The Biomedical Informatics curriculum is designed to provide a uniform foundation in the essentials of the field while meeting the needs of a wide range of students with different backgrounds and career goals.
The educational objectives consist of core courses, which provide a foundation in general Biomedical Informatics methods, techniques, and theories, while the qualitative, quantitative, information technology, and domain objectives enable students to apply these methods to one or more areas of specialization in bioinformatics, clinical informatics, public health informatics, or translational informatics.
All students are required to take five Biomedical Informatics core courses. They are BINF G4000 Acculturation to Programming and Statistics (students may place out by means of a placement exam given on the first day of class by faculty teaching the course), BINF G4001 Introduction to Computer Application in Health Care and Biomedicine, BINF G4002 Computational Methods, and BINF G4003 Symbolic Methods. Students are required to take another core course which is either BINF G6002 Research Methods or BINF G4013 Biological Sequence Analysis depending on their area of study. In addition, students are required to take additional courses, which are qualitative, quantitative, and information technology objectives and domain specific courses. The domain specific areas comprise clinical, biological, translational, and public health courses. Curriculum requirements and distribution of objective and domain specific courses vary depending on the degree and area of specialization. In addition to the 5 department core courses, MA students are required to take 4 additional courses comprising the educational objectives and domain specific categories, and one research projects course (BINF G6001) , which will ultimately result in a Master’s Essay, the content of which is left to the discretion of the research advisor. In addition to the 5 department core courses, PhD students are required to take 5 additional courses comprising the educational objective and domain specific areas, one ethics course (CMBS G4010) and research courses (BINF G6001, BINF G9001) to support their dissertation work.
Students who meet courses in the educational objectives through prior graduate courses may not be required to take further courses in these areas as determined by their academic advisor or the graduate program director, but must fulfill the Columbia University requirement of the minimum number of points of Columbia University instruction and residence units required for the degree (60 points and 6 residence units for PhD students, 30 points and 2 residence units for MA students).
Comprehensive information on departmental policies and procedures for students is found in the DBMI Trainee Handbook. In the event of questions, contact the Graduate Program Manager.
Core Courses – 5 courses
Students must demonstrate competence in areas that serve as a building block for Biomedical Informatics by successfully completing relevant graduate level courses. A number of Biomedical Informatics courses are offered to meet the educational objectives. Students are also permitted to take courses in other departments with approval from their research or academic advisors or the graduate program director.
|BINF G4000 Acculturation to Programming and Statistics (Prof. Karthik Natarajan, fall)||See the Columbia University online Directory of Classes for time and semester offered.
BINF G4000 must be taken fall term of entry.
|BINF G4001 Introduction to Computer Applications in
Health Care & Biomedicine (Prof. Nicholas Tatonetti, fall)
|See the Columbia University online Directory of Classes for time and semester offered.
BINF G4001 must be taken fall term of entry.
|BINF G4003: Methods I: Symbolic Methods (Prof. Chunhua Weng, spring)||See the Columbia University online Directory of Classes for time offered.|
|BINF G4002: Methods II: Computational Methods (Prof. Noemie Elhadad, spring)||See the Columbia University online Directory of Classes for time and semester offered.|
|BINF G6002 Methods III: Research Methods (Prof. Olena Mamykina, fall) for Clinical, Public Health or Translational students||See the Columbia University online Directory of Classes for time and semester offered.|
|BINF G4013 Biological Sequence Analysis (Prof. Richard Friedman, spring) for Bioinformatics students||See the Columbia University online Directory of Classes for time and semester offered.|
|Examples of Objective trajectories for PhDs and Postdocs||– Data science: 2 quant, 1 IT, 2 domain
– Clinical (includes public health, intervention): 3 from a combination of Qual, Quant, IT and 2 from domain or 2 from a combination of Qual, Quant, IT (at least 1 course should be from Quant or IT) and 3 from domain
– Translational: 2 quant, 1 IT, 2 domain
– Bioinformatics: 2 quant, 1 IT, 2 domain
|Examples of Objective trajectories for MAs (similar but contains one less course):||– Data science: 2 from quant, 2 from domain
– Clinical (includes public health, intervention): 2 from a combination of Qual, Quant, IT (at least one course should be from Quant or IT) and 2 from domain
– Bioinformatics: 2 educational objectives, 2 domain
– Translational: 2 educational objectives, 2 domain
|Credits||– 20 credits max per term
– 1 credit seminar every year, except 1 term if course conflict
– 1 credit ethics taken P/F for PhDs and Postdoc MAs in Spring term of first year
– research credits for PhDs and Postdocs
o Year 1: 6 credits fall and spring term
o Year 2: 9 credits fall and spring term
o Year 3: 12 credits fall and spring term
Educational Objectives in Biomedical Informatics
|Core||Is familiar with problems, issues and applications in Biomedical Informatics, and is able to apply general theories and methods to solve problems.||BINF G4000 Acculturation to Programming & Statistics
BINF G4001 Introduction to Computer Applications in Health Care and Biomedicine
BINF G4002 Methods I: Computational Methods
BINF G4003 Methods II: Symbolic Methods
BINF G6002 Methods III: Research Methods or BINF G4015 Methods III: Computational Systems Biology: Proteins, Networks and Function (experimental methods) or BINF G4013 Biological Sequence Analysis or BINF G4006 Translational Bioinformatics
|Information Technology(IT)||Can apply computer science and statistical techniques to manage data, develop software and solve problems.||BIST P8105 Data Science
EECS E6893 Information Processing: Big Data Analytics
QMSS G4063 Data Visualization
COMS W4111 Introduction to Databases
COMS W4181 Security I
CSOR W4246 Algorithms for Data Science
COMS W4156 Advanced Software Engineering
CSOR W4231 Analysis of Algorithms
COMS W4444 Programming and Problem Solving
COMS E6111 Advanced Database Systems
COMS E6998 Cloud & Big Data
|Quantitative(Quant)||Can apply mathematical and computational techniques to analyze data and test hypotheses.||BINF G5001 Data Science for Mobile Health
HBSS 4199 or HBSS 4160 Introduction to Biostatistics (Teachers College – http://www.tc.columbia.edu/academics/resources/courses/)
QMSS G4063 Data Visualization
QMSS GR5058 Data Mining for Social Science
COMS W4705 Natural Language Processing
COMS W4771 Machine Learning
COMS W4772 Advanced Machine Learning or COMS E6898 Topics: Information Processing: From Data to Solutions
EECS E6720 Bayesian Models for Machine Learning
IEOR E4540 Data Mining
ELEN E4903 Machine Learning
STAT W4026 Applied Data Mining
STAT W4107 or STAT GU4204 Statistical Inference
STAT W4240 Data Mining
COMS W4761 Computational Genomics
COMS W4995 Applied Machine Learning
COMS W4995 Causal Inference for Data Science
STAT G6104 Applied Statistics
STAT G6509/GR6701 Foundations of Graphical Models
BIST P6104/P6114 Introduction to Biostatistical Methods
BIST P8110 Applied Regression II
BIST P8116 Design of Medical Experiments
BIST P8157 Longitudinal Data Analysis
BIST P9120 Topics in Statistical Learning and Data Mining
ELEN E6690 Statistical Learning for biological and information systems
EECS E6893 Big Data Analytics
IEOR4720 Deep Learning
COMS 6998 sec7 (FUND SPEECH RECOGNITION)
|Qualitative(Qual)||Can apply techniques that aid in the understanding of behavioral and social phenomena associated with health-related problems and with delivery of healthcare.||NURS N9352 Qualitative Research Design & Methods
COMS W4170 User Interface Design
BINF G6002 Research Methods (core for clinical and translational, but can count as Qualitative objective for data science track and non-postdoc MAs)
ORL 6500 Qualitative research methods in organizations: Design and data collection
ORL 6501 Qualitative research methods in organizations: Data analysis and reporting.
ORL 6518 Methods of case study and analysis.
ORLJ 4009 Understanding behavioral research
ORLJ 5018 Using survey research in organizational consulting
B9506-001 (PhD) Organizational behavior
|Domain||Is able to apply general methods and theories of informatics to one or more areas of specialization: data science, translational bioinformatics, clinical research informatics, clinical informatics, or public health informatics.||Clinical:
BINF G4004 Applied Clinical Information Systems
BINF G4005 Process Redesign in Complex Organizations
BINF G4011 Acculturation to Medicine and Clinical Informatics
BINF G5000 Quality in Health Care
BINF G5001 Data Science for Mobile Health
PATH G6003 Mechanisms in Human Disease
BINF G4006 Translational Bioinformatics
PATH G6003 Mechanisms in Human Disease
PHAR G8001 Principles of System Pharmacology
BIOT W4200 Biopharmaceutical Development & Regulation
COMS E6998 Computational Methods/High Throughput Sequencing
BINF G4013 Biological Sequence Analysis
BINF G4015 Computational Systems Biology
BINF G4016 Quantitative/Computational Aspects of Infectious Disease
BINF G4017 Deep Sequencing
COMS W4761 Computational Genomics
BIOL W4510 Genomics of Gene Regulation
BIST P8119 Advanced Stat/Comp Methods Genetics/Genomics
ELEN E6010 Design Principles for Biological Circuits
BIOL W4799 Molecular Biology of Cancer
BINF G4062 Public Health Informatics
EPID P6400/02 Epidemiology
EPID P8471 Social Epidemiology
SOSC P8795 New Media and Health
BIST P6530 Issues & Approaches in Health Policy & Management
EHSC P6385/6 Principles of Genetics and the Environment I and II
|Research||Can conduct independent research in Biomedical Informatics.Can formulate a hypothesis, design a suitable experiment, and carry it out with sensitivity to ethical standards.||BINF G6001 Projects in Biomedical Informatics
BINF G9001 Doctoral Research in Biomedical Informatics
CMBS G4010 Responsible Conduct of Research and Related Policy Issues
|Teaching||Can prepare educational materials, deliver lectures, and evaluate students.||BINF G8010 Teaching Experience|
|Colloquia||Is familiar with investigators, institutions, projects, methods and theories in the field locally and at other institutions.||BINF G4099 Research Seminar; BINF G8001 Readings|
All doctoral and postdoctoral students are required to take CMBS G4010 Responsible Conduct of Research and Related Policy Issues during the Spring semester of their first year in the program. The ethics course satisfies a National Institutes of Health requirement.
All doctoral and postdoctoral students are required to participate in the teaching activities of the department, serving as a teaching assistant (TA) for up to two different courses (two year postdoctoral research fellows are only required to TA one course). TA preferences are solicited from both students and faculty in the Spring, with the Training Committee making final assignments based on departmental need. To fulfill the TA requirement, students enroll in BINF G8010 MPhil Teaching Experience for 2 points for each semester in which they TA.
Enrollment in the Research Seminar (BINF G4099, 1 pt) is required for PhD students in all tracks their first year. Bioinformatics students may attend the C2B2 seminar in their second year and each subsequent year in lieu of the Research Seminar. Full-time MA students are expected to enroll in the Research Seminar. Part-time MA students are not required to enroll if doing so would cause them to enter the next residence unit category. However, they are expected to attend whenever feasible. Passing the Research Seminar is dependent upon attendance.