Master of Science in Data Science and Analytics
For the fall 2020 semester, international students may enroll and take classes either partially or fully at distance while outside the USA. Students are expected to enroll in on-campus courses starting at the beginning of their second semester of study unless guidance is extended or advised at that time. During on-campus residency at Georgia State University, international students studying on F-1 student visas may only count one additional online course towards their full-time enrollment requirements.
Curriculum
The M.S. in Data Science and Analytics program comprises three semesters in a lock-step, cohort format. However, working professionals can complete the program on a part-time basis, extend the length of the program and take courses in the summer. Full-time students also have the option of completing the program in 12 months by taking their electives in the summer.
Course Sequence
Pre-requisite Requirements
Academic knowledge of Calculus I, II and III (Multivariate), Linear Algebra and Basic Programming are required. Applicants who need a math/programming refresher or lack a demonstrated knowledge of advanced calculus, linear algebra and basic programming knowledge may be required to take the courses below in the summer prior to starting the program. If required, applicants will be notified in their admission decision letter.
- MATH7100 (Basic Math for Analytics)
- MATH7110 (Mathematical Foundations for Analytics)
- CSC7003 (Foundations for Programming)
Fall 1
- MSA 8010
- MSA 8040
- MSA 8020
- MSA 8190
Spring
- MSA 8050
- MSA 8150
- MSA 8200
- MSA 8600
Summer
- Internship (optional)
Fall 2
- Elective 1
- Elective 2
- Elective 3
Fall 1
- MSA 8010
- MSA 8040
Spring 1
- MSA 8050
- MSA Elective
Summer
- Internship (Optional)
- Elective 1
Fall 2
- MSA 8190
- MSA 8020 + MSA Elective
Spring 2
- MSA 8150
- MSA 8200
- MSA 8600
Required Courses
7 Courses | 21 Hours
- MSA 8010 – Data Programming for Analytics (3 hours)
View a sample syllabus - MSA 8020 – Data Visualization (1.5 hours)
View a sample syllabus - MSA 8040 – Data Management for Analytics (3 hours)
View a sample syllabus - MSA 8150 – Machine Learning for Analytics (3 hours)
View a sample syllabus - MSA 8190 – Statistical Foundations for Analytics (3 hours)
View a sample syllabus - MSA 8200 – Predictive Analytics (3 hours)
View a sample syllabus - MSA 8050 Scalable Data Analytics (3 hours)
View a sample syllabus - MSA 8600 Deep Learning Analytics (1.5 hours)
View a sample syllabus
Electives
3 Courses | 9 Hours
- MSA 8770 – Text Analytics (3 hours)
- MSA 8500 – Image Analytics (3 hours)
- MSA 8650 – Advanced Deep Learning with Business Applications
(3 hours) - FI 8460 – Introduction to FinTech (3 hours)
Other Electives
- CIS 8020 – Systems Integration (3 hours)
- CIS 8100 – Management of Information Systems (3 hours)
- CIS 8200 – Information Systems Strategy (3 hours)
- FI 8000 – Valuation of Financial Assets (3 hours)
- FI 8200 – Financial Derivatives (3 hours)
- FI 8260 – Hedge Fund Strategies (3 hours)
- FI 8320 – Corporate Financial Strategy (3 hours)
- HA 8160 – Health Care System (3 hours)
- HA 8550 – Health Planning and Financial Management (3 hours)
- HA 8620 – Operations Management and Quality in Health Care (3 hours)
- HA 8670 – Health Information Systems (3 hours)
- HA 8750 – Data Analytics
- MK 8010 – Marketing Metrics (3 hours)
- MK 8705 – Digital Marketing Analytics (3 hours)
- MK 8715 – Brand and Consumer Analytics (3 hours)
- MK 8730 – Marketing Engineering (3 hours)
- MRM 8610 – Financial Engineering (3 hours)
- MRM 8620 – Quantitative Financial Risk Models (3 hours)
- MGS 8730 – Project Management (3 hours)
- MGS 8740 – Operations Strategy (3 hours)
- RCB 8040 – Competing on Analytics & Organizational Knowledge (3 hours)
- MGS 8110 – Applied Regression Analysis (3 hours)
- RMI 8050 – Risk Management Modeling (3 hours)
- RMI 8300 – Predictive Risk Models (3 hours)
Most students also participate in Insight Sprints, bootcamps, and in our Innovation Labs.
GPS Users
The street address of 3348 Peachtree Road is not the best landmark to use for directions. If you are using Google maps or a GPS unit, input Tower Place Drive NE 30326 (or the intersection of Tower Place Drive and Lenox Road). Then follow the directions below to locate the correct parking deck.
Gates on Tower Place Drive
There are gates on Tower Place Drive that block access to our building from Piedmont between 7 – 9:30 a.m. and 4:30 – 6:30 p.m. During these times, it is imperative that you enter from the Lenox Road intersection on Tower Place Drive.
Boot Camps
Besides the eight required and three elective courses, students will participate in boot camps covering topics such as linear algebra, R programming, data visualization, Tableau, advanced R, big data programming and SAS.
Insight Sprints
Students will complete Insight Sprints throughout the program.
- Industry partners provide real problems with data for insights and solutions.
- Student teams work with faculty from across the entire university as well as corporate participants.
- Students get feedback during weekly meetings over a 6- to10-week period.
- Students engage in practical application of machine learning and predictive analytics.