Use data to drive decisions

The Online M.S. in Data Science (MSDS) offers an integrated curriculum centered on excellence in data science that features interdisciplinary study and practical, hands-on learning projects. Designed outside the traditional curricular structure, the MSDS is a forward-looking blueprint of the world in which data science plays an increasingly important role. 

We believe you shouldn’t have to compromise if you want to pursue a rigorous MSDS program without disrupting your life or job. Our online MSDS program is rigorous, but flexible with timing. It is time-tested, well-known, and taught by professors with vast experience in industry and academia.

The MSDS curriculum is tightly prescribed, and students take a core set of courses throughout the year with two electives built into the schedule. Courses are interdisciplinary and taught by faculty at the School of Data Science and from across the University of Virginia. Several large data sets are used between courses to increase the program’s cohesion, and students work consistently in teams throughout, building strong relationships with their peers and faculty mentors.   

Curriculum

The Online MSDS program is designed around a spiral learning framework. Students begin by acquiring a foundation in languages, computation, and linear modeling. They then build upon those skills and explore Bayesian machine learning, statistics, data mining and text analytics, computer programming, and data ethics, as well as interdisciplinary electives from across the University. Throughout the program, students apply what they learn and practice effective communication skills through hands-on group projects. 

At the conclusion of the MSDS program, students address an important data science challenge through a sponsored team capstone project. Projects connect you with corporate and government partners who present unique challenges for you to tackle through hands-on learning. By applying the tools and techniques learned in the classroom, you gain real-world experience while providing the sponsoring organization valuable data-driven insights and solutions.

The 32-credit-hour M.S. in Data Science program is offered across five terms. Students take a core set of courses with elective courses offered in semesters 4 and 5. For course details and descriptions by term, see below.  

Weekly Breakdown

The Online M.S. in Data Science Program employs a flipped classroom model to accommodate the needs of students balancing professional and academic goals. Flipped classrooms are learner-focused environments that allow students to explore course topics independently, outside of class, using asynchronous course content. Live classroom time serves as a forum for class discussion aimed at deepening student understanding of course material. Students also have the option to attend live faculty office hours and group study sessions throughout the week.

Each week, students can expect one hour of live synchronous time per course, which will be scheduled in the evenings during weekdays. On a weekly basis, most part-time online MSDS students typically spend seven to nine hours per course completing asynchronous content, which includes homework, projects, and readings. However, the workload can vary by student and by course, and total hours may depend on learning styles and level of comfort with program prerequisites.

Semester 1

6 credit hours

DS 5100: Programming for Data Science (3 credits)

An introduction to essential programming concepts, structures, and techniques. Students will gain confidence in not only reading code, but also learning what it means to write good quality code. Additionally, essential and complementary topics are taught, such as testing and debugging, exception handling, and an introduction to visualization. This course is project based, consisting of a semester project and final project presentations.

STAT 6021: Linear Models for Data Science (3 credits)

An introduction to linear statistical models in the context of data science. Topics include simple, multiple linear regression, logistic regression, and generalized linear models. The primary software is R. Data wrangling in R will also be covered.

Semester 2

6 credit hours

DS 6001: Practice and Application of Data Science (3 credits)

This course covers data science practice, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Case studies will explore the impact of data science across different domains.

DS 6030 Statistical Learning (3 credits)

This course covers fundamentals of data mining and machine learning within a common statistical framework. Topics include regression, classification, clustering, resampling, regularization, tree-based methods, ensembles, boosting, and Support Vector Machines. Coursework is conducted in the R programming language.

Semester 3

6 credit hours

CS 5012: Foundations of Computer Science (3 credits)

Provide a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency for non-CS, non-CpE students. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security).

DS 6040: Bayesian Machine Learning (3 credits)

Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of world problems. This class covers the major related techniques, including Bayesian inference, conjugate prior probabilities, naive Bayes classifiers, expectation maximization, Markov chain monte carlo, and variational inference.

Semester 4

7 credit hours

DS 6050: Deep Learning (3 credits)

A graduate-level course on deep learning fundamentals and applications with emphasis on their broad applicability to problems across a range of disciplines. Topics include regularization, optimization, convolutional networks, sequence modeling, generative learning, instance-based learning, and deep reinforcement learning. Students will complete several substantive programming assignments.

DS 6011: Data Science Capstone Project Work I (1 credit)

This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects.

Elective 1 (5000-level or higher, at least 3 credit hours)*

Semester 5

7 credit hours

DS 6002: Ethics of Big Data (2 credits)

This course examines the ethical issues arising around big data and provides frameworks, context, concepts, and theories to help students think through and deal with the issues as they encounter them in their professional lives.

DS 6013: Data Science Capstone Project Work II (2 credits)

This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects.

Elective 2 (5000-level or higher, at least 3 credit hours)*

Possible Electives

DS 5001: Exploratory Text Analytics (3 credits)

Introduction to text analytics with a focus on long-form documents, such as reviews, news articles, and novels. Students convert source texts into structure-preserving analytical form and then apply information theory, NLP tools, and vector-based methods to explore language models, topic models, sentiment analyses, and narrative structures. The focus is on unsupervised methods to explore cognitive and social patterns in texts.

DS 5110: Big Data Systems (3 credits)

This course will focus on Spark, an open-source, general-purpose computing framework that is scalable & fast. Fundamental data types & concepts are covered. You will learn how to use Spark for large-scale analytics & machine learning, among other topics. Tools for data storage and retrieval are covered, including AWS.

SARC 5400: Data Visualization (3 credits)

Thinking with Images. People have been looking at data for centuries – with their eyes – to discover patterns, meaning, and insight into the most important challenges of their time. This course teaches visual and spatial thinking coupled with visual data tools and interactive web coding to envision information. Far beyond plotting, finding ways to respond to complex problems, we will study and make useful, compelling, and beautiful tools to see.

*Development of additional elective courses are planned.

The Graduate Record represents the official repository for academic program requirements.

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