DASC

16 or CS 153 Programming or Introduction to

Subject

DASC

Credits (min)

4

Credits (max)

4

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

This course investigates how data science is transforming not only our sense of science and scientific knowledge, but our sense of ourselves and our communities and our commitments concerning human affairs and institutions generally. Social implications of the digital revolution, including ethical issues associated with algorithmic design and privacy will be examined. Students will use a sociological lens to explore how our increasingly digital lifestyle changes institutions and social relations.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

or DASC 257 or Data Science or CS 251 Programming or Programming with Java

Subject

DASC

Credits (min)

4

Credits (max)

4

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

This course focuses on problem solving and programming in Python. Emphasis is placed on common algorithms and programming principles utilizing the standard library distributed with Python. Upon completion, students should be able to design, code, test, and debug Python language programs. Old Dominion University Undergraduate Catalog 2025-2026 862

Subject

DASC

Credits (min)

4

Credits (max)

4

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

DASC 157

DASC/PHIL 357E Ethics and Data 3

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

This course explores artistic foundations for visualization including art theory and aesthetics, color theory, composition and layout. We will also discuss the psychology of visual perception, and semiotics and the underlying nature of symbolic representation. Students will gain experience applying these principles by sketching and using technological tools, and will also critically evaluate visualizations based on the theories discussed.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

DASC 300

This course focuses on the design and creation of effective visualizations for communicating data. The Python programming language will be used to create static and interactive visualizations for a variety of data types including tabular, text, and geographic data. Students will develop a portfolio and also will gain experience in analyzing, interpreting, and revising visualizations created by others. Pre- or corequisite: CS 153

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

DASC 300 or permission of the instructor

16 Select two of the following: 6-8

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

This course allows students to work for an employer in a position related to data science. Students must work for 50 hours per course credit and complete course assignments.

Subject

DASC

Credits (min)

1

Credits (max)

6

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

approval by the program coordinator

Data storytelling combines data, narrative, and visualizations to communicate insights and influence decision-making. This course will present the conceptual basis for storytelling and techniques for narrative development, as well as leverage technical skills to analyze and visualize data. By the end of the course, students will have experience in developing cohesive data-driven stories using a variety of platforms and tools.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

DASC 324 or instructor permission

3 Management Approved Major Electives 6 General Elective 3

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Complete an area of specialization (27-30 credits) 27-30 Total Credit Hours 100-109 * Writing Intensive: C or better required. ** Old Dominion University Undergraduate Catalog 2025-2026 498

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

This course is designed to help students enhance their personal and professional development through innovation guided by faculty members and professionals. It offers students an opportunity to integrate disciplinary theory and knowledge through developing a nonprofit program, product, business, or other initiative. The real-world experiences that entrepreneurships provide will help students understand how academic knowledge leads to transformations, innovations, and solutions to different types of problems. The course can be delivered either as an independent project for individual students or as group projects similar to those sometimes offered in topics courses.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

junior standing

The advanced study of selected topics designed to permit small groups of qualified students to work on subjects of mutual interest which, due to their specialized nature, may not be offered regularly. These courses will appear in the course schedule, and will be more fully described in information distributed to academic advisors.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

junior standing

Independent reading and study on a topic to be selected under the direction of an instructor. Conferences and papers as appropriate. DNTH - Dental Hygiene

Subject

DASC

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

senior standing and approval of the program coordinator

The advanced study of selected topics designed to permit small groups of qualified students to work on subjects of mutual interest which, due to their specialized nature, may not be offered regularly. These courses will appear in the course schedule and will be more fully described in information distributed to academic advisors.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Independent reading and study on a topic to be selected under the direction of an instructor. Conferences and papers as appropriate.

Subject

DASC

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

approval of the program coordinator

This course provides foundational programming skills essential for future coursework in data science. Designed for students with little to no prior programming experience, it develops essential skills in programming and problem-solving, equipping students with the ability to manipulate data, perform basic analyses, and create visualizations. The course emphasizes writing clean, efficient, and reproducible code.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Requirements will be established by the School of Data Science and will vary with the amount of credit desired. Allows students an opportunity to gain a short duration career-related experience.

Subject

DASC

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Departmental permission required

Students demonstrate an ability to integrate and synthesize competencies from their certificate or degree program coursework applied to concentration areas. Students produce high quality written products and an e-portfolio that demonstrate the analysis, synthesis and intersection of AI knowledge with specific domains.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Good academic standing (Graduate GPA of at least 3.0)

The culminating course in the proposed MS in Data Science and Analytics degree program will bring students together with faculty and external partners. In consultation with a faculty advisor and a business or industry or government representative, students will be required to develop a project that aims to solve a data science/analytics problem in a real-world business, industry, or government setting. Faculty and business/industry/government representatives will serve as external mentors for the students during this experience. Note that an external mentor is not mandatory but encouraged. Pre- or corequisite: DASC 620/CS 620, CS 624, CS 625, and STAT 603

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Provides the advanced student with an opportunity to study and investigate a variety of topics in the field of data science.

Subject

DASC

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Permission of the instructor

Independent study under the direction of an instructor.

Subject

DASC

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Permission of the instructor

Departmental permission required

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Departmental permission required

12 Applications

Subject

DASC

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course introduces the basic concepts of computational imaging. The topics include principles of imaging systems, role of computational methods in enhancing imaging systems, computational imaging inverse problems, and data-driven machine learning approaches to solve inverse problems in computational imaging. experience

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Knowledge of linear algebra and prior programming

Learning and Explainable AI Total Credit Hours 12

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills t develop innovative AI solutions that address real-world challenges in health sciences.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience

Provides the advanced student with an opportunity to study and investigate a variety of topics in the field of data science.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course introduces the basic concepts of computational imaging. The topics include principles of imaging systems, role of computational methods in enhancing imaging systems, computational imaging inverse problems, and data-driven machine learning approaches to solve inverse problems in computational imaging. experience

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Knowledge of linear algebra and prior programming

Explainable AI (3 Credit Hours) Laws in many countries and states within the U.S. require that predictive models impacting humans be accompanied by an understandable interpretation, yet many such models are based on so called black box models that can’t be easily interpreted or explained. This course will enable students to produce explanations and interpretations for advanced ML and AI algorithms. It will review the state of the science methods for interpretable ML and explainable AI, including graphical and contextual approaches as well as model agnostic and model specific methods for generating understandable explanations and interpretations. The course will also introduce the concepts of algorithmic bias and model fairness as they relate to explanation and understanding.

Subject

DASC

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

BDA 511/611, or CS 522, or CS 580

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI o into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience

Provides the advanced student with an opportunity to study and investigate a variety of topics in the field of data science. DHSC - Doctor of Health Sciences

Subject

DASC

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu