The PhD 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 PhD program. Students should discuss this specialization option with their PhD advisor and their department's director for graduate studies.
Specialization Details
Credit Requirements
Specialization Requirements
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.
Approved Courses
Innovative and Cutting-Edge Curriculum
- 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
- IEOR E6613 Optimization I
- IEOR E6614 Optimization II
- IEOR E6711 Stochastic Modeling I
- EEOR E6616 Convex Optimization
- 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)
