UGA’s Departments of Computer Science and Statistics in the Franklin College of Arts and Sciences have launched a new Bachelor of Science degree in Data Science. Learn about our new interdisciplinary Data Science major, which is at the interface of computer science and statistics.

Overview of the Major: The major will provide necessary background in mathematics and build a strong foundation in Data Science, covering data structures, algorithms and database management, data collection, data mining, machine learning, modeling, and inference. Students graduating with a B.S. in Data Science 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 experiential learning via a capstone course, which will focus on applying the acquired knowledge and skills in a real-world data analytics project.

Student Learning Outcomes: All graduates will be able to:

  • Develop software, algorithms; design and manage a variety of databases and structures, process data in distributed environments;
  • Collect and analyze the data using techniques from statistics, data mining, machine learning;
  • Provide visualizations of the data and build statistical models to facilitate inference.
  • Interpret results of statistical analysis and assist decision makers

Career Opportunities: All graduates earning the B.S. in Data Science degree will learn the essential skills necessary to pursue careers in a variety of data-oriented companies [e.g., computing/internet companies (Google, Amazon, Facebook, IBM); engineering companies (Intel, Samsung, Boeing); finance/insurance (Goldman Sachs, AIG, Liberty Mutual); pharmaceutical companies (Johnson & Johnson)]; government/national labs (NASA, NIST, DoD) or pursue graduate studies.

Course Requirements: The course requirements for the B.S. in Data Science are listed below. The coursework consists of at least 68 semester hours with 19 hours of Foundation (Area VI) and at least 49 hours of major coursework.

C.1. Foundation Courses (Area VI, 19 credit hours)

CSCI 1301 / 1301L (4 hours) - Introduction to Computing and Programming

CSCI 1302 (4 hours) - Software Development

CSCI 2150 / 2150L (4 hours) - Introduction to Computational Science

CSCI 2725 (4 hours) – Data Structures

STAT 2010 (3 hours) – Statistical Methods for Data Scientists

C.2. Major Required Courses (at least 37 credit hours)

CSCI 3360 (4 hours) - Data Science I

CSCI 4260/6260 (4 hours) - Data Security and Privacy

CSCI 4360/6360 (4 hours) - Data Science II

CSCI 4380/6380 (4 hours) or STAT 4250 (3 hours) - Data Mining or Applied Multivariate Analysis and Statistical Learning

CSCI 4370/6370 (4 hours) - Data Base Management

STAT 4220 (3 hours) - Applied Experimental Designs

STAT 4230/6230 (3 hours) - Applied Regression Analysis

STAT 4355/6355 (3 hours) or STAT 4365/6365 (3 hours) - Advanced Statistical Programming or Modern Statistical Programming

STAT 4510/6510 (3 hours) – Mathematical Statistics I

STAT 4530 (3 hours) – Statistical Inferences for Data Scientists

STAT (CSCI) 4990 (3 hours) - Data Science Capstone

C.3 Major Elective Courses (choose 12 hours from the list below)

CSCI 3030 (3 hours) - Computing, Ethics, and Society

CSCI 4050/6050 (4 hours) - Software Engineering

CSCI 4150/6150(4 hours) - Numerical Simulations in Science and Engineering

CSCI 4210/6210 (4 hours) - Simulation and Modeling

CSCI 4470/64709(4 hours) - Algorithms

CSCI 4850/6850 (4 hours) - Biomedical Image Analysis

CSCI 5007/7007 (3 hours) – Internship in Computer Science Business/Industry

FINA 3001 (3 hours) – Financial Management

MARK 3001 (3 hours) – Principles of Marketing

MARK 4350 (3 hours) - Marketing Analytics

MARK 4650 (3 hours) – Digital Marketing Analytics

MATH (CSCI) 4690 (3 hours) - Graph Theory

MGMT 3001 (3 hours) – Principles of Management

MIST 5730 (3 hours) - Advanced Data Management

RMIN 4000 (3 hours) - Risk Management and Insurance

STAT 4240/6240 (3 hours) - Sampling and Survey Methods

STAT 4260/6260 (3 hours) - Statistical Quality Assurance

STAT 4280/6280 (3 hours) - Applied Time Series Analysis

STAT 42906290 (3 hours) - Nonparametric Methods

STAT 4360/6360 (3 hours) – Statistical Software programming

STAT 4620/6620 (3 hours) - Applied Categorical Data Analysis

STAT 4710/6710 (3 hours) - Introduction to Probability Theory I

STAT 4720/6720 (3 hours) - Introduction to Probability Theory II

STAT 5700/7700 (3 hours) – Internship in Statistics

The program of study will require 120 credit hours to complete. A sample program of study will be posted shortly.