UNC-Chapel Hill Master of Applied Data Science | Online

The online Master of Applied Data Science (MADS) program,  identified by Fortune Education as one of the best online master’s in data science for 20241 equips you to effectively and ethically collect, process, manage and analyze data — and translate your insights into a clear narrative that business, government and social leaders can use to drive action.

  • Graduate in as few as 4 terms. Master the latest data science tools, such as advanced programming methods and machine learning applications.
  • Build Practical Experience — Solve data challenges in collaboration with industry, government and community partners and mentors.
  • Practice Working in Teams — Complete group projects and a collaborative capstone, which will prepare you for team-based workplaces.

Apply Data Insights to Solve Today’s Greatest Challenges

At the School of Data Science and Society at the University of North Carolina at Chapel Hill, we view data as the universal language of collaboration. 

The online Master of Applied Data Science (MADS) program gives you a holistic understanding of the data life cycle, preparing you to effectively — and ethically — collect, process, manage and analyze data. Ultimately, you will be able to translate your insights into a clear narrative that business, government and social leaders can use to drive action.

Drawing on Carolina’s deep well of interdisciplinary research and faculty expertise, the program was initially conceptualized and developed in collaboration with key academic units across campus, including:

  • School of Information and Library Science
  • Department of Biostatistics at the Gillings School of Global Public Health
  • Department of Computer Science, Department of Mathematics and Department of Statistics and Operations Research at the College of Arts and Sciences 

Build the technical expertise and real-world experience to advance your data science career and impact lives in North Carolina and beyond.

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The Master of Data Science at a Glance

concentrations

10 courses, 1 optional immersion

calendar

4 TERMS to complete full-time

Real-world, team-based capstone

waiver

GRE scores not required

How You Get Career-Ready

UNC-Chapel Hill has deep ties with regional, national and global partners in industry, government and the nonprofit sector. This in turn gives us direct insight into the needs of employers. The data science master’s program incorporates these insights to position you for success in the data science workforce. Learn more about what data science is.

Learn Modern Skills and Tools

The curriculum teaches the latest data science trends, skills and tools being used today, such as advanced programming methods, big data and NoSQL databases, machine learning applications, AI ethics and more.

Gain Practical Experience

In the capstone, you’ll work with industry, government and community partners and mentors to solve real-world data challenges. Faculty and staff will leverage their networks to identify projects and organizations.

Practice Working in Teams

Data scientists must be able to clearly communicate with stakeholders, and solve problems as part of an interdisciplinary team. Group assignments and a team-based capstone arm you for collaborative work.

Data Science Careers Are on the Rise

By acquiring the skills and experience to become a data scientist, you’ll position yourself to meet employers’ increasing demand for data-driven discovery and decision making — and advance in a fulfilling career where you can have a practical, measurable impact. 

Data Scientist Job Stats

35%+

Projected job growth by 20322

$112,590

Median annual salary in 20243

Discover more data science jobs.

Learn How to Advance Your Data Science Career

Solving the grand challenges of our time requires the accurate and ethical application of data. Request information to learn more about Carolina’s approach to applied data science.

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Admissions: Master’s of Data Science Online

UNC-Chapel Hill seeks motivated analytical thinkers who are passionate about using data to solve problems and improve people’s lives. The online Master of Applied Data Science is a practice-based program and is geared more toward practitioners than researchers.

A working knowledge of programming, statistics, and linear algebra is required.

Admissions Highlights:

  • Two start dates per year: January and August
  • Bachelor’s degree required
  • Skills refresher course available for all applicants
  • Students enroll in either two courses per term (Standard track) or three courses per term (Accelerated track)

No GMAT/GRE scores are required.

Review upcoming deadlines and a complete list of application requirements for the online master’s of data science.

Online Master’s of Data Science Curriculum

The online Master of Applied Data Science curriculum equips you to thoughtfully leverage every stage of the data life cycle — collecting, curating, interpreting, visualizing and applying — to identify and tell a story through data. 

Building on a foundation of data governance and ethics, as well as a continual focus on real-world applications, you will:

  • Dive into the programming and statistical foundations of data science
  • Explore the data life cycle and advanced methods to work with big data as well as relational and non-relational databases
  • Master advanced methods in machine learning, deep learning and natural language processing
  • Explore the important possibilities of data visualization and communication
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Taught by Faculty Across Disciplines

The School of Data Science and Society serves as an interdisciplinary hub that brings together many academic units across UNC-Chapel Hill. This culture of collaboration gives you a holistic perspective on the practical and ethical ramifications of data science work.

Courses in the online master’s of data science program are taught by world-class faculty with expertise across a wide variety of disciplines, including computer science, statistics and operations research, mathematics, biostatistics, information and library science, health sciences, bench sciences, omics, political science, public health and environmental science.

COURSES OFFERED

The 30-credit curriculum is composed of 10 required courses which include a culminating capstone project. Students should be prepared to enroll in two courses per term for our Standard track and three courses per term for our Accelerated track. Read required course and elective course descriptions below.

  • This course discusses foundational stages in the data science lifecycle, using a common modern toolkit, and introducing methods and implementations of relational database management systems suited for data science applications. Students gain fluency across the full data science workflow, while also developing data science habits (version control, documentation, critical thinking about data, among others) expected in modern data science projects.

  • This course provide students with advanced concepts on the construction and use of data structures and their associated algorithms. Concepts such as abstract data types, lists, stacks, queues, trees and graphs are discussed. Algorithms for sorting, searching, hashing are covered along with an introduction to numerical error control.

  • The course coding-oriented course covers the concepts underpinning and the applications of statistical modeling/inference. Students build models with real-world data and modern data science toolkits in Python and R like Scikit-learn and TidyModels. Concepts covered in this course include:

    • Foundations in probability including basic rules, bayes formula, basic distributions
    • Sampling and the central limit theorem
    • Bootstrapping, confidence intervals, hypothesis testing, multiple testing
    • Linear models, basic and multiple regression, inference for regression, regularization
    • Prediction, model interpretation, model evaluation
    • Classification, logistic regression and tree-based methods

  • This course explores the foundational concepts of ethics in data science and AI. This overview sets the stage for a deep understanding of what ethical frameworks mean in practice, providing students the opportunity to create actionable examples. By focusing on a wide variety of case studies throughout a myriad of industries and settings, this class develops leaders who can effectively integrate and leverage data science solutions while ensuring responsible and transparent use of data in a variety of roles.

  • This course presents the mathematical intuition, theory, and techniques driving the numerical computation methods used for processing and analyzing data in various real-life problems. Topics include dimensionality reduction, linear and non-linear approximation, frequency and wavelet analysis, and a glimpse into the mathematics of deep neural networks, classification, large-scale and high-performance numerical computing. Each topic will be motivated by a data analysis challenge, introduce the mathematical intuition, theory and techniques used to address it, and conclude with a coding component with real data.

  • This course equips students with knowledge of existing tools for predictive analytics and foundational concepts in machine learning. The course covers core principles of machine learning and pattern analysis. Topics includes maximum likelihood estimation, regression; classification; cross validation; generalization and overfitting; introduction to neural networks; nonparametric estimators; clustering; tree-based methods; autoencoders; kernel methods. Applications in tabular, image, and textual data for supervised and unsupervised learning tasks are covered.

  • This course provide students with an in-depth look at deep learning fundamentals and applications with emphasis on their broad applicability to problems across a range of disciplines. Students learn to tackle practical issues that arise during the life cycle of data, both in the cloud and on the Edge. Topics include regularization, optimization, convolutional networks, sequence modeling, generative learning, instance-based learning, and deep reinforcement learning. Students complete several substantive programming assignments using relevant modern tools and frameworks.

  • Masters of Applied Data Science (MADS) students are required to complete a capstone project. Capstone projects challenge students to acquire and analyze data to solve real-world problems using techniques they have learned in the program. Project teams consist of three to four students that work together with a project sponsor to create a plan and execute it, culminating in a final presentation and delivery of a demonstrable artifact such as a dashboard or codebase.

Electives Offered

Students will also have access to several elective courses. Read the sample elective course descriptions below.

  • This course explores intermediate-level design and implementation of database systems, emphasizing scalable, distributed systems. Hands-on exercises in the course deepen students’ knowledge of advanced relational database management and discuss current and emerging practices for dealing with big data and large-scale database systems. Concepts include design and implementation of relational databases, exploration of distributed data structures including graph, document, and key-value storage models and scalable and resilient query processing.

  • This course equips participants with practical tools to estimate causal effects in real-world settings. After building a solid formal foundation, students will learn to design experiments, leverage natural experiments, and analyze observational data using modern causal inference methods. Ideal for those who want to move beyond predictive analytics in order to answer causal questions in their work.

  • This course provides students with a foundational understanding of visual perception and data visualization design practices. Students gain expertise on using visualization for tasks such as exploratory analysis and storytelling to support both data-driven discovery and communication. The class focuses on hands-on experiences with commonly used data science tools and technologies.

  • This course introduces the design and operation of machine learning systems in cloud environments. Students gain hands-on experience deploying and monitoring models at scale, building data pipelines, and applying distributed computing. Emphasis is placed on leveraging cloud technologies for efficient data handling, AI applications, and end-to-end life cycle management of ML solutions.

  • This course prepares data scientists with a strong foundation in machine learning to master the implementation and deployment of advanced AI systems in production environments. Reflecting the rapidly evolving landscape of AI, students will gain hands-on experience with state-of-the-art LLMs, advanced RAG architectures, production-grade agent frameworks, comprehensive security testing, cost optimization strategies, and industry-standard MLOps/LLMOps practices. The course emphasizes security as a foundational requirement, not an afterthought, and prepares students to build, deploy, and maintain production AI systems that are secure, scalable, cost-effective, and ethically sound.

  • This course covers statistical and machine learning methods for the analysis of biological and health-related data. Students will examine the structure, provenance, and challenges of diverse biomedical data types, including genomic and epigenomic data, population and public health data, electronic health records, and biomedical imaging. Emphasis is placed on selecting, applying, and interpreting statistical methods for hypothesis testing, association analysis, and predictive modeling in biologically meaningful contexts. Students will critically assess sources of bias, uncertainty, and methodological limitations in biomedical data science and develop skills for communicating results and caveats effectively to both computational and experimental scientists.

  • This course introduces methods for analyzing data that vary across space and time. Students learn to visualize, model, and interpret spatial data, time series data, and integrated spatiotemporal data, with attention to spatial dependence, heterogeneity, trends, and seasonality. Using modern data science tools, students apply statistical and data science techniques to real-world spatial, temporal, and spatiotemporal datasets. The course culminates in applied projects that demonstrate professional-level analysis and communication of spatiotemporal results.

PROGRAM TRACKS

Students should expect to enroll in two courses per term in the Standard track and three courses per term in the Accelerated track. Explore the program tracks below to find the option that best fits your schedule and your goals.

Standard/Part-Time Track

Accelerated Track

“At the School of Data Science and Society, we envision a world made healthy, safe, and prosperous for all, through data-informed decisions. Our focus on society allows us to utilize innovative ways to use foundational and translational data science for the public good.”

Stanley C. Ahalt, Ph.D.

Dean, UNC School of Data Science and Society

Carolina’s Approach to Online Learning

UNC-Chapel Hill’s world-class faculty and partnerships with technology experts have positioned us to design a truly collaborative, research-based online learning experience. 

  • Interact with diverse peers and faculty in live online classes, held on Zoom at convenient times for working professionals.
  • Access a sophisticated digital campus, where you can adjust settings, view grades, and complete multimedia assignments.
  • Find comprehensive support services, including academic advisors, faculty mentors, tech support, career guidance and more. 

You’ll also have the option to participate in a non-credit immersion — an in-person experience held on campus in Chapel Hill, North Carolina — that exposes you to new perspectives and strengthens your relationships with classmates and professors.

With years of in-person and online teaching experience, Carolina faculty know how to foster meaningful connections and adapt to evolving workforce and student needs.

Take the Next Step in Your Data Science Career

The online Master of Applied Data Science is designed for analytically minded professionals who want to have a greater influence at work — and help solve today’s grand challenges in North Carolina and beyond. Request information to learn more about the program today.

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FAQs About UNC’s Online Master of Applied Data Science (MADS)

  • Data science is a broad and interdisciplinary field that uses artificial intelligence and machine learning to create predictive models that uncover future insights. Data analytics is actually a subset of that field. It focuses more on processing historical information to identify trends, create visualizations, and solve specific, immediate business problems.

  • At UNC Chapel Hill, our Master of Applied Data Science (MADS) is an online, practice-based professional degree that focuses on the direct application of data science to real-world challenges. A traditional Master of Science (MS) in Data Science tends to emphasize theoretical foundations and deep statistical rigor. Those programs often serve as a pathway toward PhD studies or specialized research roles rather than immediate industry application.

  • To be eligible for the MADS program at UNC Chapel Hill, you must hold a bachelor’s degree from an accredited institution. While not strictly required, the admissions team recommends that you have foundational knowledge in statistics, programming, and linear algebra. You do not need to submit GRE scores for admission, but you will need to provide your transcripts, a résumé or CV, and a statement of purpose.

  • If you choose to study full-time, the program is designed to be completed in 16 months. However, the curriculum is built with flexibility in mind to accommodate working professionals. Many students choose a part-time track, and you have a maximum time limit of five years to finish all degree requirements.

  • Yes, math is a fundamental part of data science. The field relies on concepts from statistics, linear algebra, calculus, and probability to build accurate models and interpret complex datasets. While modern code can automate many of the actual calculations, having a strong conceptual understanding of the math is crucial for developing algorithms and advancing in your career.

  • Coding is a vital tool for almost every task a data scientist performs. However, it is important to note that many entry-level or mid-level roles may not require you to write code immediately upon being hired. In those cases, you might rely on existing tools and dashboards to analyze your specific datasets.

  • Data science is an IT-adjacent field that is deeply connected to Information Technology. It relies heavily on computer science, programming languages like Python and R, and data engineering tools to extract insights. While it is an interdisciplinary role that includes business strategy and statistics, data scientists frequently work alongside IT professionals to build models and solve business problems using modern IT infrastructure.

  • For most students, a Master’s in Data Science is a worthwhile investment for career advancement and higher earning potential. It provides a structured learning environment, valuable networking opportunities, and a strong professional portfolio. This is especially true for those looking to switch fields or secure leadership roles in a rapidly growing industry where many graduates see immediate salary increases and promotions.

  • A MADS degree qualifies you for several high-demand technical and leadership roles. Some of the top career paths include:
    – Machine learning engineer 
    – Data architect or data engineer 
    – Business analyst or enterprise architect 
    – Applications architect or machine learning scientist

  • Your potential earnings in this field will depend on several factors, including your level of experience and your geographic location. According to data from the Bureau of Labor Statistics, the overall median salary for a data scientist is $112,590. Your chosen industry also impacts your income. For example, the median salary for those working in computer systems design is $128,020, while the median for professionals in the insurance sector is $108,920.

  • The choice between these two degrees depends on your personal career goals. If you want a quicker path into management or are looking to shift your career focus entirely, an MBA might be more suitable. On the other hand, a Master’s in Data Science is generally better for those with a strong quantitative background who want to move into highly technical or specialized roles.

University of North Carolina at Chapel Hill partners with 2U to support the delivery of this online program. University of North Carolina at Chapel Hill has full control over the program, including all core academic functions. Click here to learn more about 2U’s roles and responsibilities.

1 Best Online Master’s in Data Science Programs. Fortune Education (2024).arrow_upwardReturn to footnote reference

2 Data Scientists: Job Outlook. (2024) U.S. Bureau of Labor Statistics. Retrieved March 2026.arrow_upwardReturn to footnote reference

3 Data Scientists: Pay. (2024) U.S. Bureau of Labor Statistics Retrieved March 2026.arrow_upwardReturn to footnote reference