Upcoming Courses

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.

Prerequisites: none

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.

Prerequisites: none

Knowing how to analyze your data is only half the job: You need to be able to present your research in a manner that your audience can understand. Presentations should be clear, engaging, and include a minimum of the jargon that can bog down discussion of scientific concepts. This course will raise your awareness of our jargon-infused everyday conversation, introduce concepts for connecting with your audience and meeting them where they are, and offer tips for clear writing and accessible design of graphs and presentation slides.

Prerequisites: none

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

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.

Prerequisites: none

In this course, students will explore the foundations of the NIST Cybersecurity Framework (CSF) and examine the transformative potential of AI across various cybersecurity domains. Students will identify real-world challenges in AI and cybersecurity and develop practical solutions, including a business plan, through an entrepreneurial lens. Key topics include cybersecurity measures, design thinking, market analysis, and prototype development, offering a hands-on approach to solving critical problems in digital landscapes.

Prerequisites: none

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.

Prerequisites: none

A fusion of aerobics, strength, and flexibility with data collection. 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 related insights.

Prerequisites: none

Anyone can be a scientist, regardless of age, background, and where you came from. This can be done through citizen science – a voluntary public participation in the scientific process. Citizen science seeks to answer scientific questions and provide possible solutions to real-world environmental and societal problems. This course is designed for the students to explore available citizen science data and learn how to manipulate and clean data for analyses and visualizations. At the end of the semester, students would be able to enhance their critical thinking and analytical abilities, mainly from hands-on data analytics training, class lectures, learning opportunities from known citizen science experts, and presentation of results to a broader audience.

Prerequisites: none

Grow your skills in network data management, visualization, and analysis. Following data science workflows, students will clean, transform, visualize, analyze, and report on social network data from sources of interest. Upon completion of this course, students will gain marketable foundations in coding and reporting social network phenomena.

While AI provides the opportunity for endless generated work, this course explores what is needed to generate a good story. Through artistic AI creation and play, students will have the opportunity to engage in AI and data generation through applications such as Luma Dream Machine, Midjourney, Leonardo AI, Adobe Firefly, Krea AI, Flux AI, CapCut, and more. Students will develop a semester-long data-diary rooted in reflection and AI image generation to explore the capabilities and capacities of data and AI as tools for storytelling and self expression. Moreover, this course will focus on using the data of self to investigate what gets counted as data, what data looks like, and what data can be used for.

Prerequisites: none

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.

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.

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.

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: Basic programming experience and familiarity with R or Python.

Students will use publicly available data to develop empirical models for predicting the outcome of sporting events. Students will manage, visualize, interpret and communicate statistical and general data processes. Students will apply the R programming language to forecast sporting outcomes with applications to sports betting.

Prerequisites: Familiarity/understanding of ordinary least squares and logistic regression. Familiarity with the R programming language.

For-profit companies, nonprofit service providers and government agencies often use predictive analytics to improve their services. Predictive analytics harnesses historical data and may incorporate machine learning to model future outcomes. Students will explore the value, limitations and ethical considerations of predictive analytics when used to improve services. Using their own dataset or one provided by the instructor, students will learn and apply a practical approach for planning, implementing and assessing a predictive analytics project. The instruction will highlight topics related to data preparation, model training and selection, validation, fairness, transparency and communication of results.

Prerequisites: Students should have familiarity with R Studio and some experience using R to manipulate data and run basic descriptive statistics.

Build your background knowledge into your machine learning tasks with this primer on Bayesian statistical reasoning and computational tools. Students will be introduced to basic Bayesian principles including a collection of standard models. From a Bayesian perspective, students’ data investigation questions of interest will motivate computations based on Markov chain Monte Carlo (MCMC) methods, including the Metropolis-Hastings and Gibbs MCMC algorithms. Students will have the opportunity to write their own computer code to implement these algorithms, train a variety of standard Bayesian models, and will ultimately create a custom course project implementing Bayesian computations for a machine learning task. No prior exposure to Bayesian inference is assumed.

Prerequisites: Basic proficiency in R, Python, or another interpretable programming language (e.g., know how to write a simple function), concepts from introductory statistics such as probability distributions, and measures of location and variability.