DTSC 1301Data and Society A3
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
University of North Carolina at Charlotte · Courses
DTSC
23 courses with the subject DTSC, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DTSC 1302Data and Society B3
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 2301Modeling and Society A3
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 2302Modeling and Society B3
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 3000Special Topics in Data Science. (3) Special topics in
interdisciplinary data science. Topic varies by semester and instructor. May be repeated for credit with change of topic.
- Subject
- DTSC
- Type
- course
- Repeatable
- May be repeated for credit with change of topic.
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 3201An Introduction to Large Language Models (LLMs)3
Prerequisite(s): DTSC 2301, DTSC 2302. Introduction to the fundamentals of Large Language Models (LLMs), including their design, capabilities, limitations, and ethical concerns. Students will use Python and popular libraries like Hugging Face Transformers to explore how LLMs generate and process text. The course blends technical, hands-on learning with broader discussions about the social impact of AI.
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 3400Data Science Practicum (1 to 3)3
or EXER 3900 - Undergraduate Research (1 to 3) (must be taken for or SPOA 3400 - Sports Analytics Practicum (1 to 3) or SPOA 3800 - Independent Study in Sports Analytics (1 to 3) (must be taken for 3 credits)
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Credit unit
- credit hours
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 3601Predictive Analytics and Their Implications A3
Prerequisite(s): DTSC 2301, DTSC 2302, and ITSC 2214. Corequisite(s):
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 3602Predictive Analytics and Their Implications B3
Mathematics and Statistics Courses (15 credit hours)
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 3800Independent Study. (1 to 3) Supervised investigation of a
problem or subject in the field of Data Science. To be arranged with the instructor, generally during the preceding semester. May be repeated for credit with change of topic.
- Subject
- DTSC
- Type
- course
- Repeatable
- May be repeated for credit with change of topic.
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 3900Undergraduate Research. (1 to 3) Prerequisite(s)
Permission of department. Students make progress on a new or existing original research project, studying a topic in data science, under the guidance of a faculty member. May be repeated for credit up to three times. Course Descriptions | 649
- Subject
- DTSC
- Type
- course
- Repeatable
- May be repeated for credit up to three times.
- Repeatable up to
- 3
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 4000Special Topics in Data Science. (3) Special topics in
interdisciplinary data science. Topic varies by semester and instructor. May be repeated for credit with change of topic.
- Subject
- DTSC
- Type
- course
- Repeatable
- May be repeated for credit with change of topic.
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 4301Data Science for Social Good A3
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 4302Data Science for Social Good B3
Unrestricted Elective Courses, Double Major and/or Minor to Complete Requirements for Graduation Students must earn a minimum of 120 credit hours to graduate. This requirement can be fulfilled through a combination of elective courses, a minor, or a double major. The 120-credit minimum must be satisfied regardless of the specific academic path chosen (e.g., a minor combined with electives). Degree Total = 120 Credit Hours
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 8000Special Topics in Data Science3
Prerequisite(s): DTSC 8110, DTSC 8120, DTSC 8130, DTSC 8140, DTSC 8600. Examination of major theories, methods, and issues in Data Science. Instructional method(s) vary according to topic, course objectives, and instructor. May be repeated for credit with change of topic.
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Repeatable
- May be repeated for credit with change of topic.
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 8110Statistics for Data Science3
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 8120Fundamentals of Machine Learning3
Prerequisite(s): DTSC 8110 or permission of School of Data Science. Foundational methods, mathematics, and algorithms needed to do research and applications in machine learning. Practical perspectives and applications of machine learning methods will be covered, including the development of new skills through instruction and practice; combined with an overview of the business to identify, model, retrieve, and evaluate enterprise data and knowledge assets. Focuses on the understanding of data and knowledge management and data mining using techniques such as Markov Decision Processes, Decision Trees, Supervised and Unsupervised Learning, Reinforcement Learning, Logistic Regression, and other business intelligence concepts. The course will discuss machine learning from organizational, technological, and management perspectives.
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 8130Ethics, Security, Privacy, & Governance of
Data for Social Good (3)
- Subject
- DTSC
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 8140Fundamentals of Artificial Intelligence3
Prerequisite(s): DTSC 8120; Enrolled in the Ph.D. in Data Science program or permission of the school. Foundations of AI techniques and their applications in various real- world domains and how to implement a system with intelligent functionality. Students will learn to judge when intelligent functionality and AI may be a good solution for a problem and be able to choose suitable AI methods and techniques.
- Subject
- DTSC
- Credits (min)
- 3
- Credits (max)
- 3
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 8600Research Design for Data Science2
Prerequisite(s): Enrolled in the Ph.D. in Data Science program or permission of the instructor. The foundations of knowledge discovery and knowledge creation through data science. This comprises the discussion of the overall research process, research concepts, designs, and methodologies used in data science, including scientific reasoning, the role of models and other forms of evidence-based knowledge creation such as experimental designs, deductive, inductive and abductive processes, and others. Lectures, readings, and discussions will teach how to ask pertinent questions, develop and apply relevant data-based research designs, interpret the world through the lens of data, and convey findings to diverse audiences.
- Subject
- DTSC
- Credits (min)
- 2
- Credits (max)
- 2
- Type
- course
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 8601Data Science Research Seminar. (1) Seminar
introduction to the frontier of data science research in various domains of applications. It is structured with 3 to 4 modules through the semester, each one dedicated to a different body of literature. Students will gain research proficiency in the domains of application in focus during the semester. Topics will rotate with each offering. May be repeated for credit with change of topic.
- Subject
- DTSC
- Type
- course
- Repeatable
- May be repeated for credit with change of topic.
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 8800Independent Study in Data Science. (1 to 4)
Prerequisite(s): DTSC 8140, DTSC 8120, DTSC 8110, DTSC 8130. Individual research in specific topics of Data Science under the direction of a faculty member. May be repeated for credit with change of topic up to maximum allowed by program.
- Subject
- DTSC
- Type
- course
- Repeatable
- May be repeated for credit with change of topic up to maximum allowed by program.
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
DTSC 8900Dissertation Research. (1 to 9) Under the
direction of a dissertation advisor and committee, students design and execute an original research study. This study should address a significant issue or problem within the field of Data Science. May be repeated for credit to reflect research activities of the student.
- Subject
- DTSC
- Type
- course
- Repeatable
- May be repeated for credit to reflect research activities of the student.
- Edition
- 2026-2027
- Source
- catalog.charlotte.edu
Source: University of North Carolina at Charlotte's catalog, linked per course · table learning_unit · CourseShelf publish 61