The Master of Science in Data Science allows students to apply data science techniques to their field of interest, building on four foundational courses offered in our Certification of Professional Achievement in Data Sciences program. Our students have the opportunity to conduct original research, included in a capstone project, and interact with our industry partners and faculty. Students may also choose an elective track focused on entrepreneurship or a subject area covered by one of our eight centers.
This program is jointly offered in collaboration with the Graduate School of Arts & Science' Department of Statistics, and The Fu Foundation School of Engineering & Applied Science's Department of Computer Science and Department of Industrial Engineering & Operations Research.
- Undergraduate degree
- Prior quantitative coursework (calculus, linear algebra, etc.)
- Prior introductory computer programming coursework
WHO SHOULD APPLY?
Individuals looking to strengthen their career prospects or make a career change by developing in-depth expertise in data science.
- Uploaded transcripts from every post-secondary institution attended
- Three recommendation letters
- Personal statement
- Curriculum vitae / resumé
Official *Graduate Record Examination (GRE) general test scores
(*GMAT may not replace the GRE test requirement)
- $85 non-refundable application fee
- TOEFL, IELTS or PTE Academic test scores, if applicable
We routinely offer a number of online information sessions and other recruiting events, please [Click Here]. To learn more about the admissions application requirements and to submit your application, please visit the Graduate Engineering Admissions page.
The priority deadline for Fall application submission is February 15th. [Apply Here]
TUITION AND FEES
Students enrolled in the Master of Science program pay Columbia Engineering's rate of tuition. Tuition and fees are prescribed by statute and are subject to change at the discretion of the Trustees. For more information on rates of tuition and other applicable fees, refer to Student Financial Services and the Columbia Engineering Bulletin.
Candidates for the Master of Science in Data Science are required to complete a minimum of 30 credits, including 21 credits of required/core courses and 9 credits of electives. This program may be pursued part-time or full-time. Most students enroll on a full-time status, completing the program in three semesters/one and a half years (Fall: 12-credits; Spring: 12-credits; Summer: Optional Internship or elective; Fall: final 3- or 6-credits).
For the most up-to-date course offering and schedule information refer to COURSES.
STAT GR5701 PROBABILITY AND STATISTICS FOR DATA SCIENCE*
CSOR W4246 ALGORITHMS FOR DATA SCIENCE
STAT GR5703 STATISTICAL INFERENCE AND MODELING
COMS W4121 COMPUTER SYSTEMS FOR DATA SCIENCE
COMS W4721 MACHINE LEARNING FOR DATA SCIENCE
STAT GR5702 EXPLORATORY DATA ANALYSIS AND VISUALIZATION
ENGI E4800 DATA SCIENCE CAPSTONE AND ETHICS
Nine (9) credits of elective courses should be drawn upon existing graduate level courses at Columbia University. In addition to advisor approval, elective course selection will be subject to course prerequisites, course availability, and the cross-registration procedures of the school/department offering the requested courses.
COMS E6910x and y FIELDWORK
1 pt. Members of the faculty.
Prerequisites: Obtained internship and approval from Professor Eleni Drinea. Only for M.S. students in the Computer Science Department (and MS in Data Science program) who need relevant work experience as part of their program of study. Final report required. This course may not be taken for pass/fail credit or audited. For more information visit http://www.cs.columbia.edu/education/ms/cpt.
*Please note that this course reflects the change to the required statistics coursework for the MS students entering the program in Fall 2018. Prior to Fall 2018, the MS in Data Science program required that students complete Probability Theory STAT GU4203. The changes to GR5701 as well as the curriculum requirement for Masters students were approved by the DSI Curriculum Committee.