Ph.D. Specialization in Data Science
The Ph.D. specialization in data science is an option within the Applied Mathematics, Computer Science, Electrical Engineering, Industrial Engineering and Operations Research, and Statistics departments.
Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization. Students should fulfill the requirements below in addition to those of their respective department's Ph.D. program. Students should discuss this specialization option with their Ph.D. advisor and their department's director for graduate studies.
Applied Mathematics Doctoral Program
Computer Science Doctoral Program
Decision, Risk, and Operations (DRO) Program
Electrical Engineering Doctoral Program
Industrial Engineering and Operations Research Doctoral Program
The specialization consists of either five (5) courses from the lists below, or four (4) courses plus one (1) additional course approved by the curriculum committee. All courses must be taken for a letter grade and students must pass with a B+ or above. At least three (3) of the courses should come from outside the student’s home department. At least one (1) course has to come from each of the three (3) thematic areas listed below.
Specialization Requirements
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- COMS 4231 Analysis of Algorithms I
- COMS 6232 Analysis of Algorithms II
- COMS 4111 Introduction to Databases
- COMS 4113 Distributed Systems Fundamentals
- EECS 6720 Bayesian Models for Machine Learning
- COMS 4771 Machine Learning
- COMS 4772 Advanced Machine Learning
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- IEOR E6613 Optimization I
- IEOR E6614 Optimization II
- IEOR E6711 Stochastic Modeling I
- EEOR E6616 Convex Optimization
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- STAT 6301 Probability Theory I
- STAT 6201 Theoretical Statistics I
- STAT 6101 Applied Statistics I
- STAT 6104 Computational Statistics
- STAT 5224 Bayesian Statistics
- STCS 6701 Foundations of Graphical Models (joint with Computer Science)Â
Information Request Form
Ph.D. Specialization Committee
David Blei
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Faculty of Arts and Sciences
Professor of Statistics -
The Fu Foundation School of Engineering and Applied Science
Professor of Computer Science
Richard A. Davis
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Faculty of Arts and Sciences
Howard Levene Professor of Statistics
Vineet Goyal
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The Fu Foundation School of Engineering and Applied Science
Associate Professor of Industrial Engineering and Operations Research
Garud N. Iyengar
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Data Science Institute
Avanessians Director of the Data Science Institute -
The Fu Foundation School of Engineering and Applied Science
Professor of Industrial Engineering and Operations Research
Gail Kaiser
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The Fu Foundation School of Engineering and Applied Science
Professor of Computer Science
Rocco A. Servedio
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The Fu Foundation School of Engineering and Applied Science
Professor of Computer Science
Clifford Stein
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The Fu Foundation School of Engineering and Applied Science
Wai T. Chang Professor of Industrial Engineering and Operations Research and Professor of Computer Science
John Wright
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The Fu Foundation School of Engineering and Applied Science
Associate Professor of Electrical Engineering
Ming Yuan
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Data Science Institute
Associate Director for Research -
Faculty of Arts and Sciences
Professor of Statistics