BDML Program
About the Program
The Big Data & Machine Learning (BDML) Concentration is one of only two concentrations of the university-wide M.S. in Data Science and Analytics (MSA) at Georgia State University. I developed and proposed the program to the University as Founding Director in 2019. Establishing it required sustained negotiation and collaboration across the Department of Computer Science and the Department of Mathematics & Statistics, both within GSU’s College of Arts and Sciences (CAS) — bridging two departments with distinct cultures, curricula, and student pipelines around a shared vision for data science education at the university level.
Important: BDML is a concentration of the university-wide M.S. in Data Science and Analytics (MSA) degree, and while it is hosted predominantly at the Department of Computer Science — it is not a concentration of the M.S. in Computer Science. Please direct program questions accordingly.
BDML at a Glance
| ⏱️ Duration | 3 semesters (~1.5 years) |
| 📚 Credit hours | 34–36 hours |
| 🔬 Course mix | ~75% CS courses, ~25% Math/Statistics courses |
| 🇺🇸 STEM-designated | Qualifies for the 24-month OPT STEM extension under CIP code 11.0401, making international students eligible for up to 3 years of post-graduation work authorization in the U.S. |
| 🏭 Industry-focused | Capstone project + internship opportunity |
| 🏛️ Home college | College of Arts & Sciences, Georgia State University |
Why BDML? The Sweet Spot
BDML was designed to fill a gap that existing programs leave open:
| BDML @ MSA | MS in Computer Science | MS in Applied Statistics | |
|---|---|---|---|
| Focus | Interdisciplinary: CS + Math/Stats + Applied Capstone | Depth in CS: theory, systems & applications | Rigorous statistical theory & methods |
| AI / ML | 3–5 dedicated courses (ML, Deep Learning, Adv. DL, Adv. ML, Secure & Private AI) | Elective ML courses available; strong algorithms & theory foundation | Statistical learning via regression, multivariate & time series methods |
| Math & Stats | 2 required courses + electives | Mathematics-heavy prerequisites; minimal statistics requirements | Statistics-intensive: inference, regression, Bayesian, stochastic processes |
| Programming & Data Eng. | Big Data Programming, Database Systems, Databases & Web | Software Eng., Prog. Languages, Compilers, Algorithms; database elective available | Statistical computing in R/SAS; no big data or software engineering |
| Capstone | Applied DS project using CRISP-DM; internship encouraged | Thesis, project, or course-only option | Thesis or comprehensive exam; research-oriented |
| Career target | Data Scientist, ML Engineer, AI Specialist, Data Engineer | Software Engineer, Systems Architect, CS Researcher | Statistician, Biostatistician, Data Analyst — healthcare, government, academia |
BDML uniquely combines all three — deep AI/ML coursework, rigorous math and statistics, and hands-on big data engineering — so you graduate ready to build, evaluate, and deploy models end-to-end.
AI Market Alignment
Every major AI market segment maps directly to courses in the BDML curriculum:
| AI Market Segment | Your BDML Courses | Career Roles |
|---|---|---|
| Machine Learning ($294B+ market) | CSC 6850, CSC 8850, CSC 8851 | ML Engineer, Applied Scientist, AI Researcher |
| Natural Language Processing | CSC 8851, CSC 8852, CSC 8850 | NLP Engineer, LLM Specialist, Conversational AI Developer |
| Supply Chain & Predictive Analytics | CSC 6740, CSC 8740, MATH 6751/6752 | Data Scientist, Demand Forecasting Analyst |
| Big Data & Cloud Infrastructure | CSC 6760, CSC 8530, CSC 6710 | Data Engineer, Big Data Architect, Platform Engineer |
| Risk, Fraud & Compliance | CSC 8741, CSC 8230, STAT 8610 | Risk Analyst, Fraud Detection Engineer, FinTech DS |
| Computer Vision & Robotics | CSC 8851, CSC 8852, CSC 8810 | CV Engineer, Robotics ML, Autonomous Systems |
You are earning an advanced degree while building expertise for a $1.8 trillion AI industry.
What You Will Be Able to Do
After completing the BDML concentration, graduates will be able to:
- Apply data science theory for practical analysis tasks across domains
- Collect, store, search, mine, and visualize big data at scale
- Transform raw data into tangible business or research value
- Identify organizational data science needs and design corresponding solutions
- Evaluate multiple data science models and select the right tool for each task
- Communicate findings through sophisticated, human-friendly visualizations
Curriculum
Required Courses
Semester 1 — Foundation (4 courses, 12 credit hours)
| Course | Title | Credits |
|---|---|---|
| CSC 6780 | Fundamentals of Data Science | 4 |
| CSC 6710 | Database Systems | 4 |
| MATH 6751 | Mathematical Statistics I | 3 |
| CSC 8902 | Ethics for Data Science | 1 |
Semester 2 — Core (4 courses, 15 credit hours)
| Course | Title | Credits |
|---|---|---|
| CSC 6760 | Big Data Programming | 4 |
| CSC 6740 | Data Mining | 4 |
| MATH 6752 | Mathematical Statistics II | 3 |
| CSC 6850 | Machine Learning | 4 |
Electives (Choose 2 courses, 6–8 credit hours)
| CS Electives (4 cr each) | Statistics Electives (3 cr each) |
|---|---|
| CSC 8851 — Deep Learning | STAT 8090 — Applied Multivariate Statistics |
| CSC 8852 — Advanced Topics of Deep Learning | STAT 8561 — Linear Statistical Analysis I |
| CSC 8850 — Advanced Machine Learning | STAT 8610 — Time Series Analysis |
| CSC 8740 — Advanced Data Mining | STAT 8674 — Monte Carlo Methods |
| CSC 8741 — Graph Mining | |
| CSC 8230 — Secure and Private Artificial Intelligence | |
| CSC 8530 — Parallel Algorithms | |
| CSC 8810 — Computational Intelligence | |
| CSC 8713 — Spatial and Scientific Databases | |
| CSC 8712 — Advanced Database Systems | |
| CSC 8711 — Databases and the Web | |
| CSC 8710 — Deductive Databases and Logic Programming |
Capstone & Internship
- DSCI 8930 — Capstone Project (1–4 credits): Full data science project following CRISP-DM, SEMMA, or KDD methodology. Includes oral final examination. Required for graduation.
- DSCI 8940 — Data Science Internship (1 credit, optional): Available after core coursework is complete. Strongly recommended — start searching early!
Your Semester-by-Semester Journey
SEM. 1 — Fall (4 courses, 12 cr.) SEM. 2 — Spring (4 courses, 15 cr.)
────────────────────────────────── ──────────────────────────────────
CSC 6780 Fundamentals of DS CSC 6760 Big Data Programming
CSC 6710 Database Systems CSC 6740 Data Mining
MATH 6751 Math Statistics I MATH 6752 Math Statistics II
CSC 8902 Ethics for Data Science CSC 6850 Machine Learning
SUMMER — Internship (optional) SEM. 3 — Fall (3 courses, 7–9 cr.)
────────────────────────────────── ──────────────────────────────────
DSCI 8940 Internship (1 cr) 2 Electives (Deep Learning,
Start searching in early Spring! Adv. ML, Graph Mining, etc.)
DSCI 8930 Capstone Project
Admission Requirements
In addition to general College of Arts and Sciences requirements, applicants are expected to demonstrate a strong academic record (minimum GPA 3.0). While many of our students come from computer science, engineering, mathematics, or statistics backgrounds, we actively welcome applicants from diverse disciplines — biology, chemistry, physics, economics, social sciences, geography, and beyond. Interdisciplinary interests and backgrounds are encouraged; the versatility of perspectives is what makes data science teams stronger.
All applicants should have foundational experience in:
- Programming and data structures
- Linear algebra
- Probability and statistics
Students who lack some of these prerequisites may be admitted conditionally and complete them as deficiency courses prior to or alongside their first semester.
For complete and current admission requirements and to apply, visit the official GSU BDML program page or the official GSU catalog.
Key Contacts
| Role | Contact |
|---|---|
| BDML Program Director | Dr. Rafal Angryk · rangryk@gsu.edu · Course substitutions, petitions, program questions |
| BDML Admissions | BDMLAdmissions@cs.gsu.edu · 404-413-5714 |
| CAS Graduate Services | Ms. Naeemah Ahmed · nsheikahmed1@gsu.edu · Registration, course scheduling, holds, DegreeWorks, graduation milestones and evaluations |
| CS Dept. Programs Coordinator | Mr. Jamie Hayes · jhayes14@gsu.edu · GTA appointments, general CS graduate administration |
| International Student & Scholar Services (ISSS) | isss.gsu.edu · For visa status, CPT/OPT authorization, and international student support |
Official Program Links
- GSU BDML program page & admissions
- Official GSU catalog — curriculum details
- Direct Admit application portal
BDML Quick Links
- Already a GSU Undergraduate? — No-repeat rules and direct admit info
- 4+1 Dual Degree: BS-CS → BDML — Accelerated pathway for CS undergrads (Spring 2027)
- 4+1 Dual Degree: BS-DS → BDML — Accelerated pathway for DS undergrads (Fall 2026)
- Funding & Assistantships — GTA/GRA automatic consideration and requirements
- Capstone Project — DSCI 8930 requirements and expectations
- Data Science Internship — DSCI 8940 timing, requirements, and approval process