Certificate in Data Science
Apply computing to scientific and engineering analysis and problem solving in this certificate program, using statistics, data visualization, and machine learning. Data science has far-reaching applications in engineering, medicine, business, education, and more.
Who should apply?
The Certificate in Data Science is intended for students who possess a Bachelor of Science in a Science, Technology, Engineering, or Mathematics (STEM) field, but who lack sufficient background in Data Science to be admitted to a Master of Science in Data Science program.
Program of study
The certificate program in Data Science consists of core and elective courses from an approved list. This certificate is jointly administered between the Departments of Computer Science and Electrical and Computer Engineering. Learn more.
The Certificate in Data Science requires five courses, including:
- EE 104: Probabilistic Systems Analysis (3 semester-hour units, or SHUs)
- COMP 135: Introduction to Machine Learning (3 SHUs)
- One elective in each of the following areas:
- Data infrastructure (including COMP 112: Networks, COMP 115: Database Systems, COMP 116: Introduction to Computer Security, COMP 117: Internet-Scale Distributed Systems, COMP 118: Cloud Computing, and COMP 151: Special Topics in Data Infrastructure and Systems) (3 SHUs each)
- Data analytics and/or interfaces (including COMP 152: Special Topics in Data Infrastructure and Systems, COMP 167: Computational Biology, COMP 171: Human Computer Interaction, COMP 175: Computer Graphics, COMP 177: Visualization, and ME 150: Applied Mathematics for Engineers (3 SHUs each); and CEE 187: Geographical Information Systems (4 SHUs))
- Computational and theoretical aspects of data analysis (including COMP 136: Statistical Pattern Recognition, COMP 153: Special Topics in Computational and Theoretical Aspects of Data Science, and COMP 236: Computational Learning Theory (3 SHUs each); MATH 123: Mathematical Aspects of Data Analysis, MATH 155: Nonlinear Dynamics and Chaos, and MATH 158: Complex Variables (3 SHUs each); MATH 126: Numerical Analysis, MATH 128: Numerical Linear Algebra, MATH 152: Partial Differential Equations II, MATH 161: Probability, and MATH 162: Statistics (4 SHUs each); and EE 109: Convex Optimization, EE 127: Information Theory, and EE 140: Stochastic Processes, Detection, and Estimation (3 SHUs each))
- In addition, any undergraduate prerequisites required for these courses may be taken as part of the certificate.
Please see the Graduate Programs website for information about current tuition rates.