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They empower fundamental new understandings of biological mechanisms with far-reaching applications to biological and medical sciences. To fulfill this promise, new computational techniques are needed to analyze genome sequences, protein structures, metabolic and regulatory pathways, evolutionary patterns, and the genetic basis of disease. The computational biology track seeks to provide state of the art understanding of this concomitant growth of high-throughput experimental techniques, computational techniques to analyze their data, the resulting new understandings of biological mechanisms, and their applications to pharmacological and medical practice (from diagnosis to drug design).

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SUMMARY OF REQUIREMENTS

• Complete a total of 30 points (Courses must be at the 4000 level or above)
• Maintain at least a 2.7 overall GPA. (No more than 1 D is permitted).
• Complete the Columbia Engineering Professional Development & Leadership (PDL) requirement
• Satisfy breadth requirements
• Take at least 6 points of technical courses at the 6000 level
• At most, up to 3 points of your degree can be Non-CS/Non-track If they are deemed relevant to your track and sufficiently technical in nature. Please submit the course syllabus to your CS Faculty Advisor for review, and then forward the approval confirmation email to ms-advising@cs.columbia.edu

1. Breadth Courses

Visit the breadth requirement page for more information.

2. Required Track Courses

Students are required to complete two required courses (6 points): One course from either COMS W4761 (Computational Genomics) or COMS W4762 Machine Learning for Functional Genomics and one course from either COMS W4771 or SIEO W4150/IEOR W4150/*STAT 4001. Students who have taken equivalent courses in the past and received grades of at least a B may apply for waivers and take other CS courses instead.

Any course option taken but not used towards the Required Course can be counted as an elective.

Students must take one of the following courses:

Course ID	Title
COMS W4761	Computational Genomics
COMS W4762	Machine Learning for Functional Genomics

Students must take one of the following courses:

Course ID	Title
COMS W4771 or STAT 4400	Machine Learning or Statistical Machine Learning
STAT 4001/IEOR W4150*	Probability and Statistic

* STAT 4001 has replaced SIEO W4150. SIEO W4150 taken before Spring 2024 may be used to fulfill this requirement. 

3. Track Electives

Students are required to take two courses from the following list, at least one of which must be a 6000-level course. Other courses on this list may be used as general electives or waiver replacements when the student has received a waiver.

Course ID	Title
COMS W4111	Introduction to Databases
COMS W4252	Introduction to Computational Learning Theory
COMS W4772 (E6772)	Advanced Machine Learning
COMS W4995	Visit the topics courses page to see which COMS 4995 courses apply to this track.
COMS E6111	Advanced Database Systems
COMS E6901	Projects in Computer Science (Advisor approval required)
COMS E6998	Visit the topics courses page to see which COMS 6998 courses apply to this track.
CMBS 5305	Topics in Mathematical Genomics and Biology
BIOC W4512	Molecular Biology
BIOL W4031	Genetics I
BIOL W4032	Genetics II
BIOL W5031	Genetics (This replaces BIOL 4031 and BIOL 4032. Courses cannot be double counted.)
BIOL W4034	Biotechnology
BIOL W4037	Bioinformatics of Gene Expression
BIOL W4041	Cell Biology
BIOL W4070	The Biology and Physics of Single Molecules
BIOL W4300	Drugs and Disease
BIOL W4073	Cellular and Molecular Immunology
BIOL W4400	Biological Networks
BIOL W4510	Genomics of Gene Regulation
BIOL G6560	Human Evolutionary Genetics
BCHM G4026	Biochemistry of Nucleic and Protein Synthesis
BCHM G4250	Biochemistry and Molecular Biophysics
BCHM G6300	Biochemistry and Molecular Biology of Eukaryotes I
BCHM G6301	Biochemistry and Molecular Biology of Eukaryotes II
BMEN E6480	Computational Neural Modeling and Neuroengineering
GEND G4050	Advanced Eukaryotic Molecular Genetics
STAT G6101	Statistical Modeling and Data Analysis
APMA E4400	Introduction to Biophysical Modeling
BINF G4006	Translational Bioinformatics
BINF G4014	Computational Biology I: Functional and Integrative Genomics
BINF G4015	Computational Systems Biology: Proteins, Networks, Function
BINF G4017	Deep Sequencing
BINF G6001	Projects in Biomedical Informatics
ECBM E4060	Intro to Genomic Info Sci & Tech
EECS E6720	Bayesian models for Machine Learning
EECS E6894	Deep Learning for Computer Vision and Natural Language Processing

4. General Electives

Students must complete the remaining credits of General Elective Courses at the 4000 level or above. At least three of these points must be chosen from either the Track Electives listed above or from the CS department at the 4000 level or higher.

Students may also request to use at most 3 points of Non-CS/Non-Track coursework if approved by the process listed below.

• At most, up to 3 points of your degree can be Non-CS/Non-track If they are deemed relevant to your track and sufficiently technical in nature. Please submit the course syllabus to your CS Faculty Advisor for review, and then forward the approval confirmation email to ms-advising@cs.columbia.edu

Please note:

• At least 3 elective points must be selected from courses in biological departments

• Students who waive track requirements by using previous courses must still complete 30 graduate credits. This can be done by expanding their elective selection to include courses listed as required track courses and elective track courses; or by taking other graduate courses

• Due to a significant overlap in course material, MS students not in the Machine Learning track can only take 1 of the following courses – COMS 4771, ELEN 4903, IEOR 4525, STAT 4240, STAT 4400/4241/5241 – as part of their degree requirements

• The Degree Progress Checklist should be used to keep track of your requirements. If you have questions for your Track Advisor or CS Advising, you should have an updated Checklist prepared

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TRACK PLANNING

Please visit the Directory of Classes to get the updated course listings. Please also note that not all courses are offered every semester or even every year. A few courses are offered only once every two or three years or even less frequently.

Please note that some Data Science Institute courses, such as COMS/CSEE W4121 (Computer Systems for Data Science), do not count towards the CS MS degree. If you have any questions, please contact your advisor or CS Advising.

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Updated: 09/04/2024