DSA

Subject

DSA

Credits (min)

1

Credits (max)

1

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

Subject

DSA

Credits (min)

5

Credits (max)

5

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

Subject

DSA

Credits (min)

5

Credits (max)

5

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

Organizations leverage Artificial Intelligence (AI) to make decisions, analyze data, speed up processes, and cut costs. There are frequent consequences (both positive and negative) of the use of AI in this capacity that point to several ethical concerns. Through a semester-long qualitative research project, students will explore the ethical use of AI within their chosen industry or field of study. Prerequisites. None. Typically offered in Fall, Spring, and Summer e DSA 225 Data Science for Social Good (1 credit hours) Data science offers powerful tools for addressing a multitude of societal challenges, yet it is no panacea and will require collaboration and commitment from across society to fulfill its promise. Students will investigate the growing use of data science in the social impact space, drawing from real-world examples aligned with the United Nations' Sustainable Development Goals. These examples will span practice areas and approaches, including machine learning, natural language processing, and image recognition. Students will discuss the challenges of implementing data science for social good solutions, including considerations of community involvement, bias, & equity and identify best practices. Typically offered in Fall, Spring, and Summer

Subject

DSA

Credits (min)

1

Credits (max)

1

Credit unit

credit hours

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

Hours DSA 406 Exploratory Data Analysis for Big Data 1 3 DSA 412 Exploring Machine Learning 1

Subject

DSA

Credits (min)

1

Credits (max)

1

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

Subject

DSA

Credits (min)

5

Credits (max)

5

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

Students will explore the crucial interplay between user experience (UX), data analysis, and software success. Through comprehensive usability testing, students will gain experience in analyzing user needs and feedback, and will directly link these insights to the overall success of software projects. Students will utilize data analysis and visualization techniques applied to UX metrics to clearly articulate a detailed understanding of user interactions and satisfaction. Topics include software efficacy, user retention, iterative testing and more, with a focus on effective communication and stakeholder engagement. Students will use techniques from the class and engage directly with users to develop inclusive solutions for diverse user groups and guide data-driven enhancements in the software industry. Typically offered in Fall, Spring, and Summer

Subject

DSA

Credits (min)

1

Credits (max)

1

Credit unit

credit hours

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

hours) Offered as needed to present material not normally available in regular departmental course offerings or for offering new courses on a trial basis. Students may repeat this course under a different topic. DSC 295 (Level 1) courses do not require any previous data science knowledge or skills. Typically offered in Fall, Spring, and Summer

Subject

DSA

Credits (min)

1

Credits (max)

3

Credit unit

credit

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

Finding, cleaning, and preparing data is often required prior to conducting any data analysis. Data wrangling often accounts for the majority of the time spent working with data and learning these concepts is fundamental to the data science process. Students will learn how to manipulate and clean data for analyses and visualizations, read data from web pages, and merge multiple data sets of reasonable sizes. Prerequisite Skills and Knowledge: Students should enter the course with basic knowledge of a programming language (such as R or Python). Typically offered in Fall, Spring, and Summer

Subject

DSA

Credits (min)

1

Credits (max)

1

Credit unit

credit hours

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

Exploratory data analysis (EDA) focuses on summarizing the main characteristics of data sets, often using visualization methods. The goal is not formal modeling or hypothesis testing, but understanding and exploring data to formulate hypotheses for further investigation. Students will use techniques of EDA and generalize those approaches to large data sets. Prerequisite skills and knowledge: Students should enter the course with some basic programming experience, such as experience using and familiarity with R or Python. Typically offered in Fall, Spring, and Summer

Subject

DSA

Credits (min)

1

Credits (max)

1

Credit unit

credit hours

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

hours) Students will prepare to apply for internships for social impact in nonprofits, governmental organizations, and community organizations. As part of this preparation, students will become familiar with tools (such as a data maturity questionnaire) that can help organizations assess their own use of data, and use assessment results to initiate conversations about the organization's data practices and goals. Students will learn about the appropriate scope of projects for an internship, and practice some basic data management, analysis, and visualization through a mini- project utilizing data from real organizations with a focus on social impact. Additional emphases include developing and refining interviewing skills, professional and personal networks, job applications, and job selection. Prerequisite skills: Some elementary data science experience that could be applied in an internship. Typically offered in Fall, Spring, and Summer

Subject

DSA

Credits (min)

1

Credits (max)

1

Credit unit

credit

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

Machine learning (ML) is a fundamental component of artificial intelligence. Students will deconstruct the basic ideas behind popular ML algorithms, such as logistic regression or K-means, using a projects- centered approach. Students will create projects from successful ML use cases tailored to their specific domains of interest. Prerequisite skills and knowledge: Students should have basic knowledge of a programming language (e.g., R, Python, or others), experience with appropriate use of data structures (e.g., lists and matrices), and flow control mechanisms, such as loops. Students should also be familiar with matrix-vector multiplication and the norm of a vector. Typically offered in Fall, Spring, and Summer

Subject

DSA

Credits (min)

1

Credits (max)

1

Credit unit

credit hours

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

3 EC 451 Econometrics II 3 3 ISE 361 Deterministic Models in Industrial Engineering 3 6 ISE 362 Stochastic Models in Industrial Engineering 3 9 ISE 417 Database Applications in Industrial & Systems 3

Subject

DSA

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

Subject

DSA

Credits (min)

1

Credits (max)

1

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

Offered as needed to present material not normally available in regular departmental course offerings or for offering new courses on a trial basis. Students may repeat this course under a different topic. DSC 495 (Level 2) courses are more advanced than DSC 295 (Level 1) courses and have skill-based prerequisites. Students, please note that the course description will include suggested skills and/or topical knowledge. Please prepare accordingly. Typically offered in Fall, Spring, and Summer

Subject

DSA

Credits (min)

1

Credits (max)

3

Credit unit

credit hours

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

An introduction to the R programming environment geared towards the biological sciences. Topics include installation & software setup, programming, and data exploration & analysis, with heavy focus on data visualization [graphics] within R. Students will learn analyses with a focus on shareability and reproducibility within the context of a project or mini projects. Students may bring their own data and use- case datasets will be available as well. Upon completion of the course, students will be prepared to extend their contextual programming skills and understanding into future work involving data science and AI pipelines with a greater understanding of the underlying processes. Although examples focus on biological contexts, learners from all fields are welcome to bring their background knowledge and extend their understanding. [Skill-based prerequisites]: Familiarity with biology and biological research, or research/background knowledge in a field of interest. Typically offered in Fall and Spring

Subject

DSA

Credits (min)

1

Credits (max)

1

Credit unit

credit hours

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu

hours) Offered as needed to present material not normally available in regular departmental course offerings or for offering new courses on a trial basis. Students may repeat this course under a different topic. R: Graduate Standing or permission from the Data Science Academy. Typically offered in Fall, Spring, and Summer

Subject

DSA

Credits (min)

1

Credits (max)

3

Credit unit

credit

Type

course

Edition

2026-2027

Source

catalog.ncsu.edu