Courses

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. The qualitative, quantitative, information technology objectives enable students to apply these methods to one or more domain of specialization, which includes data science, clinical informatics, translational informatics, bioinformatics, or public health informatics.

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 to meet educational objective and domain requirements that are not listed below with approval from their research or academic advisors or the graduate program director.

Examples of Objective-Domain Trajectories
PhDs and Postdocs:
– Data Science: 2 Quant Objectives + 1 IT Objective + 2 Domain Courses
– Clinical/Public Health: 3 Qual, Quant, or IT Objectives + 2 Domain Courses (at least 1 should be Quant or IT) + 2 Domain Courses
– Bioinformatics: 2 Quant Objectives + 1 IT Objective + 2 Domain Courses
– Translational: 2 Quant Objectives + 1 IT Objective + 2 Domain Courses

MAs:
– Data Science: 2 Quant Objectives + 2 Domain Courses
– Clinical/Public Health: 1 Quant or IT + either 1 Qual, Quant or IT + 2 Domain Courses
– Bioinformatics: 2 Qual, Quant, or IT Objectives + 2 Domain Courses
– Translational: 2 Quant or IT Objectives + 2 Domain Courses

Credits
PhD students may enroll in up to 20 credits per term without incurring additional charges. Any points in excess of 20 are not covered by the department, but may be covered by the student’s research advisor with prior approval before enrolling.  Depending upon degree type, in addition to the credit load from the core, objectives, and domain courses, students may also need to register for Research Projects (BINF G6001, BINF G9001 and BINF G9999), the Ethics Course (CMBS G4010), the MPhil Course (BINF G8010), or Research Seminar (BINF G4099). More information on these courses can be found at the bottom of the page.

Core Courses – 4 Courses

By the end of the core, students should be familiar with problems, issues, and applications in Biomedical Informatics, and are expected to apply general theories and methods to solve problems.
 
DBMI core courses (BINF G4002 Machine Learning in Health Care and BINF G6002 Methods III: Research Methods) require substantial competence in statistics and programming in Python. Incoming students will be administered a test to assess their knowledge of statistics and programming. The exam will be administered twice, in June and in August. Those showing insufficient competence in statistics, will be required to take one of the Intro to Statistics courses offered at Columbia University (for example STATS GU4001 Introduction to Probability & Statistics, BIST P6014 Introduction to Biostatistical Methods , BMEN E4110 Biostatistics for Engineers, STAT 4204 GU Statistical Inference, or another similar course). This course will have to be taken for a letter grade and will count towards quantitative educational objective requirements. Those showing insufficient competence in programming in Python will be required to take online course(s)* (see list below) to expand their expertise. Non-Columbia courses cannot be used towards the GSAS MA degree requirement of 30 points of coursework and 2 residence units per GSAS. For online courses, students will be expected to submit a certificate or other proof of completion.
 
BINF G4001 Introduction to Computational Biomedicine and Health (Prof. TBA, fall) Taught on main (Morningside) campus. An overview of the field of biomedical informatics, combining perspectives from medicine, computer science and social science. Use of computers and information in health care and the biomedical sciences, covering specific applications and general methods, current issues, capabilities and limitations of biomedical informatics. Biomedical Informatics studies the organization of medical information, the effective management of information using computer technology, and the impact of such technology on medical research, education, and patient care. The field explores techniques for assessing current information practices, determining the information needs of health care providers and patients, developing interventions using computer technology, and evaluating the impact of those interventions. BINF G4001 must be taken fall term of entry.
 
BINF G4002: Methods II: Computational Methods (Prof. Matthew McDermott, spring) Survey of the computational methods underlying the field of medical informatics. Explores techniques in mathematics, logic, decision science, computer science, engineering, cognitive science, management science and epidemiology, and demonstrates the application to health care and biomedicine.
 
BINF G4003: Symbolic AI In Healthcare (Prof. Chunhua Weng, fall) Survey of foundational symbolic methods for modeling health information systems and for making those models explicit and sharable.  The topics cover clinical terminologies (e.g., ICD-9, SNOMED-CT, MeSH, UMLS), biomedical ontologies (e.g., GO, Disease Ontology, PharmGKB), knowledge representation, computerized practice guidelines, semantic interoperability, and text processing. Prerequisites: Acculturation to Programming and Statistics (BINF G4000) or permission of instructor.
 
BINF G6002 Research Methods (Prof. Lena Mamykina, spring) for Clinical, Public Health or Translational students. Provides an overview of research methods relevant to biomedical informatics. The overall goal of the course is to prepare the student to participate in and perform scientific research. Competencies of the course include learning to design a study of a biomedical informatics resource; perform quantitative and qualitative analysis relating to a biomedical informatics resource; and write a biomedical informatics-related research proposal. By the end of the course, all trainees must be able to write a biomedical informatics-related research summary and complete certification in responsible conduct of research.
 
BINF G4013 Biological Sequence Analysis (Prof. Richard Friedman, spring) for Bioinformatics students. Taken in lieu of BINF G6002 Research Methods (Mamykina). Biological Sequence Analysis introduces the basics of sequential, structural, and functional genomics.  The course is both a lecture and lab course, in which students learn the basic bioinformatic principles and apply these principles through laboratory exercises. The course accommodates both students with a computational background with little previous biology, and students from a primarily biological background, with little previous computation. Topics include basic Unix, biological databases, sequence comparison, database searching, multiple sequence alignment, biological regular expressions, profile methods (including hidden Markov models), protein and RNA structure prediction, mapping, primer design, genomic analysis, molecular phylogetics, and functional genomics including microarray analysis and pathway analysis.
 
Objectives

Information Technology (IT)
Apply computer science and statistical techniques to manage data, develop software, and solve problems.

BIST P8105Data Science
COMS W4111Introduction to Databases
COMS W4181Security I
COMS W4156Advanced Software Engineering
COMS W4444Programming and Problem Solving
COMS W4995Networks and Crowds
COMS W4995Introduction to Data Visualization
COMS E6111Advanced Database Systems
COMS E6998Cloud & Big Data
COMS E6998High Perf Machine Learning
COMS 6998Machine Learning Datasets
CSOR W4231Analysis of Algorithms
CSOR W4246Algorithms for Data Science
EAEE E4009GIS-Research, Environment, Infrastructure Management
EECS E6893Information Processing: Big Data Analytics
ELEN E6883An Introduction to Blockchain Technology
GR5243/GU4243Applied Data Science – Hands-on Machine Learning with Python
IEOR 4526Analytics on the Cloud
IEOR E4575Operations Research: Policy for Privacy Technologies
IEOR E6998 001Special Topics in Computer Science: Privacy Preserving Systems
QMSS G4063Data Visualization
STAT GR5702Exploratory Data Analysis and Visualization

Quantitative (Qual)
Apply statistical, mathematical, and computational techniques to analyze data and test hypotheses.

BINF G5001Data Science for Mobile Health
BIST P6104P6114 Introduction to Biostatistical Methods
BIST P8105Data Science
BIST P8110Applied Regression II
BIST P8116Design of Medical Experiments
BIST P8157Longitudinal Data Analysis
BIST P9120Topics in Statistical Learning and Data Mining
COMS 6998-7Statistical Methods for NLP
COMS E6111Advanced Database Systems
COMS E6998Cloud & Big Data
COMS W4111Introduction to Databases
COMS W4156Advanced Software Engineering
COMS W4181Security I
COMS W4444Programming and Problem Solving
COMS W4705Natural Language Processing
COMS W4761Computational Genomics
COMS W4771Machine Learning
COMS W4772Advanced Machine Learning or COMS E6898 Topics: Information Processing: From Data to Solutions
COMS W4995Applied Machine Learning
COMS W4995Causal Inference for Data Science
CSOR W4231Analysis of Algorithms
CSOR W4246Algorithms for Data Science
EECS E6720Bayesian Models for Machine Learning
EECS E6893Information Processing: Big Data Analytics
EECS E6893Big Data Analytics
ELEN E4903Machine Learning
ELEN E6690Statistical Learning for Biological and Information Systems
HBSS 4199 or HBSS 4160Introduction to Biostatistics (Teachers College)
IEOR 4720Deep Learning
IEOR E4540Data Mining
QMSS G4063Data Visualization
QMSS G4063Data Visualization
STAT G6104Applied Statistics
STAT G6509/GR6701Foundations of Graphical Models
STAT W4026Applied Data Mining
STAT W4107 or STAT GU4204Statistical Inference
STAT W4240Data Mining

Qualitative (Qual)
Apply techniques that aid in the understanding of behavioral and social phenomena associated with health-related problems and with delivery of healthcare.

B9506-001 (PhD)Organizational behavior
BINF G4008001 Special Topics in Biomedical Informatics: Intelligent Decision Support: History, Paradigms, Applications
BINF G4008002 Special Topics in Biomedical Informatics: Ethics and Fairness in Digital Health
BINF G6002Research Methods (core for clinical and translational, but can count as Qualitative objective for data science emphasis and non-postdoc MAs when not taken for core requirement)
COMS W4170User Interface Design
NURS N9352Qualitative Research Design & Methods
ORL 6500Qualitative research methods in organizations: Design and data collection
ORL 6501Qualitative research methods in organizations: Data analysis and reporting.
ORL 6518Methods of case study and analysis.
ORLJ 4009Understanding behavioral research
ORLJ 5018Using survey research in organizational consulting

Domains

Students should be able to apply general methods and theories of informatics to one or more areas of specialization: data science, clinical informatics, translational informatics, bioinformatics, and public health informatics. MDs and Nurses are exempt from the clinical domain requirement and will pick substitute courses with their academic and/or research advisor.

Clinical

BINF G4011Acculturation to Medicine and Clinical Informatics
BINF G5000Defining, Evaluating and Improving Quality in Health Care
PATH G6003Mechanisms in Human Disease

Translational

BINF G4006Translational Bioinformatics
BIOT W4200Biopharmaceutical Development & Regulation
COMS E6998Computational Methods/High Throughput Sequencing
PATH G6003Mechanisms in Human Disease
PHAR G8001Principles of System Pharmacology

Bioinformatics

BINF G4013Biological Sequence Analysis
BINF G4015Computational Systems Biology
BINF G4016Quantitative/Computational Aspects of Infectious Disease
BINF G4017Deep Sequencing
BIOL W4510Genomics of Gene Regulation
BIOL W4799Molecular Biology of Cancer
BIST P8119Advanced Stat/Comp Methods Genetics/Genomics
COMS W4761Computational Genomics
ELEN E6010Design Principles for Biological Circuits

Public Health

BIST P6530Issues & Approaches in Health Policy & Management
EHSC P6385/6Principles of Genetics and the Environment I and II
EPID P6400/02Epidemiology
EPID P8471Social Epidemiology
SOSC P8795New Media and Health

Research

Students should conduct independent research in Biomedical Informatics; including the ability to formulate a hypothesis, design a suitable experiment, and carry it out with sensitivity to ethical standards.

BINF G6001 Projects in Biomedical Informatics. Taken at least once for MA students and every fall and spring term for PhD students until successful passage of Oral II/Depth Exam. MA students enroll for 3 points. First year PhD students enroll for 6 points fall and spring terms; 9 points fall and spring terms in 2nd year; 12 points fall and spring terms each subsequent year until enrollment in BINF G9001. NLM funded postdoctoral MA students enroll for 6 points fall and spring terms of first year, and 9-12 points for fall and spring terms of their 2nd and 3rd years, depending upon their course load.
BINF G9001 Doctoral Research in Biomedical Informatics. Taken the term following successful passage of the 2nd preliminary PhD exam, the Oral II/Depth Exam.
BINF G9999 Doctoral Dissertation. Taken in the final term of enrollment for PhD students along with BINF G9001.
CMBS G4010 Responsible Conduct of Research and Related Policy Issues
BINF G8010 Teaching Experience; Teaching can prepare educational materials, deliver lectures, and evaluate students.
BINF G4099 Research Seminar;  ColloquiaIs familiar with investigators, institutions, projects, methods and theories in the field locally and at other institutions.
BINF G8001 Independent Readings

Other Requirements

Ethics Course
All doctoral and postdoctoral students are required to take the Ethics Course (CMBS G4010 Responsible Conduct of Research and Related Policy Issues, 1 pt) during the Spring semester of their first year in the program. The ethics course satisfies a National Institutes of Health requirement.

MPhil Course 
BINF G8010 MPhil Teaching Experience, 2 pts
Serving as a Teaching Assistant (TA) is a GSAS degree requirement for PhD students and a DBMI degree requirement for postdoctoral students.
PhD students TA for 2 courses. (BINF G8010 MPhil Teaching Experience, 2 pts)  MD-PhD students TA for 1 course.  Postdoctoral MA students TA for one (two year postdoctoral students) or two courses (three year postdoctoral students).
Students are solicited for TA preferences over email Spring term.  Final assignments are made by the Graduate Program Director.

Research Seminar
BINF G4099 Research Seminar, 1 pt, P/F
Enrollment in the Research Seminar is required for PhD students.  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.