Data Science and Big Data Concentration

Students in the B.S. in Computer Science program may opt to declare a concentration in “Data Science and Big Data.” For most students, this concentration can be obtained with careful selection of appropriate elective courses, not requiring any additional courses beyond the regular BS requirements. The concentration will be an official degree designation that is shown on a student’s transcript, indicating that the student has studied key courses in this in-demand field.

The Data Science and Big Data concentration is designed to provide students key knowledge of appropriate theories, algorithms, and technologies, towards development of analytical systems/models for disparate, complex, and small/large scale datasets. Students completing this concentration will have learned skills necessary to tackle a wide variety of data-focused scientific, social, and environmental challenges.


The requirements shown below are from the 2022-23 UNCG catalog. Students follow the requirements in the catalog that was in effect when they declared their major program of study, so existing computer science majors will follow the requirements that were in place when they declared their C.S. major (adding the concentration does not change your catalog year). Current students can check in degree works to find what catalog year they are following, and can refer to the catalog archive for requirements. The Catalog Year Policy has official information about the catalog year requirement, and existing students can talk with their advisor to learn about the options available to them.

New students and students who declared a computer science major for Fall 2022 or later will follow the requirements below, as listed in the 2022-23 UNCG catalog. Students must satisfy all requirements for the BS in Computer Science, and must complete the following courses:

Required (Take Both):

  1. CSC 405 – Data Science
  2. CSC 410 – Big Data and Machine Learning

Elective Courses (Take One – 3 credits):

  • CSC 407 – Network Analysis
  • CSC 416 – Digital Image Processing
  • CSC 417 – Deep Learning in Computer Vision
  • CSC 425 – Bioinformatics
  • CSC 429 – Artificial Intelligence
  • CSC 454 – Algorithm Analysis and Design
  • CSC 474 – Principles of Data Mining
  • STA 431 – Introduction to Probability
  • STA 435 – Theory of Linear Regression