Data Science Undergraduate Certificate
Certificate Requirements
The undergraduate certificate in Data Science is a five-course (15 credit hour) program. It provides skills, both statistical and computational, and technologies for the growing and popular fields involving data science and analysis. A student pursuing this certificate can choose from one of the two tracks, the computational track or the statistical track. Each track consists of three required courses (9 credit hours) and two additional elective courses (6 credit hours).
Computational Track
| Required Courses | ||
| CMP SCI 4200 | Python for Scientific Computing and Data Science | 3 |
| CMP SCI 4340 | Introduction to Machine Learning | 3 |
| CMP SCI 4342 | Introduction to Data Mining | 3 |
| Electives | ||
| Choose two of the following courses: | 6 | |
| Introduction to Data Visualization | ||
| Introduction to Intelligent Web | ||
| Introduction to Statistical Methods for Data Science | ||
| Introduction to Artificial Intelligence | ||
| Introduction to Biological Data Science | ||
| Introduction to Deep Learning | ||
| Database Management Systems | ||
| Exploratory Data Analysis with R | ||
| Introduction to High-dimensional Data Analysis | ||
| Bayesian Statistical Methods | ||
| Introduction to Statistical Computing | ||
| Introduction to Stochastic Processes | ||
| Total Hours | 15 | |
Statistical Track
| Required Courses | ||
| MATH 4200 | Mathematical Statistics I | 3 |
| MATH 4210 | Mathematical Statistics II | 3 |
| MATH 4250 | Introduction to Statistical Methods in Learning and Modeling | 3 |
| or CMP SCI 4340 | Introduction to Machine Learning | |
| Electives | ||
| Choose two of the following courses: | 6 | |
| Introduction to Intelligent Web | ||
| Python for Scientific Computing and Data Science | ||
| Introduction to Artificial Intelligence | ||
| Introduction to Evolutionary Computation | ||
| Introduction to Machine Learning (if course not used above) | ||
| Introduction to Data Mining | ||
| Introduction to Biological Data Science | ||
| Introduction to Deep Learning | ||
| Exploratory Data Analysis with R | ||
| Introduction to High-dimensional Data Analysis | ||
| Bayesian Statistical Methods | ||
| Introduction to Statistical Computing | ||
| Introduction to Statistical Methods in Learning and Modeling (if course not used above) | ||
| Introduction to Stochastic Processes | ||
| Total Hours | 15 | |
A minimum of three courses must be taken from UMSL. Courses may be substituted with the permission of the certificate coordinator. For more information, contact the department chair or email info@arch.umsl.edu.
Learning Outcomes
Upon completion the program, certificate earners will be able to:
- Identify, interpret, and manage the computational issues involved in the handling of large volumes of data
- Apply algorithmic principles and statistical theories to analyze data-sets
- Build and evaluate data-based models
- Apply machine learning techniques to data-mining problems