All coursework offered at Columbia Graduate Center. Most courses are hybrid. This will mean that some weeks will substitute synchronous in classroom learning for asynchronous out-of-classroom learning.
The program consists of 10 (or 11) courses. The requirements include the following.
- Four (or five) core courses in business, statistics, and computer science.
- Two additional statistics courses.
- Two additional computer science courses;.
- An elective from statistics, computer science, or business.
- A technical capstone project.
For the required statistics and computer science courses, one course is proscribed while the other is an elective.
For course descriptions, please see the Graduate Catalog.
- CS701 Introduction to Programming for Data Science (waiveable)
- CS703 Programming for Data Science (taught by CS faculty)
- ST710 Statistical Computing (taught by Statistics faculty)
- DS730 Introduction to Data Science
- DS851 Business Intelligence and Data Mining (taught by IS faculty)
Additional Required Courses
- CS737 Machine Learning
- ST765 Linear Statistical Models
- DS795/796 Data Science Project
Computer Science Elective Courses
- CS745 Multimedia data analysis and mining
- CS746 Data Visualization
- CS750 Special Topics in Computer Science
- CS751 Independent Study
- CS765 Database Retrieval
- CS766 Information Retrieval and Natural Language Processing
Statistics Elective Courses
- ST767 Multivariate Analysis
- ST775 Generalized Linear Models and Multilevel Models
- ST776 Bayesian Inference
- ST778 Time Series
- ST791 Special Topics in Statistics
- ST792 Independent Study
Business Elective Courses
- GB700 Ethics and Social Responsibility
- GB701 Operations Management and Process Strategies
- GB705 Leadership and Management
- GB732 Data Management and Governance
- GB733 Enterprise Systems
- GB736 Data Visualization for Decision Making
- GB852 Advanced Analytics
- GB853 Social Media and Web Mining
- GB735 Project Management