THE ONLINE MASTER OF APPLIED DATA SCIENCE PROGRAMTHE ONLINE MASTER OF APPLIED DATA SCIENCE PROGRAMTHE ONLINE MASTER OF APPLIED DATA SCIENCE PROGRAM

FROM UNC SCHOOL OF DATA SCIENCE AND SOCIETYFROM UNC SCHOOL OF DATA SCIENCE AND SOCIETYFROM UNC SCHOOL OF DATA SCIENCE AND SOCIETY

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 is delivered in collaboration with key academic units on 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

16 months 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

36%+

Projected job growth by 20311

$100,910

Median annual salary in 20212

#3

Best job in the U.S. for 20223

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:

  • Three start dates per year: January, May, August
  • Bachelor’s degree required
  • Skills refresher course available for all applicants

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.

SAMPLE Course Descriptions

The 30-credit curriculum is composed of 10 required courses which include a culminating capstone project. Read sample course descriptions below. Elective courses on Natural Language Processing, Reinforcement Learning, and other relevant topics will be developed and added to this list in the coming months.

  • This course will provide students with advanced concepts on the construction and use of data structures and their associated algorithms. Abstract data types, lists, stacks, queues, trees and graphs. Sorting, searching, hashing and an introduction to numerical error control. Techniques of algorithm analysis and problem-solving paradigms, using relevant programming languages and tools.

  • The course will be coding-oriented and cover the concepts underpinning and the applications of statistical modeling/inference. Students will build models with real-world data and modern data science toolkits. Concepts covered in this course will 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; classification, logistic regression and tree-based methods; prediction, model interpretation, model evaluation.

  • Data is central to data science. The data explosion experienced in every aspect of our lives, from social media to advanced instrumentation, requires a deeper understanding of the full spectrum of data life cycle management. Starting with the concept of ‘what is data?’, the first part of this course introduces various stages of the data life cycle, from defining data requirements, to data creation and gathering, to data fusion and data preparation, to data cleaning and quality control, to exploratory analytics, data interpretation, and visualization. We will explore concepts in FAIR data principles of data curation, metadata, and digital preservation policies with the aim of data reuse and reproducible science. The second part of this course will introduce the concept of relational databases that provide storage and management for structured data. We will explore concepts and implementations of relational database management systems suited for data science applications.

  • This course will be an introductory course to machine learning. The goal is to equip students with knowledge of existing tools for data analysis and to get students prepared for more advanced courses in machine learning. The course will cover core principles of artificial intelligence for statistical inference and pattern analysis. Topics will include probability distributions; graphical models; optimization, maximum likelihood estimation, regression; classification; cross validation; generalization and overfitting; neural networks; nonparametric estimators; clustering; autoencoders; generative models; and kernel methods. Applications in tabular, image, and textual data for supervised and unsupervised learning tasks will be covered.

  • This course will explore intermediate-level design and implementation of database systems with an emphasis on scalable, distributed systems. The course will deepen students’ knowledge of advanced relational database management, followed by discussing current and emerging practices for dealing with big data and large-scale database systems used by many social networking and e-commerce services. 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. Students will gain practice working with real data and multiple modern database technologies.

  • Big data originates in a variety of venues. Access to that data and ownership of it raise social and ethical issues. Models and algorithms are built by humans (and sometimes machines) with potential biases and trained with datasets that may reinforce those biases. Data analytics produces results that allow us to draw conclusions and take action. These uses also have social and ethical implications for practice because consumers of data may not have the tools or the right information to assess, opt-out or contest those results. This course will focus on these issues that arise during the entire life cycle of data from a data governance and applied ethics perspective.

  • This course will present 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, and visualization. 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 will provide students with a foundational understanding of visual perceptional and data visualization design practices, provide instruction on using visualization for tasks such as exploratory analysis and storytelling to support both data-driven discovery and communication. The class will focus on hands-on experiences with commonly used data science tools and technologies.

  • This course will 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 will 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 will complete several substantive programming assignments using relevant tools and frameworks.

  • During your final term, you’ll complete a team-based practicum in which you will learn team science and work on real-world data challenges with two mentors – one academic and one from industry, government, or the nonprofit sector. Program staff will work with you and your faculty mentor to identify an appropriate partner or organization for your capstone project. This capstone will replace a thesis, and the final paper/report and oral presentation will replace an oral exam. The presentation will describe the practicum problem, data and methods used, findings, and any direct and/or indirect outcomes for the organization. The final presentation will be public, with a closed session for the examination from the capstone committee.

“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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1 Data Scientists: Job Outlook. Bureau of Labor Statistics, Occupational Outlook Handbook. Updated September 2022. Retrieved February 2023.arrow_upwardReturn to footnote reference

2 Data Scientists: Pay. Bureau of Labor Statistics, Occupational Outlook Handbook. Updated September 2022. Retrieved February 2023.arrow_upwardReturn to footnote reference

3 50 Best Jobs in America for 2022. Glassdoor, Inc. Retrieved February 2023.arrow_upwardReturn to footnote reference