Rice MDS Online Curriculum:
Designed for You

DATA SCIENCE ONLINE MASTER'S PROGRAM

Master of Data Science Course Curriculum

The online Master of Data Science curriculum at Rice University features instruction by data science experts and a best-in-class online teaching methodology. Academic rigor ensures you will be well-prepared to meet the demands of employers, while the program’s collaborative and engaging format supports a positive learning experience.

The non-thesis curriculum requires the completion of a minimum of 31* credit hours, including five core courses designed to help you gain an understanding of the computational and statistical foundations of data science. You’ll then complete your data science specialization in Machine Learning or Business Analytics and further customize your program of study with electives in database management, ethics, cybersecurity, and/or security and privacy.

Finally, to give you experience applying your knowledge with real-world data sets, you’ll participate in a data science capstone project that will help you demonstrate your skills, collaborative ability, and problem-solving acumen, while building your data science project portfolio.

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Curriculum Details

The online master's in data science program offers a strong foundation in data science, plus a specialization track in Machine Learning or Business Analytics, a range of elective options, and a capstone course. Students are free to choose how many courses to take each semester so they can make the program fit their lifestyle and work schedule. Students typically take 1-2 classes each semester.

Core Required Courses [15 credit hours]

COMP 614 - PROGRAMMING FOR DATA SCIENCE [3 credit hours]

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. Some foundational knowledge or a background in Python programming may help students feel more comfortable completing their coursework, but no prior programming knowledge is required for program admission.

COMP 642 - MACHINE LEARNING [3 credit hours]

Machine learning is the automation of the inductive learning process that humans do so well. Machine learning algorithms are critical to the fields of robotics, medicine and bioinformatics, security and transportation, and many more. 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 credit hours]

This class will cover the theory and practice of Big Data. "Big Data" is a colloquial term that refers to tools and techniques for extracting useful information from very large data sets. Data sets are typically considered "very large" if they are too large to be stored in the memory of a single computer, instead stored and processed in “the cloud” using services like AWS and Microsoft Azure. Topics covered include set theory (specifically, the relational algebra and calculus, which serve as the theoretical basis for modern Big Data systems), the modern cloud computing infrastructure for big data storage, migration and analysis, the use of relational systems for data analytics, and mathematical programming for Big Data analytics. The course will also cover distributed computing and file systems, and distributed analytics frameworks such as MapReduce, as well as the state-of-the-art open source systems that implement MapReduce and its generalizations.

COMP 665 - DATA VISUALIZATION [3 credit hours]

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 credit hours]

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.

Specialization [9 credit hours]

Rice's online MDS students will select one 9-credit hour specialization. At this time, online students can take Machine Learning to deepen their knowledge of statistical machine learning, natural language processing, and deep learning methods and applications. (Future specializations are in the works).

MACHINE LEARNING [9 credit hours]

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:

• Natural Language Processing: 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.

BUSINESS ANALYTICS [9 credit hours]

Build business acumen and transform data into actionable business insights. In partnership with the Rice Business school, this specialization comprises six 1.5-credit courses that will empower students to use cutting-edge data analytics and data science techniques across core business functions:

• Foundations of Finance: an examination of the basics of corporate finance and data analytics tools needed to answer businesses’ questions about financing, investments, and other associated topics.
• Quantitative Finance: an application of machine learning and other data analytics tools to improve investment, financing and risk-management decisions.
• Foundations of Operations Management: an introduction to design and integration of fundamental operations management methods for individual organizations and supply chains.
• Quantitative Operations: an introduction to modern data analysis in optimizing business processes, production efforts, inventory management, and supply chain management.
• Foundations of Marketing: an exploration of the key concepts of marketing and how marketing works with other aspects of a business’s structure and growth trajectory. This includes market research, new product launches, customer targeting, and pricing strategies.
• Data-Driven Marketing: students will learn to transform data and customer insights to facilitate decision making, customer acquisition and retention, optimize pricing, and more.

Electives [3 credit hours]

The electives below are representative and may be subject-to-change based on Faculty availability.

COMP 628 - CYBERSECURITY [3 credit hours]

This introductory cybersecurity course includes topics relevant to core components of cybersecurity technologies, processes, and practices designed to protect networks, computers, and data from attack, damage, and unauthorized access. Specifically how to identify, protect, detect, respond, and recover. Topics include threat landscape, cryptography, malware, network security, and cloud security.

COMP 630 - DATABASES [3 credit hours]

This course includes five learning objectives:

• Big picture: Understand the trade-offs of relational and non-relational databases
• Queries: Manage data and understand the costs of doing so
• Design: Build complex databases and understand design trade-offs
• Real-world data: Curate and merge data from real-world sources
• Communication: Explain concepts and implementation and design decisions

COMP 682 - PRINCIPLES OF ALGORITHMS AND SOFTWARE AREA [3 credit hours]

This course covers the fundamental algorithms and data structures that all masters of computer science students should know. Students will master classic algorithm design methods and understand fundamental algorithms to serve as a starting point for solving more complex problems.

RCEL 504 - ETHICAL-TECHNICAL LEADERSHIP [3 credit hours]

Technology-based companies are powered by teams of engineers who create products and services that create value and competitive advantages for organizations that can turn into profits. However, the matrices of technical- and user-related decision paths that engineering leaders make to guide the team are not always constrained by ethics in a formal way. This course will help students understand the impact of ethics on engineering and technology in order to apply ethics concepts to decision making on issues that emerge in the workplace during one’s career.

Capstone Project [4 credit hours]

DSCI 535 - APPLIED MACHINE LEARNING AND DATA SCIENCE PROJECTS [4 credit hours]

In this project-based course, you'll have the 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 using real-world data from a variety of organizations, 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.

Professional Development Requirement [0-3 credit hours]*

PROFESSIONAL DEVELOPMENT REQUIREMENT [0-3 credit hours]

As the Master of Data Science program is a professional master's degree, an important part of the program is a student's professional development. Rice expects most students to satisfy this requirement with on-the-job experience. Students may satisfy the requirement with relevant work experience spanning 10 weeks or more at the same company, between the time they receive their Bachelor's degree and the time they graduate with the Rice MDS degree.

If the work experience requirement is satisfied, the Rice MDS Online program requires 31 credit hours in order to graduate.

If, for whatever reason, a student chooses not to use work experience to satisfy this requirement, s/he may take one of a selected set of courses in Engineering Leadership instead for 3 credit hours, satisfying the 34 credit hour degree requirement to graduate.

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