Students in the Master of Science in Analytics, concentration in Data Science program, must successfully complete 30 credits and maintain a cumulative GPA of not less than 3.00.
The Georgetown Analytics program is giving an asynchronous, online course in programming preparation that covers R, Python, and command line use in the summer prior to matriculation. The course is equivalent to three credits, is designed for matriculating MS Analytics students, and is offered free of charge. It is required for incoming students who do not have a computer science degree and adequate preparation. Students admitted to the program will only have this requirement waived on discussion with the Program Director (Todd Leen) or Program Coordinator (Heather Connor). This course will run during Georgetown Summer Session II (July 10 - August 11). Remember, you must complete this course to matriculate in the fall unless you are granted a waiver by the program.
Students may enroll on a full-time or part-time basis. International students must maintain full-time academic status (three courses per semester) as long as possible and may only drop to part-time status for their last semester (if this is necessary to complete the degree). There is a three-year window to complete the program. The expected time to complete the degree is two years. By using transfer credits, taking optional summer courses, and taking four classes per semester, it is possible for a student to graduate in as little as 12 calendar months. A common time for full time students is 16 months (three full-time semesters plus one course in their final spring semester).
Concentration in Data Sciences COURSEWORK
There are six required core courses, designed to provide students with a solid foundation in data science. Five additional elective courses allow students to learn tailored skills, helping them apply data analysis to fields of interest. Coursework may be taken in any order that is allowed by the prerequisites.
CORE COURSES - 15 credits
Our six-course core is designed to give students an overview of the massive data landscape.
- Advanced Programming Topics - ONLINE (no credit)
- Introduction to Data Analytics (ANLY-501)
- Massive Data Fundamentals (ANLY-502)
- Scientific and Analytical Visualization (ANLY-503)
- Probabilistic Modeling and Statistical Computing (ANLY-511)
- Statistical Learning for Analytics (ANLY-512)
ELECTIVES - 15 credits
The following courses have been pre-approved by the program and will satisfy elective requirements. Additional coursework may be approved upon request, and at the discretion of the program.
Please be aware that courses offered by other programs (course prefixes BIST, CCTP, COSC, and MATH) likely have seating priority for their own students and prerequisite restrictions. You should speak directly with the course instructor to see if seating is available and if you satisfy prerequisites prior to semester enrollment.
- Effective Presentation for Technology & Science (ANLY-520)
- Databases (ANLY-531)
- Technology & Policy for Data Privacy (ANLY-540)
- Structures and Algorithms for Analytics (ANLY-550)
- Optimization (ANLY-561)
- ANLY Internship (ANLY-905)
- Bioinformatics for Omics Data (BIST-532)
- Intro to Social Network Analysis (CCTP-696)
- Image Processing (COSC-455)
- Data Privacy (COSC-531)
- Natural Language Processing (COSC-572)
- Statistical Machine Learning (COSC-578)
- Web Search and Sense-making (COSC-589)
- Mathematics of Climate (MATH-412)
- Mathematics of Social Networks (MATH-442)
- Stochastic Simulation (MATH-611)
- Bayesian Statistics (MATH-640)
- Time Series Analysis (MATH-645)
- Categorical Data Analysis (MATH-657)