Data Science Certificate


Program Overview

The Wake Forest University Certificate in Data Science (DS) program seeks to train and mentor students to become well qualified scientists and researchers. The certificate provides training in algorithms for structured and unstructured datasets, as well as statistical modeling techniques for such datasets. Students will study the theory and application of databases, data processing, data mining, statistical modeling and statistical learning.

Students who successfully complete the DS Certificate Program will receive a certificate in Data Science, as well as a degree in any other graduate programs in which they matriculate. The Certificate Program is implemented by collaboration among the programs of Computer Science and Mathematics & Statistics at Wake Forest University.

To indicate interest in this program, please complete the form linked here.  One of the faculty members involved in the DS Certificate Program will reach out to you regarding the program. The current co-directors of the program are Dr. Sam Cho (Department of Computer Science) and Dr. Rob Erhardt (Department of Mathematics and Statistics).


Currently enrolled Wake Forest Graduate students, following matriculation and at least one semester of coursework in a graduate program, can apply for admission to the DS Graduate Certificate Program.​ Admission to the Certificate Program is initiated by meeting with one of the DS co-directors. The student will then submit a letter of intent and a Wake Forest University graduate transcript to the DS admissions committee. The letter of intent should express the student’s interest in the DS program, a proposed plan of study, and how the DS program meets the student’s career and academic goals. Following favorable evaluation, applicants may be recommended for admission by the DS admissions committee, with final approval determined by the Graduate School. Students not enrolled in a Wake Forest graduate program may also apply directly to the DS Certificate Program.

Prior to admission, applicants must have completed coursework (or demonstrate sufficient background) in calculus, linear algebra, and introductory statistics, as well as computer programming and also a background course covering data structures, algorithms, and complexity (material equivalent to CSC 201). Gaps in student preparation should be discussed with the program co-directors.

Students must take 15 credits of data science courses, with two courses selected from the area of Statistical Modeling and Statistical Learning, two courses selected from the area of Computational Data Science, and one course from a set of defined electives. The courses falling into each area are described on the next tab named Course Requirements.

The DS Co-Directors are tasked with approving a student’s plan of coursework. Students enrolled in the Interdisciplinary Graduate DS Certificate Program as well as another graduate program must complete all graduate degree requirements in the individual department to which they were admitted. Courses from the areas of Statistical Modeling and Statistical Learning and Computational Data Science taken from outside a student’s home department do not count towards both the DS certificate and the home degree program.

Area A: Statistical Modeling and Statistical Learning (select two from the following)
STA 612 Linear Models
STA 662 Multivariate Statistics
STA 663 Statistical Learning

Area B: Computational Data Science (select two from the following)
CSC 621 Database Management Systems
CSC 622 Data Management and Analytics
CSC 673 Data Mining
One, but not both, of:
CSC 674 Machine Learning
CSC 675 Neural Networks and Deep Learning

Electives (select one from the following)
One additional graduate elective selected from STA, an approved course from MST, or a CSC course selected from:
Any CSC course listed in but not taken as part of fulfilling the Area B requirements
CSC 652 Numerical Linear Algebra
CSC 655 Numerical Methods
CSC 646 Parallel Computation
CSC 647 GPU Programming
CSC 671 Artificial Intelligence
CSC 726 Parallel Algorithms