UTSA Undergraduate Certificate in Data Science
With the rise of big data, there’s a real demand for specialized knowledge in data science. Understanding how to gather, analyze, interpret, and visualize data is a valuable skill in all disciplines.
Details
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.
Industry
There is an industry need for more data-science capable professionals, with job titles including:
Data Analyst
Business Intelligence
Developer
Upon completion, you will be prepared to apply data science in real-world in areas like:
Business
Engineering
Education
Defense and Security
Natural Sciences
Humanities
Health Care
Social Sciences
and many others!
Certificate Requirements
- Complete 5 courses (15 semester credit hours)
- Online asynchronously (at your own time)
- No prior knowledge in programming or advanced mathematics required
Courses
DS 4003
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.
DS 4013
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.
DS 4023
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.
DS 4033
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.
STA 3003
Applied Statistics
Students can take either STA 3003 or DS 3023 to fulfill the certificate requirements.
Prerequisite: Completion of MAT 1093 (or equivalent)
Corequisite: Either MAT 1133 or MAT 1214 (or equivalents)
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. The course will emphasize data analysis and interpretation and effective communication of results through reports or presentations.
DS 3023
Statistical Analysis for Data Science
Students can take either DS 3023 or STA 3003 to fulfill the certificate requirements.
Prerequisite: MAT 1073 or the equivalent.
Introduction to the scientific method; principles of sampling and experimentation; scales of measurement; exploratory data analysis; basic probability; models for discrete and continuous data; simple simulations and inferences based on resampling; fundamentals of hypothesis testing and confidence intervals; analysis of variance and linear regression model; tensors and matrices. The course will emphasize data analysis and interpretation and effective communication of results through reports or presentations within data science contexts.
This certificate is administered by the University College in conjunction with UTSA's School of Data Science.
For more information, email datascience.cert@utsa.edu.
