UTSA Undergraduate Certificate in Data Science
This certificate is designed for undergraduate students from all academic backgrounds to build strong analytical and computational foundation to investigate data science problems. Completing this certificate will help you gain the foundational data science knowledge as well as practical skills in data curation, data analytics, data visualization, data mining, and machine learning.
You may work toward this certificate as part of an existing degree program (i.e., in elective credits or as an actual part of their degree) without having to change your major.
Note for Departments: You may permit students to use one or more equivalent courses as substitutes for the prescribed courses. Departments also have the flexibility to incorporate this certificate program into their existing programs if they desire.
- Complete 5 courses (15 semester credit hours)
- Online asynchronously (at your own time)
- No prior knowledge in programming or advanced mathematics required
Introduction to Data Science
Prerequisite: MAT 1023, MAT 1043, MAT 1053, MAT 1073, or the equivalent, or consent of instructor. An introduction to the Data Science life cycle and concepts associated, to include ethics. Focus areas on data visualization, data curation, and tools available for analysis will be covered.
Programming for Data Science
Prerequisite: MAT 1073 or the equivalent. An introduction to computer programming emphasizing structured programming, problem solving, and algorithmic thinking. Topics may include assignment, decisions, loops, functions, arrays/lists, and use of math/stat packages.
Data Organization and Visualization
Prerequisite: DS 4013 or the equivalent. This course focuses on programming concepts that are involved in integrating, loading, processing, and transforming data from external sources for exploratory data analysis and visualization. Topics may include extended programming concepts, file input/output, recursion, searching algorithms, and data visualization using data science software packages and APIs.
Data Mining and Machine Learning
Prerequisite: DS 4013. This course utilizes fundamental data science concepts to introduce in-depth analysis, data mining, machine learning, and artificial intelligence. Topics may include clustering, classification, evaluation metrics, supervised and unsupervised learning, search algorithms, intelligent agents, and AI applications in select areas.
Prerequisite: Completion of or concurrent enrollment in MAT 1093,MAT 1133, MAT 1214, or an equivalent. Introduction to the Scientific Method; principles of sampling and experimentation; scales of measurement, exploratory data analysis; introduction to basic probability; models for discrete and continuous data; simple simulations and inferences based on resampling; fundamentals of hypothesis testing and confidence intervals; introduction to analysis of variance and linear regression model. The course will emphasize data analysis and interpretation and effective communication of results through reports or presentations.