PROFESSIONAL MASTER'S PROGRAM

Master of Data Science

Master of Data Science (MDS):
Online & On-Campus Programs


Rice MDS StudentData science has revolutionized almost every industry, providing some of the most in-demand and highest-paying jobs for graduates in many different fields—science, healthcare, energy, manufacturing, and many others. The availability of data has already changed many disciplines in fundamental ways and soon, virtually all disciplines will require a new type of scientist: a “data scientist” who is not just a statistician, or a computer scientist, or a mathematician, but is well-versed in all of these fields and how they apply to the study of data.

Program Overview

The MDS degree will be offered with both an on-campus and an online option. Students must apply to either the online or on-campus program and will be explicitly admitted to one program or the other.

Rice’s Master of Data Science (MDS) program is designed to support the needs of interdisciplinary professionals who want to apply data science knowledge, theory, and techniques to solve real-world problems.

The program offers:

  • Multidisciplinary, interdepartmental and intercollegiate instruction
  • Customizable, specialized degrees comprised of 31 graduate credit hours
  • Same online & in-person degrees

Program Learning Outcomes

Upon completing the MDS degree, students will have proficiency in:

  • Understanding the computational and statistical foundations of Data Science
  • Knowing and understanding how to use the core methods of Data Science as applied to an area of specialization or across a breadth of areas
  • Applying Data Science knowledge, theory, and techniques to solve difficult, real-world problems, beginning with raw data and ending with actionable insights
  • Effectively communicating written and orally about Data Science methods and results to a lay audience

Curriculum Overview

This non-thesis curriculum requires the completion of a minimum of 31 credits. It is a rigorous blend of courses that deliver the skills you need to collect, evaluate, interpret and communicate data for effective decision-making across a variety of industries.

  • Core Courses:

    Your curriculum includes five core courses designed to help you gain an understanding of the computational and statistical foundations of data science:
    1. Computer Programming for Data Science: This course gives an overview of computer science and teaches students about problem solving in a way that utilizes computation. The course will cover the basics of designing and implementing algorithms to solve computational problems, as well as implementing those algorithms using the Python programming language.
    2. Statistics for Data Science: Probability and statistics are essential tools in data science. They are at the core of areas such as efficiency analysis, randomized algorithms, bioinformatics, social informatics, and, of course, machine learning. This course covers topics in probability and statistics, especially as they related to data science.
    3. Big Data: The course will focus on software tools used by practitioners of modern data science. In particular, this class explores the use of these tools and models in the analysis of “big” data, that is datasets that are too large to be analyzed on a typical personal computer. The course will include an introduction to SQL databases, MapReduce, and Apache Spark.
    4. Machine Learning: Machine learning is the process of automatically learning how to complete a task by analyzing a set of data. This course will focus on providing a foundational understanding of modern algorithms in machine learning, focusing on practical applications. The course includes an introduction to deep learning algorithms and tools.
    5. Data Visualization: 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. The class will use Python as both the primary tool for processing the data as well creating visualizations of this data. To enhance the students’ depth of knowledge, the class will also cover some of the geometric algorithms used to create advanced visualizations
  • Specialization:

    You’ll gain deeper knowledge in data science by choosing a specialization in business analytics or machine learning
  • Elective:

    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.
  • Capstone:

    Then, 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. In this project-based course, student teams will complete semester-long data science research or analysis projects selected from a variety of disciplines and industries. Students will also learn best practices in data science.
  • Specialization:

    Students must complete a 9-credit hour specialization.This is typically three full-semester courses, or six half-semester courses. For example, the business analytics specialization, offered in conjunction with the Jones Graduate School of Business which consists of six, half-semester courses:
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.

Online or On-Campus, which is right for you?

Rice MDS StudentOnline MDS

The Online MDS is a part-time program that allows working professionals to get the same benefits and curriculum of a full-time, on-campus program in an online environment. Students have access to best-in-class materials and resources and can connect with peers and world-class educators. Learn More.

Rice MDS StudentOn-Campus MDS

The On-Campus MDS is a full-time program at the Rice University campus in Houston, Texas. The program hosts a lively and invigorating community of scholars in the Department of Computer Science, the largest academic department at Rice. Learn More.



Engineering Professional Master's Programs

The following professional master's programs also offer non-thesis, advanced degrees involving data science:

  • Master of Computational and Applied Mathematics The Professional Master of Computational and Applied Mathematics (MCAM) is designed for students interested in a technical career path in industry or business.
  • Master in Computational Science and Engineering The Professional Master in Computational Science and Engineering (MCSE) is offered jointly by the Department of Computational and Applied Mathematics, Computer Science and Statistics in the School of Engineering.
  • Master of Computer Science The Professional Master of Computer Science (MCS) degree is a terminal degree for students intending to pursue a technical career in the computer industry.
  • Master of Electrical and Computer Engineering The Department of Electrical and Computer Engineering offers a Professional Master of Electrical and Computer Engineering (MECE) program with a focus in Data Science.
  • Master of Industrial Engineering The School of Engineering offers a Professional Master of Industrial Engineering for students seeking a deeper understanding of how sophisticated decision models can optimize complex systems in any industry as well as the nonprofit sector.
  • Master of Statistics The Department of Statistics offers a Professional Master of Statistics (MStat) program that includes a solid foundation in statistical computing, statistical modeling, experimental design, and mathematical statistics, plus electives in statistical methods and/or theory.
  • Professional Science Master's Program The Subsurface-Geoscience Professional Science Master’s program offers a program focus area in Energy Data Management.