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. PRE-REQUISITE COURSES 1) Some basic background in Statistics (at the level of STAT 2000 + STAT 4210 in the UGA curriculum) 2) 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) REQUIRED ENTRANCE EXAMS 1) The GRE (Verbal, Quantitative, & Analytical) is required. 2) TOEFL iBT (minimum of 80) or IELTS (minimum of 6.5) are required for international applicants. 3) An international applicant may waive the TOEFL/IELTS requirement if s/he is from an English-speaking country, or if s/he has a degree from an institution of higher learning in an English-speaking country. Please contact gradadm@uga.edu for waiver. Guidelines for Data Science MS Degree For the M.S. in Data Science degree, the table below lists the “Required Courses” under C.1, C.2, and C.3. a or b (at least 18 credit hours) and the “Electives” (at least 14 credit hours). In C.3, students are allowed to choose either a non-thesis (C.3.a) option or a thesis (C.3.b) option. Students will be admitted to only one of these tracks. Students must additionally complete an ethics course. The coursework consists of 32 semester hours. In planning one's program of study, one should consult with a Graduate Advisor, as there are restrictions on when courses are offered and a number of hidden pre-requisites. Course Prefixes & Numbers Course Titles Credit Hours C.1. Core Courses 8 1 CSCI (STAT) 6375 Foundations of Data Science 4 2 CSCI 6360 Data Science II 4 C.2. Advanced Courses 6 STAT 6420 Applied Linear Models 3 3 Or or STAT 6530 Statistical Inference for Data Scientists 3 4 STAT 8330 Advanced Statistical Applications and Computing 3 C.3.a. Non-thesis option 4 CSCI 7200 Master’s Project 4 Or or STAT 7000 Master's Research 5 C.3.b. Thesis option CSCI 7300 or STAT 7300 Master’s Thesis or Master’s Thesis 4 Electives (Two courses from each category)* 14* 6 Category A (see below) CSCI Elective 1 4 7 CSCI Elective 2 4 8 Category B (see below) STAT Elective 1 3 9 STAT Elective 2 3 Total Credit Hours 32 *The 14 credit hours of Electives mentioned in the table above will consist of 8 credit hours from Category A (Computer Science) and 6 credit hours from Category B (Statistics) Category A: CSCI 6150 (4 hours) - Numerical Simulations in Science and Engineering CSCI 6170 (4 hours) - Introduction to Computational Investing CSCI 6210 (4 hours) - Simulation and Modeling CSCI 6370 (4 hours) - Database Management CSCI 6380 (4 hours) - Data Mining CSCI 6470 (4 hours) - Algorithms CSCI 6780 (4 hours) - Distributed Computing Systems CSCI 6795 (4 hours) - Cloud Computing CSCI 6850 (4 hours) - Biomedical Image Analysis CSCI 8360 (4 hours) - Data Science Practicum CSCI 8370 (4 hours) - Advanced Database Systems CSCI 8380 (4 hours) - Advanced Topics in Information Systems CSCI 8535 (4 hours) - Multi Robot System CSCI 8790 (4 hours) - Advanced Topics in Data Intensive Computing CSCI 8820 (4 hours) - Computer Vision and Pattern Recognition CSCI 8850 (4 hours) - Advanced Biomedical Image Analysis CSCI 8920 (4 hours) - Decision Making Under Uncertainty CSCI 8945 (4 hours) - Advanced Representation Learning CSCI(ARTI) 8950 (4 hours) - Machine Learning CSCI 8951 (4 hours) - Large-Scale Optimization for Machine Learning CSCI 8955 (4 hours) - Advanced Data Analytics: Statistical Learning and Optimization. CSCI 8960 (4 hours) - Privacy-Preserving Data Analysis Category B: STAT 6240 (3 hours) – Sampling and Survey Methods STAT 6250 (3 hours) - Applied Multivariate Analysis and Statistical Learning STAT 6280 (3 hours) - Applied Time Series Analysis STAT 6350 (3 hours) - Applied Bayesian Statistics STAT 6430 (3 hours) - Design and Analysis of Experiments STAT 6510 (3 hours) - Mathematical Statistics I STAT 6620 (3 hours) - Applied Categorical Data Analysis STAT 6800 (3 hours) - Tools for Statistical Theory STAT 8000 (3 hours) - Introductory Statistical Collaboration STAT 8060 (3 hours) - Statistical Computing I STAT 8070 (3 hours) - Statistical Computing II STAT 8210 (3 hours) - Multivariate: Theory and Methods STAT 8230 (3 hours) - Applied Nonlinear Regression STAT 8260 (3 hours) - Theory of Linear Models STAT 8270 (3 hours) - Spatial Statistics STAT 8280 (3 hours) - Time Series Analysis STAT 8290 (3 hours) - Advances in Experimental Designs STAT 8620 (3 hours) - Categorical Data Analysis and Generalized Linear Models STAT 8630 (3 hours) - Mixed-Effect Models and Longitudinal Data Analysis Sample programs of study Sample Program 1 Courses (list acronym, number, and title) Semester Hours CSCI(STAT) 6375, Foundations of Data Science (NEW) First Year, Fall 4 STAT 6420, Applied Linear Models First Year, Fall 3 STAT 6250, Applied Multivariate Analysis and Statistical Learning First Year, Fall 3 CSCI 6360, Data Science II First Year, Spring 4 CSCI 6370, Database Management First Year, Spring 4 STAT 8060, Statistical Computing I First Year, Spring 3 CSCI 8360, Data Science Practicum Second Year, Fall 4 CSCI 7200, Master’s Project Second Year, Fall 4 STAT 8330, Advanced Statistical Applications and Computing Second Year, Fall 3 Total 32 Sample Program 2 Courses (list acronym, number, and title) Semester Hours CSCI(STAT) 6375, Foundations of Data Science (NEW) First Year, Fall 4 STAT 6420, Applied Linear Models First Year, Fall 3 STAT 6250, Applied Multivariate Analysis and Statistical Learning First Year, Fall 3 CSCI 6360, Data Science II First Year, Spring 4 CSCI 6380, Data Mining First Year, Spring 4 STAT 6430, Design and Analysis of Experiments First Year, Spring 3 CSCI(ARTI) 8950, Machine Learning Second Year, Fall 4 CSCI 7200, Master’s Project Second Year, Fall 4 STAT 8330, Advanced Statistical Applications and Computing Second Year, Fall 3 Total 32 Sample Program 3 Courses (list acronym, number, and title) Semester Hours CSCI(STAT) 6375, Foundations of Data Science (NEW) First Year, Fall 4 STAT 6420, Applied Linear Models First Year, Fall 3 STAT 8060, Statistical Computing I First Year, Fall 3 CSCI 6360, Data Science II First Year, Spring 4 CSCI 6370, Database Management First Year, Spring 4 STAT 8000, Introductory Statistical Collaboration First Year, Spring 3 CSCI 6795, Cloud Computing Second Year, Fall 4 CSCI 7200, Master’s Project Second Year, Fall 4 STAT 8330, Advanced Statistical Applications and Computing Second Year, Fall 3 Total 32 Sample Program 4 Courses (list acronym, number, and title) Semester Hours CSCI(STAT) 6375, Foundations of Data Science (NEW) First Year, Fall 4 STAT 6530, Statistical Inference for Data Scientists First Year, Fall 3 STAT 6350, Applied Bayesian Statistics First Year, Fall 3 CSCI 6360, Data Science II First Year, Spring 4 CSCI 6150, Numerical Simulations in Science and Engineering First Year, Spring 4 STAT 8210, Multivariate: Theory and Methods First Year, Spring 3 CSCI 6470, Algorithms Second Year, Fall 4 CSCI 7200, Master’s Project Second Year, Fall 4 STAT 8330, Advanced Statistical Applications and Computing Second Year, Fall 3 Total 32 Career opportunities A Master's degree in Data Science opens up a world of exciting career opportunities: Data Scientist: Analyzing data to extract valuable insights and develop predictive models for businesses and organizations. Machine Learning Engineer: Building and deploying machine learning models to automate decision-making processes. Data Analyst: Collecting, cleaning, and visualizing data to support data-driven decision-making. Business Intelligence Analyst: Transforming data into actionable insights to guide business strategies. Data Engineer: Developing and maintaining data pipelines and infrastructure. AI Researcher: Pushing the boundaries of AI and machine learning through research. Consultant: Advising organizations on data-driven strategies and implementations.
