This non-thesis curriculum requires completion of a minimum of 31 credits. It is a rigorous blend of courses that deliver the skills students need to collect, evaluate, interpret and communicate data for effective decision-making across a variety of industries, including healthcare, engineering, finance and more.
Your curriculum includes six core courses designed to help you gain an understanding of the computational and statistical foundations of data science:
- COMP 614: Computer Programming for Data Science [3 CREDITS] — An introduction to computer programming designed to give an overview of programming and algorithmic topics commonly seen in Data Science, such creating and manipulating data structures, graphs, dynamic programming, sorting and heuristic search algorithms. Students learn how to think about these problems and how to structure effective solutions to them using Python. No prior programming knowledge is required or expected.
- COMP 642: Machine Learning [3 CREDITS] — Machine learning is the automation of the inductive learning process that humans do so well. Machine learning is critical to the fields of robotics, medicine, security and transportation. In this course that focuses on practical applications, you will gain a foundational understanding of modern algorithms in machine learning.
- COMP 643: Big Data [3 CREDITS] — Data science is the study of how to extract actionable, non-trivial knowledge from data. This course will introduce you to data science and focus on the software tools used by practitioners of modern data science and the mathematical and statistical models that are employed in conjunction with those tools. You will learn how to apply these tools and systems to different problems and domains with a focus on the analysis of “big” data — datasets that are too large to be analyzed on a typical personal computer.
- COMP 665: Data Visualization [3 CREDITS] — Data is being generated by humans and algorithms at an astounding rate. Analyzing and interpreting this data visually is key to informed decision-making across industries. This class will cover the basic ways that various types of data can be visualized and what properties distinguish useful visualizations from not-so-useful ones. You will learn to use Python as both the primary tool for processing the data and for creating visualizations of this data.
- COMP 680: Statistics for Computing and Data Science [3 CREDITS] — Probability and statistics are essential tools in data science and central to fields like bioinformatics, social informatics, and machine learning. They are the foundation for quantifying uncertainty and assessing support for hypotheses and derived models, and are at the heart of areas such as efficiency analysis of algorithms and randomized algorithms. This course covers topics in probability and statistics, including probability and random variables, basic stochastic processes, basic descriptive statistics, and various methods for statistical inference and measuring support.
Enhance your skill set by selecting one nine credit specialization. Program participants can choose from business analytics and machine learning. An image processing specialization is also offered for on-campus MDS students. Specializations are typically three full-semester courses, or six half-semester courses.
- BUSINESS ANALYTICS [9 CREDITS] — Learn to navigate, understand and interpret data and apply it to help improve business performance. In the business analytics customization, you’ll be immersed in a sequence of six 1.5-credit courses that include:
- Introduction to Operations Management: Introduction to the design and integration of successful operations tactics both within the organization and across supply chains. The course focuses on understanding, managing and improving processes and flows of products, customers and information and touches on bottlenecks, inventory, quality management, and strategic issues in operations.
- Introduction to Finance: Introduction to the theory and practice of corporate finance and the analytical tools necessary to answer the most important questions related to firms’ financing and investment decisions, focusing the following building blocks: Valuation, Investment Decisions, Risk and Return, Financing Decisions, Derivative Securities.
- Introduction to Marketing: Introduction to the key concepts underlying the function of marketing and its interaction with other functions in a business enterprise. Explores marketing's role in defining, creating, and communicating value to customers.
- Data-driven Operations: This applied course focuses on the digital transformation of operations management including topics such as process optimization and adaptive decision-making using AI and internet-of-things data and inventory and supply chain management using advanced, data-driven technologies.
- MACHINE LEARNING [9 CREDITS] — Understand the basis for machine learning and how a machine can learn without being programmed. In the machine learning customization, three 3-credit courses will help you gain experience in using machine learning to aid in tasks including data visualization, pattern classification and more:
- Algorithms for Machine Learning: An introduction to the machine learning algorithms that automatically create models from data.
- Statistical Machine Learning: An introduction to how statistical techniques and machine learning can be used to analyze data.
- Deep Learning: An introduction to the multi-stage machine learning methods that learn representations of complex data.
- Data-driven Marketing: This applied course focuses on using customer information to optimize implementation of marketing strategies and measuring success. Topics include digital marketing campaigns, customer experimentation, advanced market research, and pricing.
- Data-driven Finance: This applied course focuses on analytical finance to support business decision-making. This includes applying machine learning and other data analytic tools to improve investment, financing, and risk management decisions.
You’ll further customize your program of study with an elective on either data security, privacy or the social/societal/organizational implications of data analytics.
To give you experience applying your knowledge to a real-world problem, you’ll participate in a capstone project, offered by the Data To Knowledge Lab (D2K). This will help demonstrate skill, collaborative ability and problem-solving acumen.
- DSCI 535: APPLIED MACHINE LEARNING AND DATA SCIENCE PROJECTS [4 CREDITS] — In this project-based course, you gain a unique opportunity to put your new knowledge into practice. You will be part of a student team that will complete a semester-long data science research or analysis project sponsored by a client from across a variety of industries and disciplines. As a team, you will conduct and report on your work, receive and provide feedback and deliver a presentation about your recommendations.
Online Bridge Course
Rice University’s online bridge course is designed to provide you the necessary background in math and programming that will help you succeed in the Master of Data Science program. The six-week long session will give you a head start on mastering technical skills that will ease your transition into the data science master’s degree curriculum. We encourage you to join our non-credit bridge course before you apply, after you submit your application or upon acceptance into the program. Note, enrollment is managed by The Glasscock School of Continuing Studies at Rice University. All courses are taught by computer science faculty. Registration for the bridge course is available now.
Sample Degree Plans
View six example configurations of a Master of Data Science degree plan. These include options with and without summer courses, with and without a computer science or statistics background, and examples for different specializations.