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Dynamic, innovative, cross-disciplinary instruction

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Access to courses on timely and relevant topics

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Ability to customize coursework to achieve specific career goals

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Strong industry partnerships and employment opportunities

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CECS Programs

UT currently offers a Data Science minor and a Global Development minor, which will become part of CECS offerings. Beginning in Fall 2023, CECS will offer courses in artificial intelligence, cybersecurity, and data science so students can enroll now to begin their academic journey in these emerging fields.

The following courses are available Fall 2023 and Spring 2024:

Artificial Intelligence

AI 101 Introduction to the World of AI (Fall 2023 and Spring 2024)
3 credit hours. Fall 2023: MWF 10:20 am – 11:10 am
Introduction to foundational concepts, techniques, and applications of Artificial Intelligence (AI) relevant for all disciplines – especially across non-computer science fields. Explores the history and current scope of AI, data sources and tools, and fundamental components of AI solutions. Special attention will be placed on the strengths and weaknesses of the methods as well as on identifying bias, social impacts, and other ethical considerations of AI. Students will gain experience building applications through hands-on activities using no-code AI platforms. Students will also explore the capabilities of modern AI tools such as ChatGPT and DALL-E.
Prerequisite(s): none


3 credit hours. Fall 2023: TR 5:40 pm – 6:55 pm
Provides foundational knowledge and training in cybersecurity concepts. Students will explore a broad overview of the field incorporating the nature of cyber-attacks on computers and networks. Students will also work to identify and mitigate attacks, from the perspective of applications in real-world scenarios and sectors. Ethics, privacy, governance and policy, and human factors in cybersecurity are also covered. Students will gain understanding and appreciation of terminology, approaches, and underlying technologies used in cybersecurity.
Prerequisite(s): None.

Data Science

DATA 201 Data Knowledge and Discovery (Fall 2023 and Spring 2024)
3 credit hours. Fall 2023: MWF 11:30 am – 12:20 pm
Introduction to the essential elements of data science through the examination of data sets drawn from a variety of fields. Explores data collection and management, exploration and visualization of data, modeling, computing, and larger issues of data science. Introduces students to programming through hands-on activities. Satisfies Vol Core requirement.
Prerequisite(s): None.
DATA 202: Data Management and Visualization (Fall 2023 and Spring 2024)
3 credit hours. Fall 2023: MWF 9:10 am – 10:00 am
Introduction to foundational concepts and techniques in the management and presentation of data for effective data-informed decision making. Explores data storage and indexing strategies, data warehousing, metadata management, visualization of time-series and geospatial data, and best practices for presenting data to inform decision making, such as heat maps and infographics.
Prerequisite(s): DATA 201.
DATA 301: Data Stewardship and Ethics (Fall 2023 and Spring 2024)
3 credit hours. Fall 2023: TR 11:20 am – 12:35 pm
Overview of the data life cycle, including creation, collection, assurance, description, discovery, integration, use, reuse, and preservation. Explores data management principles and the development and implementation of data life cycle management plans. Examines the legal, ethical, and technological challenges in developing and implementing data management policies.
Prerequisite(s): None.
DATA 302: Analytical Methods of Data Science (Fall 2023 and Spring 2024)
3 credit hours. Fall 2023: TR 2:30 pm – 3:45 pm
Survey of modern algorithms and methods in data science, focusing on how, why, and when various methods work. Includes topics in statistics, machine learning, and optimization.
Prerequisite(s): DATA 202.

Interdisciplinary Work

ECS 501 Introduction to Transdisciplinary Research Concepts (Spring 2024)
3 credit hours. Spring 2024: Days and Times TBD
The course will explore through case studies the process of transdisciplinary team science. Explores collaborative definition of transdisciplinary research questions; team assembly, function, and support; liberating structures for team science; and how disciplinary, multidisciplinary, and transdisciplinary efforts complement each other to advance foundational knowledge. Case studies will be borrowed from ongoing projects across the UT system. Student assignments will include being asked to envision what their own field/research could (additionally) contribute to each project as a way to gain experience in creative, collaborative, and supportive team growth.
Prerequisite(s): none

For more information about the Data Science minor, please contact Xiaopeng Zhao at

Learn More About CECS