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APPLY BY JULY 1 FOR
FALL '22 COHORT

MDS@RICE

Robust Data Science Curriculum with ML and Business Analytics Tracks

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MDS@RICE

Master of Data Science Course Curriculum & Specializations

The online Master of Data Science curriculum 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.

CONTACT ENROLLMENT COACH

PROGRAM HIGHLIGHTS

Quick Facts About the Online MDS Program

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10 Courses

ACADEMIC
RIGOR

Intellectual challenge met with unquestioning support.

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Holistic Approach

HOLISTIC
APPROACH

Obtain comprehensive knowledge in how to apply core methods of data science to areas of specialization.

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Same Degree As In-Person

SAME DEGREE AS
IN-PERSON

The same curriculum offered on-campus with the added flexibility of an online program.

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Leading Faculty

LEADING
FACULTY

World-class faculty that provide hands-on education and thoughtful interactions with students.

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Real-World Application

REAL-WORLD
APPLICATION

Coursework designed to enable students to solve real-world problems with data science theory and techniques.

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RICE’S 35-YEAR COMPUTER SCIENCE HISTORY

CUSTOMIZABLE
PROGRAM

The flexible format prepares students to launch or advance their careers in data science and technology industries.

COURSEWORK OVERVIEW

Professional Preparation in Data Science

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, including healthcare, engineering, finance and more.

Your curriculum includes five core courses designed to help you gain an understanding of the computational and statistical foundations of data science. You’ll gain deeper knowledge in data science by choosing a specialization in business analytics or machine learning. You’ll further customize your program of study with electives in ethics, cybersecurity, or security and privacy. Then, to give you experience applying your knowledge to a real-world problem, you’ll participate in a data science capstone project that will help you demonstrate your skill, collaborative ability and problem-solving acumen.

CONTACT ENROLLMENT COACH

WHAT YOU'LL GAIN

Program Outcomes & Experience

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#1

DATA SCIENCE SKILL ATTAINMENT

Quickly acquire computational and statistical foundations in data science, specialized knowledge in subjects of your choice and hands-on experience managing raw data to solve real-world problems.

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#2

NEW CONFIDENCE IN BUSINESS COMMUNICATIONS

Gain professional confidence in communicating to lay audiences orally and in writing about data science methods and results.

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#3

PROFESSIONAL PREPARATION

Experience a collaborative and engaging format and a rigorous curriculum taught by faculty recognized for their teaching, research and practice — all designed to prepare you to meet the demands of employers.

MDS@RICE

Curriculum Details

Core Courses

COMP 614 - 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]

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 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.

Electives

Electives are representative and may be subject-to-change based on Faculty availability.

COMP 628 - CYBERSECURITY [3 CREDITS]

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 CREDITS]

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 CREDITS]

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 CREDITS]

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.

COMP 644 - DATA PRIVACY AND SECURITY (SYNCHRONOUS ONLY) [3 CREDITS]

This course will cover types of threats in data and computer systems. This includes foundational computer security material such as encryption, public-private key systems. In addition, foundations of data privacy and security, including models of disclosure, k-anonymity, and differential privacy, will be covered. Please note: Course is currently synchronous only, or 100% scheduled live sessions with the instructor and classmates.

Capstone

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.

Areas of Specialization

Enhance your skill set by selecting one nine-credit specialization. Program participants can choose from business analytics or machine learning.

Business Analytics

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:

  • Marketing Foundations, an introduction to marketing and its function in defining, creating and communicating value.
  • Quantitative Marketing, a focus on using customer data to optimize marketing decisions.
  • Finance Foundations, an introduction to the theory and practice of corporate finance and the analytical tools necessary to answer the most important questions related to financing and investment decisions.
  • Quantitative Finance, an application of machine learning and other data analytic tools to improve investment, financing and risk-management decisions.
  • Operations Management Foundations, an introduction to the design and integration of successful operations tactics both within the organization and across supply chains.
  • Quantitative Operations Management, application of advanced statistics, optimization and machine learning techniques on process optimization, production, inventory and supply chain issues.
Machine Learning

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.
BETTER PREPARE FOR THE PROGRAM AND YOUR DATA SCIENCE/ML CAREER

Online Bridge Course: Refresher for STEM/Technical Backgrounds

Rice University’s online bridge course is designed to provide you with the necessary refresh in math and programming that will help you succeed in the online 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 courses before you apply to the Online MDS program, after you submit your application, or upon acceptance into the program.

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