In today's data-driven world, the ability to harness the power of data for informed decision-making has become a critical skill. As organizations across industries increasingly rely on data to drive innovation and stay competitive, the demand for professionals with expertise in Data Science has surged. A master's degree in Data Science is a comprehensive and advanced program designed to equip students with the knowledge, skills, and tools necessary to excel in this dynamic and rapidly evolving field.
The UGA Master’s degree in Data Science is an interdisciplinary program that is jointly offered by the Department of Statistics and the School of Computing. It will provide the students with a strong foundation in Data Science, covering algorithms, distributed systems, database management, and machine learning from the School of Computing, and regression, time-series analysis, design of experiments, statistical learning, and Bayesian statistics from Statistics. It is tailored to individuals who aspire to become data scientists, analysts, or leaders in the field of data-driven decision-making. Students graduating with the degree will know how to develop software, design and maintain databases, process data in distributed environments, analyze the data using techniques from statistics, data mining and machine learning, provide visualizations of the data or the results of analysis, and assist decision makers.
The program will include practical application of acquired knowledge and skills in the form of a Master’s Project course.
This major is part of a Double Dawgs pathway: Data Science BS/Data Science MS(non-thesis) Mathematics BS (Applied Mathematics)/Data Science MS(non-thesis) Statistics BS/Data Science MS(non-thesis) For more information, go to doubledawgs.uga.edu.
Director of Graduate Studies I Ting Zhang, Professor tingzhang@uga.edu Director of Graduate Studies II Liang Liu, Professor lliu@uga.edu Graduate Program Administrator Holly Smith hasmith@uga.edu 706-542-5256 https://www.stat.uga.edu/
32 hours
Apply to the UGA Graduate School and include the following: a) GRE scores b) 3 letters of recommendation c) Resume d) Statement of Purpose e) Unofficial transcript f) International students: For students whose primary language is not English, proof of English proficiency is required. Students must submit official university documents according to the UGA Graduate School requirements. Students should possess the following: a) Some basic background in Statistics (at the level of STAT 2000 + STAT 4210 in the UGA curriculum) b) Some basic background in Programming (at the level of CSCI 1301-1301L + CSCI 2150-2150L in the UGA curriculum) 3) Two semesters of Data Structures (at the level of CSCI 2720-2725 in the UGA curriculum) 4) Three semesters of Calculus (Differential, Integral, Multivariate) (at the level of MATH 2250-2260-2270 in the UGA curriculum) 5) A course in Linear Algebra (at the level of MATH 3300 in the UGA curriculum)
The application deadline is January 1 for the following fall if seeking financial support. If applicants are not seeking financial support: Fall – April 15 for international students and July 1 for domestic students, Spring – October 15 for international students and November 15 for domestic Students, Summer – February 15 for international students and May 1 for domestic students.
| CSCI 4360/6360 | Data Science and Applied Machine Learning II | 4 |
| CSCI(STAT) 6375 | Foundations of Data Science | 4 |
|
STAT 8330
Analytical Thinking
|
Elements of Statistical Learning | 3 |
Choose 1 course(s) from the following:
|
STAT 6420
Analytical Thinking
|
Applied Linear Models | 3 |
| STAT 4530/6530 | Statistical Inference for Data Scientists | 3 |
Choose 1 course(s) from the following:
| CSCI 7300 | Master's Thesis | 1-6 |
| STAT 7300 | Master's Thesis | 1-9 |
Choose 1 course(s) from the following:
| CSCI 7200 | Master's Project | 2-6 |
| STAT 7200 | Master's Project | 2-6 |
Choose 8 credit hour(s) from the following:
| CSCI 4150/6150 | Numerical Simulations in Science and Engineering | 4 |
| CSCI 4170/6170 | Introduction to Computational Investing | 4 |
| CSCI 4210/6210 | Simulation and Modeling | 4 |
| CSCI 4370/6370 | Database Management | 4 |
| CSCI 4380/6380 | Data Mining | 4 |
| CSCI 4470/6470 | Algorithms | 4 |
| CSCI 4780/6780 | Distributed Computing Systems | 4 |
| CSCI 4795/6795 | Cloud Computing | 4 |
| CSCI 4850/6850 | Biomedical Image Analysis | 4 |
| CSCI 8360 | Data Science Practicum | 4 |
| CSCI 8370 | Advanced Database Systems | 4 |
| CSCI 8380 | Advanced Topics in Information Systems | 4 |
| CSCI 8535 | Multi-Robot Systems | 4 |
| CSCI 8790 | Advanced Topics in Data Intensive Computing | 4 |
| CSCI 8820 | Computer Vision and Pattern Recognition | 4 |
| CSCI 8850 | Advanced Biomedical Image Analysis | 4 |
| CSCI 8920 | Decision Making Under Uncertainty | 4 |
| CSCI 8945 | Advanced Representation Learning | 4 |
| CSCI(ARTI) 8950 | Machine Learning | 4 |
| CSCI 8951 | Large-Scale Optimization for Machine Learning | 4 |
| CSCI 8955 | Advanced Data Analytics: Statistical Learning and Optimization | 4 |
| CSCI 8960 | Privacy-Preserving Data Analysis | 4 |
Choose 6 credit hour(s) from the following:
| STAT 4240/6240 | Sampling and Survey Methods | 3 |
| STAT 4250/6250 | Applied Multivariate Analysis and Statistical Learning | 3 |
| STAT 4260/6260 | Statistical Quality Assurance | 3 |
| STAT 4270/6270 | Network Data Analysis and Graphical Models | 3 |
| STAT 4280/6280 | Applied Time Series Analysis | 3 |
| STAT 4290/6290 | Nonparametric Methods | 3 |
| STAT 4350/6350 | Applied Bayesian Statistics | 3 |
| STAT 4440/6440 | Design and Analysis of Complex Computer Models | 3 |
| STAT 4530/6530 | Statistical Inference for Data Scientists | 3 |
| STAT 4620/6620 | Applied Categorical Data Analysis | 3 |
| STAT 6430 | Design and Analysis of Experiments | 3 |
| STAT 6510 | Mathematical Statistics I | 3 |
| STAT 6520 | Mathematical Statistics II | 3 |
|
STAT 6800
Analytical Thinking
Critical Thinking
|
Tools for Statistical Theory | 3 |
| STAT 8000 | Introductory Statistical Collaboration | 3 |
|
STAT 8001
Analytical Thinking
|
Advanced Statistical Collaboration | 3 |
| STAT 8040 | Environmental Statistics | 3 |
|
STAT 8060
Analytical Thinking
|
Statistical Computing I | 3 |
|
STAT 8070
Analytical Thinking
|
Statistical Computing II | 3 |
| STAT 8210 | Multivariate: Theory and Methods | 3 |
| STAT 8230 | Applied Nonlinear Regression | 3 |
| STAT 8250 | Multivariate Methods | 3 |
|
STAT 8260
Analytical Thinking
|
Theory of Linear Models | 3 |
| STAT 8270 | Spatial Statistics | 3 |
| STAT 8280 | Time Series Analysis | 3 |
| STAT 8290 | Advances in Experimental Designs | 3 |
|
STAT 8620
Analytical Thinking
|
Advanced Statistical Models | 3 |
| STAT 8630 | Mixed-Effect Models and Longitudinal Data Analysis | 3 |
As required by Graduate School, all new graduate students must take GSRC 7001 in their first or second semester.
| GRSC 7001 | GradFIRST: First-year Research and Scholarship Training Seminar | 1 |
By TA Policy, all graduate students with instructional duties must complete GRSC 7770 prior to or concurrently with their first semester on assistantship.
| GRSC 7770 | Graduate Teaching Seminar | 1-3 |
Select additional courses, as needed, in consultation with an advisor to meet the minimum number of required credit hours for this program.
MAGNet program – internship with State Farm
The Stat Club is an organization which has both social and administrative functions within the Department of Statistics. The membership is open to all students in Statistics, both undergraduate and graduate. The officers of the club organize social outings, such as bowling or mini-golf, about once a month. They also organize the International Potluck Dinner in the Fall and the Bradley Lecture Festivities (the lecture, the banquet, and the picnic) in the Spring. Other activities include helping with events like the graduate ceremony, Industry Days, Statistics and Data Science open houses, special lectures, and Georgia Statistics Days.
Applied Data Science (M.S., online) Statistics (M.S.) Statistics (Ph.D.)