哥伦比亚大学ds硕士课程列表_托普仕留学Document

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哥伦比亚大学ds硕士课程列表

上传时间:2021-05-12浏览量:181

摘要:哥伦比亚大学ds硕士课程列表常见核心课程和选修两部分组成,共计学分达到30个,核心课程达到7门!下面不妨赶紧跟随托普仕留学Hanna老师一起去看看具体情况吧!

  一、哥伦比亚大学ds硕士课程列表(内附学分修读要求)

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  哥伦比亚大学ds硕士开设在数据科学学院下面,提供硕士和博士课程,主要培养学生在数据领域的分析能力,所需课程包括计算机科学、工程和统计分析等领域的7门核心课程,加上选修课程,合计在读学生需要学分达到30个!

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  1、核心必修课程:21个学分核心课程

  计算机科学领域课程:

  COMS W4121 Computer Systems for Data ScienceSpring Semester: 3个学分

  COMS W4721 Machine Learning for Data ScienceSpring Semester: 3个学分

  CSOR W4246 Algorithms for Data ScienceFall Semester: 3个学分

  工程领域课程:

  ENGI E4800 Data Science Capstone and Ethics

  统计领域课程:

  STAT GR5701 Probability and Statistics for Data Science:秋季学期r: 3个学分

  STAT GR5702 Exploratory Data Analysis and Visualization:秋季学期r: 3个学分

  STAT GR5703 Statistical Inference and Modeling:秋季和春季学期r: 3个学分

  2、选修课程:

  除了21个核心课程学分外,数据科学专业的理学硕士还必须完成至少9个选修学分。除了DSI选修课程外,还鼓励MS学生探索整个大学开设的课程,并利用哥伦比亚大学广泛学科领域的专业知识。

  COMS W4995 Topics in Computer Science: Applied Machine Learning

  COMS W4995 Topics in Computer Science: Applied Deep Learning

  COMS W4995 Topics in Computer Science: Causal Inference for Data Science

  COMS W4995 Topics in Computer Science: Data Analytics Pipeline

  COMS W4995 Topics in Computer Science: Elements of Data Science

  COMS E6998 Topics in Computer Science: Machine Learning with Probabilistic Programming

  COMS E6998 Natural Language Processing: Computational Models of Social Meaning

  EECS E6894 Topics in Information Processing: Deep Learning for Computer Vision, Speech, and Language

  IEOR E4571 Topics in Operations Research: Personalization Theory & Application

  IEOR E4721 Topics in Quantitative Finance: Big Data in Finance

  STATS GR5293 Topics in Modern Statistics: Applied Machine Learning for Financial Modeling and Forecasting

  STATS GR5293 Topics in Modern Statistics: Applied Machine Learning for Image Analysis

  二、哥伦比亚大学开设DS硕士专业的学院

  1、Data Science Institute:

  M.S. in Data Science (with Graduate School of Arts and Sciences and Fu Foundation School of Engineering and Applied Science)

  Ph.D. Specialization in Data Science (with Applied Mathematics, Computer Science, Electrical Engineering, Industrial Engineering and Operations Research, and Statistics departments)

  Certification of Professional Achievement in Data Sciences (with Graduate School of Arts and Sciences and The Fu Foundation School of Engineering and Applied Science)

  DSI Scholars Program

  Columbia University AI4ALL Summer Program (with School of Social Work)

  DSI I-Cubed Lectures

  2、Columbia Business School:

  M.S. in Business Analytics (with Fu Foundation School of Engineering and Applied Science)

  Technology, Coding and Analytics (with Fu Foundation School of Engineering and Applied Science)

  Fu Foundation School of Engineering and Applied Science:

  M.S. in Business Analytics (with Columbia Business School)

  M.S. in Data and Computational Sciences in Chemical Engineering

  B.A. in Data Science (with School of General Studies)

  Technology, Coding and Analytics (with Columbia Business School)

  Columbia University AI4ALL Summer Program (with Data Science Institute and School of Social Work)

  MicroMasters in Business Analytics

  Data Analytics Bootcamp

  3、Graduate School of Arts and Sciences:

  M.A. in Statistics

  Graduate School of Journalism

  M.S. in Data Journalism

  Mailman School of Public Health

  M.S. in Biostatistics, Public Health Data Science Track

  Certificate in Applied Biostatistics

  SHARP Training Series

  4、School of General Studies:

  B.A. in Data Science (with Fu Foundation School of Engineering and Applied Science)

  School of Professional Studies:

  M.S. in Applied Analytics

  Certification of Professional Achievement in Sustainability Analytics

  DataCamp

  5、School of Social Work:

  Minor Studies in Emerging Technology, Media and Society

  Columbia University AI4ALL Summer Program (with Data Science Institute and Fu Foundation School of Engineering and Applied Science)

  6、Teachers College:

  M.S. in Learning Analytics

  7、Columbia Research:

  Distinguished Lectures in Computational Innovation

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