II: Multivariate Analysis
EECS 505: Computational Data Science and ML
EECS 545: Machine Learning
EECS 476: Data Mining
EECS 576: Advanced Data Mining
SI 670: Applied Machine Learning
SI 671: Data Mining: Methods and Applications
BIOSTAT 626: Machine Learning for Health Sciences
Capstone
STATS 504: Principles and Practices in Effective Statistical Consulting
STATS 750: Directed Reading
EECS 599: Directed Study
SI 599-00X: Computational Social Science
SI 691: Independent Study
SI 699-004: Big Data Analytics
BIOSTAT 610: Reading in Biostatistics
BIOSTAT 629: Case Studies for Health Big Data
BIOSTAT 698: Modern Statistical Methods in Epidemiologic Studies
BIOSTAT 699: Analysis of Biostatistical Investigations
选修课
数据科学原理
BIOSTAT 601 (Probability and Distribution Theory)
BIOSTAT 602 (Biostatistical Inference)
BIOSTAT 617 (Sample Design)
BIOSTAT 626 (Machine Learning for Health Sciences)
BIOSTAT 680 (Stochastic Processes)
BIOSTAT 682 (Bayesian Analysis)
EECS 501 (Probability and Random Processes)
EECS 502 (Stochastic Processes)
EECS 551 (Matrix Methods for Signal Processing, Data Analysis and Machine Learning)
EECS 553 (Theory and Practice of Data Compression)
EECS 559 (Optimization Methods for SIPML)
EECS 564 (Estimation, Filtering, and Detection)
SI 670 (Applied Machine Learning)
STATS 451 (Introduction to Bayesian Data Analysis)
STATS 470 (Introduction to Design of Experiments)
STATS 510 (Probability and Distribution Theory)
STATS 511 (Statistical Inference)
STATS 551 (Bayesian Modeling and Computation)
数据分析
BIOSTAT 645 (Time series)
BIOSTAT 651 (Generalized Linear Models)
BIOSTAT 653 (Longitudinal Analysis)
BIOSTAT 665 (Population Genetics)
BIOSTAT 666 (Statistical Models and Numerical Methods in Human Genetics)
BIOSTAT 675 (Survival Analysis)
BIOSTAT 685 (Non-parametric statistics)
BIOSTAT 695 (Categorical Data)
BIOSTAT 696 (Spatial statistics)
EECS 556 (Image Processing)
EECS 559 (Advanced Signal Processing)
EECS 659 (Adaptive Signal Processing)
STATS 414 (Topics in Applied Data Analysis
STATS 501 (Statistical Analysis of Correlated Data)
STATS 503 (Statistical Learning II: Multivariate Analysis)
STATS 509 (Statistics for Financial Data)
STATS 531 (Analysis of Time Series)
STATS 600 (Linear Models)
STATS 601 (Analysis of Multivariate and Categorical Data)
STATS 605 (Advanced Topics in Modeling and Data Analysis)
STATS 700 (Topics in Applied Statistics)
计算
BIOSTAT 615 (Statistical Computing)
BIOSTATS 625 (Computing with Big Data)
EECS 481 (Software Engineering)
EECS 485 (Web Systems)
EECS 486 (Information Retrieval and Web Search)
EECS 490 (Programming Langiages)
EECS 493 (User Interface Development)
EECS 504 (Computer Vision)
EECS 542 (Advanced Topics in Computer Vision)
EECS 549/SI 650 (Information Retrieval)
EECS 548/SI 649 (Information Visualization)
EECS 586 (Design and Analysis of Algorithms)
EECS 587 (Parallel Computing)
EECS 592 (Artificial Intelligence)
EECS 595/SI 561 (Natural Language Processing)
SI 608 (Networks)
SI 618 (Data Manipulation and Analysis
SI 630 (Natural Language Processing (Algorithms and People)
SI 671 (Data Mining: Methods and Applications)
STATS 406 (Computational Methods in Statistics and Data Science)
STATS 507 (Data Science Analytics using Python)
STATS 506 (Computational Methods and Tools in Statistics)
STATS 606 (Statistical Computing)
STATS 608 (Monte Carlo Methods and Optimization Methods in Statistics)
申请条件：
申请者来自不同的本科专业，包括统计、数学、计算机科学、物理、工程、信息和数据科学。虽然不需要数据科学本科专业，但预计申请者在加入前至少需要具备以下背景:
2个学期的大学微积分
1学期的线性代数或矩阵代数
计算机课程简介
语言要求：
托福总分达到100分以上，阅读听力达到23分以上，口语写作达到21分以上;
雅思总分达到7分，单项达到6.5以上
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