University of North Carolina at Charlotte · Courses

DSBA

31 courses with the subject DSBA, 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.

DSBA 5122Visual Analytics and Storytelling3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6010Special Topics in Data Science and Business

Analytics (3)

Subject: DSBA
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6100Big Data Analytics for Competitive

Advantage (3)

Subject: DSBA
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6112Graduate Econometrics3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6115Statistical Learning with Big Data3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6122Decision Modeling and Analysis3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6155Knowledge-Based Systems3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6156Applied Machine Learning3

Design Computation Concentration Elective Courses (23 credit hours) Select from ITCS, ITIS, ARCH, or DSBA courses. Software and Information Systems Concentration Core Courses (9 credit hours) A GPA of 3.6 or above is required for these courses. Advanced versions of the core courses can be used as substitutes upon permission from both the student’s academic advisor and the program coordinator. Core course requirements must be met during the first 30 credit hours within the program. Select three of the following:

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6160Database Systems for Data Scientists3

Cross-listed Course(s): HCIP 6160. Prerequisite(s): Full graduate standing and enrollment in M.S. or Graduate Certificate in Data Science program.. The modeling, programming, integration, and provenance of big data. Focuses on SQL and NoSQL, but may also address other advanced topics. Topics include: (1) modeling/theory: basics of RDBMS and NoSQL, database design; (2) programming: SQL and NoSQL query languages; (3) integration: data warehousing, preprocessing; (4) databases in the Cloud; and (5) provenance: data version control, data lifecycle management.

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6162Knowledge Discovery in Databases3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6165Artificial Intelligence and Deep Learning3

Capstone (6 credit hours)

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6170Ethics, Privacy, Security and Governance of

Big Data (3)

Subject: DSBA
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6171Data Engineering for AI3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6180AI and Model Risk Management3

Prerequisite(s): DSBA 6156 or DSBA 6211; Proficiency with Python is assumed. Surveys major risk elements in quantitative predictive modeling, covering basic steps and considerations in model development. Topics include performance measures, variable screening, transformation, and selection, with a focus on improving models to better control risks. Causality, over-fitting/under-fitting, interpretability, explainability, and sensitivity analysis are emphasized. Advanced methods such as LIME, SHAP, integrated gradients, and assessments of robustness, reliability, and fairness are also covered.

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6188Text Mining and Information Retrieval3

Cross-listed Course(s): ITIS 6188. Introduces the general computational techniques for analyzing large amounts of text data, as well as basic techniques for information retrieval. By introducing text mining (TM) and information retrieval (IR) in a unified framework, this course emphasizes the importance of integration of IR and TM needed to build the two major types of text information systems, i.e., search engines (such as Google, MS Bing,

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6190Cloud Computing for Data Analysis3

Prerequisite(s): DSBA 6160 and DSBA 6156, or permission of instructor; proficiency with Python and/or R, SQL, Unix, data structures, machine learning algorithms, and statistics; good programming skills and a solid mathematical (linear algebra) background. Introduction to the basic principles of cloud computing for data analysis and data-intensive applications. Covers a broad range of technologies and solutions used in designing cloud-based data platform architectures and in performing analysis on massive data sets. Focuses on the scalable deployment of cloud resources and the integration between individual services. Topics covered will include the deployment and use of data architectures with data lakes and data warehouses, containerized applications, distributed computing using cluster technologies such as Apache Spark, machine learning, and deep learning using scalable/GPU-based infrastructure. Course may be taught on Microsoft Azure, Amazon Web Services, or another large enterprise cloud provider, depending on the instructor.

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6201Business Intelligence and Analytics3

Prerequisite(s): Full graduate standing and enrollment in the DSBA M.S. or Graduate Certificate program. An overview of the business approach to identifying, modeling, retrieving, sharing, and evaluating an enterprise's data and knowledge assets. Focuses on the understanding of data and knowledge management, data warehousing, data mining (including rule-based systems, decision trees, neural networks, etc.), and other business intelligence concepts. Covers the organizational, technological and management perspectives.

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6207Business Project Management3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6208Supply Chain Management3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6211Advanced Business Analytics3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6213Applied Healthcare Business Analytics3

Cross-listed Course(s): HCIP 6213 and MBAD 6213. Prerequisite(s): Demonstrated proficiency with Microsoft Excel; STAT 5123, HADM 6108 (for students with background in healthcare administration), HCIP 6108 (for students with background in healthcare informatics), or equivalent. Focuses on applying business analytics within the healthcare setting. Students learn analytical tools used to synthesize big data into meaningful management information that is used in making key business decisions that impact the delivery of healthcare services. Case studies are utilized to prepare students for delivering boardroom level presentations of their findings.

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6276Strategic Business Analytics3

Elective Courses (9 or 12 credit hours) At least one course must be from the list of Data Science Elective Courses and one must be from the list of Business Analytics Elective Courses. Data Science Elective Courses

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6277Social Media Marketing and Analytics3

Cross-listed Course(s): MBAD 6277. Prerequisite(s): Full graduate standing and enrollment in the DSBA M.S. or Graduate Certificate program. The utilization of social media in marketing strategy and tactics. Topics include: the use of social media in building brand strength and equity, as a customer acquisition tool, and as a customer relationship management tool. The utilization of analytics in effective social media marketing.

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6284Digital Marketing Analytics3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6322Complex Adaptive Systems3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6326Network Science3

Business Analytics Elective Courses

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6335Agentic AI3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6345Modern Data Science Systems3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6390Data Science and Business Analytics Project

Practicum (3)

Subject: DSBA
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6400Internship3

Subject: DSBA
Credits (min): 3
Credits (max): 3
Type: course
Edition: 2026-2027
Source: catalog.charlotte.edu

DSBA 6991Master Thesis Research. (1 to 5) Focused

academic research resulting in the development, writing, and defense of a master's thesis. Must be taken over two or more semesters. May be repeated for credit but no more than six hours may be applied to M.S. degree requirements. Variations in credit distribution across semesters are permissible (e.g., 1+5, 3+3, 2+2+2).

Subject: DSBA
Type: course
Repeatable: May be repeated for credit but no more than six hours may be applied to M.
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