卡内基梅隆大学计算数据科学硕士专业 让你成为挖掘数据的开发者!
身处在大数据时代之下，各行各业都依靠数据来进行商业运作，从而对计算数据科学人才的需求量与日俱增，可国内目前鲜少有大学开设此专业，而美国很多大学都相继开设了数据科学专业，其中最出色的就是卡内基梅隆大学，下面，小编就带大家深入了解卡内基梅隆大学计算数据科学硕士专业，希望对大家有所帮助：
Master of Computational Data Science
该项目(成立于2004年，作为超大型信息系统的MSIT)培养专业硕士学生在超大型信息系统的设计、工程和部署的各个方面。在课程中，你将深入研究数据库、分布式算法和存储、机器学习、语言技术、软件工程、人机交互和设计等主题。通过核心课程和选修课，您将对大型信息系统有一个统一的认识，而我们的实习和顶点项目要求确保您拥有在职业生涯中获得成功所需的知识和经验。
课程设置：
Common Core
The following four (4) core courses are to be completed by all students during their first two semesters:
15-619 Cloud Computing
10-601 Machine Learning
05-839 Interactive Data Science
11-631 Data Science Seminar
Systems Concentration
15-605 Operating Systems Implementation
15-618 Parallel Computer Architecture & Programming
15-640 Distributed Systems
15-641 Computer Networks
15-645 Database Systems
15-712 Advanced and Distributed Operating Systems
15-719 Advanced Cloud Computing
15-721 Advanced Databases
15-746 Advanced Storage Systems
15-821 Mobile and Pervasive Computing
36-702 Statistical Machine Learning
36-705 Intermediate Statistics
36-725 Convex Optimization
Analytics Concentration
10-608 Conversational Machine Learning
10-701 Introduction to Machine Learning (PhD)
10-703 Deep Reinforcement Learning & Control
10-708 Probabilistic Graphical Models
10-715 Advanced Intro to Machine Learning
10-725 Convex Optimization
10-805 Machine Learning with Big Data Sets
11-641 Machine Learning for Text Mining
11-661 Language and Statistics
11-727 Computational Semantics for NLP
11-741 Machine Learning for Text Mining
11-747 Neural Networks for NLP
11-755 Machine Learning for Signal Processing
11-761 Language and Statistics
11-763 Structured Prediction
11-777 Advanced Multimedia Machine Learning
11-785 Intro to Deep Learning
Choose one course in Software Systems:
11-642 Search Engines
11-747 Neural Networks for NLP
11-775 Large-Scale Multimedia Analysis
11-777 Advanced Multimedia Machine Learning
11-791 Design & Engineering of Intelligent Information Systems
11-792 Intelligent Systems Project
11-797 Question Answering
Choose one course with a focus on Big Data:
10-605 Machine Learning with Big Data Sets
10-805 Machine Learning with Big Data Sets
11-775 Large-Scale Multimedia Analysis
Human-Centered Data Science (HCDS) Concentration
Choose one course in Behavioral Research Methods:
05-816 Applied Research Methods
94-834 Applied Econometrics I & II
Choose two courses in HCI Methods:
05-821 Social Web
05-823 E-Learning Design Principles and Methods
05-833 Applied Gadgets, Sensors & Activity Recognition
05-836 Usable Privacy and Security
05-840 Tools for On-Line Learning
05-872 Rapid Prototyping of Computer Systems
05-891 Designing Human Centered Systems
05-899 Crowd Programming
05-899 Learning Analytics & Educational Data Science
05-899 Special Topics in HCI: Sensemaking
05-899 Design of Large-Scale Peer Learning Systems
05-899 Learning With Peers at Massive Scale
05-899 Mobile Health
入学要求：
GPA 3.0或更高。
GRE成绩:必须少于5年。
申请材料：
简历，请提交你目前的简历，概述你的教育，研究经历，工作经历，出版，奖学金，所获奖项和荣誉，社会成员。
目的陈述，准备一或两页的文章，描述你的主要兴趣领域的研究，你的相关经验，和你在卡内基梅隆大学攻读研究生学位的目标
三封推荐信。
你就读的每所大学的正式成绩单，无论你是否在那里获得了学位。
语言要求：
新托福考试成绩至少为100分。
综上所述，以上讲的就是关于卡内基梅隆大学计算数据科学硕士专业的相关问题介绍，希望能给各位赴美留学的学子们指点迷津。近年来，赴美留学一直是广大学生最热门的话题，同时，很多学生对于签证的办理、院校的选择、就业的前景、学习的费用等诸多问题困扰不断，别担心，IDP留学专家可以为你排忧解难，同时，更多关于赴美留学的相关资讯在等着你，绝对让你“浏览”忘返。在此，衷心祝愿各位学子们能够顺利奔赴自己心目中理想的学校并且学业有成!
