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Upcoming DSA Courses

See what upcoming courses are being offered in Spring 2024.

Spring 2024 Courses

200 – 295 level courses – No prerequisites

Students will develop introductory skills in R and Python needed for data science. Topics include data types, data structures, control structures, good coding practices, and reproducible coding. Students will become acquainted with basic data science techniques and their implementations in R and Python. Skills acquired in this course serve as a foundation for many of the Data Science Academy classes that suggest some experience with R or Python.

Visualizations can be one of the most effective means to communicate quantitative information. Students will cover the principles of effective visualization and how to interpret data displays. Students will evaluate current examples in the media and learn tools for creating static, interactive, and dynamic data displays.

Employers want to hire people who can communicate to a wide variety of audiences. Many disciplines have their own specialized language or jargon. It is easy to fall into the habit of using that jargon to communicate data analysis results or concepts to the public, or to “cherry-pick” and selectively present data or results to convey a specific idea. Students will fine-tune their awareness regarding the use of discipline-specific language. Students will present scientific results and concepts clearly, unambiguously, and with a minimum of jargon.

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.

A fusion of aerobics, strength, and flexibility with data analysis. Students will participate in health and fitness activities and collect data on their physiological responses to assess their progress throughout the semester. Parameters include 20 measures such as urinalysis test results, training heart rate, recovery heart rate, temperature, blood pressure, components of the Functional Movement Screening Assessments, site body fat indicators, and more. This data will be collected at regular intervals throughout the semester and used in a project to investigate health trends and communicate the data informed stories of students’ fitness progress.

Students will discover how organizations leverage data from diverse sources like market research, web analytics, user testing, and surveys to achieve success. Equipped with these insights, students will be empowered to shape and enhance strategic decision-making within their current or future department or company, driving it towards the right trajectory. Students will cover various phases of software design and effective data gathering techniques, which will enable them to adeptly communicate and influence strategic decisions. Through collaborative group activities and individual assignments, students will apply and refine these skills. By the course’s end, learners’ capacity to contribute to their team or organization’s decision-making processes will be greatly amplified.

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.

The vast majority of individuals in the U.S. have been exposed to cybersecurity threats ( Personal and economic effects can be devastating. Students will investigate the use of data to discover, explore, and address relevant cybersecurity use cases. Students will become familiar with fundamental approaches to tackle common cybersecurity problems using Python.

400 – 495 level courses – Skill-based prerequisites

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.

Prerequisites: Students should enter the course with basic knowledge of a programming language [such as R or Python].

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.

Prerequisites: Students should enter the course with some basic programming experience, such as experience using and familiarity with R or Python.

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.

Prerequisites: 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.

Quantum computing promises to revolutionize the field of computation by leveraging the unique principles of quantum mechanics to solve intractable problems in a wide range of industries and scientific disciplines. Students will survey and deconstruct the fundamental ideas behind the most promising quantum computing algorithms and their associated domain problems. Topics include algorithms and problems with some “near-term” viability, like the Variational Quantum Eigensolver in Quantum Chemistry and the Quantum Approximate Optimization Algorithm in Combinatorial Optimization. A design principle that these algorithms share is the conscious use of quantum resources and data that they generate. Students will apply this design principle to explore the challenges of working with “quantum” data in connection to relevant use cases where quantum data naturally occur.

Prerequisites: Students should have basic knowledge of the Python programming language, data structures, such as lists and matrices, and flow control mechanisms, such as loops. Students should also be familiar with concepts covered in a first semester of a linear algebra course such as systems of linear equations, vectors, matrices, vector spaces, eigenvalues, and linear transformations.

APACHE Spark has become the industry-prevalent Big Data framework, and is the core engine in Databricks. Students will take an introductory hands-on approach to processing datasets up to 6 TB in size with this framework on NC State’s High Performance Computing center. Spark applications like Natural Language Processing, Structured Streaming, SQL, MLib, PySpark, SparkR, and GraphX will be covered. Participants will also accelerate Spark NLP on GPUs.

Prerequisites: Students should enter the course with programming experience and familiarity with R or Python.

595 level courses – Skill-based prerequisites, Research readiness

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.

Prerequisites: Familiarity with biology and biological research.

This course provides introduction and an in-depth exploration of neural networks and reinforcement learning, covering both their theoretical foundations and practical applications. We will use case studies and paper readings to understand classical machine learning topics, like AlphaGo, basics of ChatGPT that is based on reinforcement learning, diffusion models (generative artificial intelligence techniques used in DALL-E vs. Midjourney) and diffusion policy. Students will gain a solid understanding of these advanced machine learning techniques, equipping them with the knowledge and skills necessary to design and implement reinforcement learning algorithms using neural network models.

Prerequisites: Students are preferred to have a basic understanding of matrix operations, statistics and probability, and programming in Python. Proficiency in these areas is not required but suggested to better engage with the course material.

Course Search

Go to Class Search for delivery format and offered times. Choose DSC as the Course Subject.

Where do DSC courses count in your program:

DepartmentProgramDSC Courses for Credit
College of Sciences
Department of Biological SciencesB.S. in Biological SciencesIntro to R/Python & Big Data courses for Additional Science and Math category
Bio Sci with MCDIntro to R/Python & Big Data courses for MCD electives
GeneticsAll Courses can be applied to Restricted Electives
ZoologyAll Courses can be applied to Additional Science and Math
Paleontology MinorIntro to R/Python & Big Data courses
Department of MathematicsMajor Electives (12-15 hrs)All Courses
Free Electives (12-15 hrs)All Courses
Department of StatisticsAdvised ElectivesAll Courses - except Intro to R/Python
Master of Microbial BiotechnologyApplied Science ElectiveUp to 2
DSC 405: Data Wrangling and Web Scraping
DSC 406: Exploratory Data Analysis for Big Data
DSC 495: by permission
DSC 595: by permission
College of EducationLearning, Design and TechnologyDSC 201, 202 and 205 are accepted in this program
Poole College of Management
Business Analytics Honors ProgramB.S. Business Admin program3 credit hours of DSC courses
Intro to R/Python
Exploratory Data Analysis for Big Data
Data Wrangling and Web Scraping
Wilson College of Textiles
Textile Engineering, Chemistry and Science DepartmentTextile Technology DegreeDSC Courses count as Advised Electives (3-6hrs)
Polymer Color Chemistry (PCC) degreeWith permission DSC Courses count as Advised Elective Group II or II
College of Engineering
Computer Science14 CSC BSOther Restricted Electives 200+: DSC 200-295
Other Restricted Electives 300+: DSC 400-595
AI ConcentrationSame as 14 CSC BS in addition to:
AI Restricted Electives: DSC 405, 406, 410 and 412
Cybersecurity ConcentrationSame as 14 CSC BS in addition to:
DSC 295 and 495 courses related to Cybersecurity, Data Governance, etc. can count for CSC 297
Games ConcentrationSame as 14 CSC BS in addition to:
DSC 295/495 topics appropriate for Games. Connect with your advisor.

Steps for Faculty/Staff Course Registration