更新時(shí)間：2024-10-06 15:50:11作者：留學(xué)之路
哥倫比亞大學(xué)ds碩士開設(shè)在數(shù)據(jù)科學(xué)學(xué)院下面，提供碩士和博士課程，主要培養(yǎng)學(xué)生在數(shù)據(jù)領(lǐng)域的分析能力，所需課程包括計(jì)算機(jī)科學(xué)、工程和統(tǒng)計(jì)分析等領(lǐng)域的7門核心課程，加上選修課程，合計(jì)在讀學(xué)生需要學(xué)分達(dá)到30個(gè)!
1、核心必修課程：21個(gè)學(xué)分核心課程
計(jì)算機(jī)科學(xué)領(lǐng)域課程：
COMS W4121 Computer Systems for Data ScienceSpring Semester: 3個(gè)學(xué)分
COMS W4721 Machine Learning for Data ScienceSpring Semester: 3個(gè)學(xué)分
CSOR W4246 Algorithms for Data ScienceFall Semester: 3個(gè)學(xué)分
工程領(lǐng)域課程：
ENGI E4800 Data Science Capstone and Ethics
統(tǒng)計(jì)領(lǐng)域課程：
STAT GR5701 Probability and Statistics for Data Science：秋季學(xué)期r: 3個(gè)學(xué)分
STAT GR5702 Exploratory Data Analysis and Visualization：秋季學(xué)期r: 3個(gè)學(xué)分
STAT GR5703 Statistical Inference and Modeling：秋季和春季學(xué)期r: 3個(gè)學(xué)分
2、選修課程：
除了21個(gè)核心課程學(xué)分外，數(shù)據(jù)科學(xué)專業(yè)的理學(xué)碩士還必須完成至少9個(gè)選修學(xué)分。除了DSI選修課程外，還鼓勵(lì)MS學(xué)生探索整個(gè)大學(xué)開設(shè)的課程，并利用哥倫比亞大學(xué)廣泛學(xué)科領(lǐng)域的專業(yè)知識(shí)。
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
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