Strength in Numbers

The online MSDS is based on the same principles and rigorous course of study as our on-Grounds program. Students will move through a varied curriculum drawing from different disciplines, all with the unifying goal of becoming leaders in data science and analysis. Coursework includes quantitative methodology as well as a humanist component and plenty of collaboration to ensure that our students approach data science holistically, as part of a team, and with the utmost integrity.

MSDS Curriculum (2 years approximately, 12 graded courses, 32 credit hours)*

  • MSDS students will be enrolled automatically by the SDS into all courses for the online MSDS program.
  • For each course, students can expect one weekly hour of live synchronous time, which will be scheduled in the evenings during weekdays. Approximately 15 to 20 hours of related course work (asynchronous sessions, homework, readings, etc.) can be expected for two 3 credit hour courses (the typical part-time course load). However, this can vary course-to-course and based on a student’s background.
  • A minimum of B- in each class and a cumulative GPA of 3.00 are required to meet degree requirements.

*The MSDS curriculum is evolved each academic year to keep up with industry standards and therefore is subject to change.

DS 5100: Programming for Data Science (3)

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)

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.

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

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)

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.

CS 5012: Foundations of Computer Science (3)

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)

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.

DS 6050: Deep Learning (3)

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)

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)*

DS 6002: Ethics of Big Data (2)

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)

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)*

DS 5001: Exploratory Text Analytics (3)

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)

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)

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.