Master of Data Science (MDS) Degree
Program Learning Outcomes for the MDS Degree
Upon completing the MDS degree, students will be able to:
- Develop a graduate-level understanding of the computational and statistical foundations of Data Science.
- Through in-depth study, obtain mastery of either one of the core methods of Data Science or one application area of Data Science.
- Apply Data Science techniques to solve difficult, real world problems, beginning with raw and dirty data, and ending with actionable insights that are effectively communicated to a lay client.
Requirements for the MDS Degree
The MDS degree is a non-thesis master's degree. For general university requirements, please see Non-Thesis Master's Degrees. For additional requirements, regulations, and procedures for all graduate programs, please see All Graduate Students. Students pursuing the MDS degree must complete:
- A minimum of 10-13 courses (31-35 credit hours), depending on course selection, to satisfy degree requirements.
- A minimum of 31 credit hours of graduate-level study (graduate semester credit hours, coursework at the 500-level or above).
- A minimum of 24 graduate semester credit hours credit hours must be taken at Rice University.
- A minimum of 24 graduate semester credit hours must be taken in standard or traditional courses (with a course type of lecture, seminar, laboratory, lecture/laboratory).
- A minimum residency enrollment of one fall or spring semester of part-time graduate study at Rice University.
- A maximum of 2 courses (6 graduate semester credit hours) from transfer credit. For additional departmental guidelines regarding transfer credit, see the Policies tab.
- The requirements for one area of specialization (see below for areas of specialization). The MDS degree program offers five areas of specialization:
- A Professional Development requirement.
- A minimum overall GPA of 2.67 or higher in all Rice coursework.
- A minimum program GPA of 2.67 or higher in all Rice coursework that satisfies requirements for the non-thesis master’s degree.
The courses listed below satisfy the requirements for this degree program. In certain instances, courses not on this official list may be substituted upon approval of the program's academic advisor, or where applicable, the department or program's Director of Graduate Studies. Course substitutions must be formally applied and entered into Degree Works by the department or program's Official Certifier. Additionally, these must be approved by the Office of Graduate and Postdoctoral Studies. Students and their academic advisors should identify and clearly document the courses to be taken.
Summary
| Code | Title | Credit Hours |
|---|---|---|
| Total Credit Hours Required for the MDS Degree | 31-35 | |
Degree Requirements
| Code | Title | Credit Hours |
|---|---|---|
| Core Requirements 1 | ||
| Big Data | ||
| Select 1 course from the following: | 3 | |
| GRADUATE TOOLS AND MODELS - DATA SCIENCE | ||
| BIG DATA MANAGEMENT FOR DATA SCIENCE | ||
| BIG DATA | ||
| Data Visualization | ||
| COMP 665 | DATA VISUALIZATION | 3 |
| Machine Learning | ||
| Select 1 course from the following: | 3 | |
| MACHINE LEARNING | ||
| INTRODUCTION TO MACHINE LEARNING | ||
| Programming | ||
| COMP 614 | COMPUTER PROGRAMMING FOR DATA SCIENCE | 3 |
| Statistics | ||
| COMP 680 | STATISTICS FOR COMPUTING AND DATA SCIENCE | 3 |
| Elective Requirements 1 | ||
| Select 1 course from the following: | 3-4 | |
| COMPUTER ETHICS | ||
| AI ETHICS | ||
| PROBABILISTIC ALGORITHMS AND DATA STRUCTURE | ||
| GRADUATE DESIGN AND ANALYSIS OF ALGORITHMS | ||
| SYSTEMS SOFTWARE | ||
| DATA ETHICS | ||
| CYBERSECURITY | ||
| DATA PRIVACY & SECURITY | ||
| PRINCIPLES OF ALGORITHMS AND SOFTWARE AREA | ||
| Area of Specialization 1 | ||
| Select 1 from the following Areas of Specialization (see Areas of Specialization below): | 9 | |
Business Analytics | ||
Energy Transition and Sustainability | ||
Image Processing | ||
Machine Learning | ||
Sport Analytics | ||
| Professional Development | ||
| Select 1 from the following: | 0-3 | |
A Professional Development course (see course list below) | ||
A relevant internship 10 weeks to 6 months in length. Students are responsible for obtaining and selecting an internship that best aligns with their career goals. | ||
Current or past post-baccalaureate relevant work experience of at least 10 weeks. | ||
| Capstone 1 | ||
| DSCI 535 / COMP 549 | APPLIED MACHINE LEARNING AND DATA SCIENCE PROJECTS | 4 |
| Total Credit Hours | 31-35 | |
Footnotes and Additional Information
| 1 | Students admitted into either program (online or on-campus) will be allowed to take up to 9 credit hours in the other modality (on-campus or online) with permission from the program advisors. |
Areas of Specialization
Students must complete a minimum of 3 courses (minimum of 9 credit hours) from one Area of Specialization.
Area of Specialization: Business Analytics
| Code | Title | Credit Hours |
|---|---|---|
| Select a minimum of 3 courses (minimum of 9 credit hours) from the following: | 9 | |
| DATA-DRIVEN MARKETING I and DATA-DRIVEN MARKETING II 1 | ||
| DATA-DRIVEN FINANCE I and DATA-DRIVEN FINANCE II 2 | ||
| FOUNDATIONS OF OPERATIONS MANAGEMENT and QUANTITATIVE OPERATIONS 3 | ||
| PRESCRIPTIVE ANALYTICS | ||
| COMPUTATIONAL PRESCRIPTIVE ANALYTICS | ||
| OPTIMIZATION METHODS IN FINANCE | ||
| QUANTITATIVE FINANCIAL RISK MANAGEMENT | ||
| QUANTITATIVE FINANCIAL ANALYTICS | ||
| Total Credit Hours | 9 | |
Footnotes and Additional Information
| 1 | The course BUSI 711 can only be counted towards the Area of Specialization: Business Analytics if the course BUSI 712 is also counted towards the Area of Specialization: Business Analytics. |
| 2 | The course BUSI 721 can only be counted towards the Area of Specialization: Business Analytics if the course BUSI 722 is also counted towards the Area of Specialization: Business Analytics. |
| 3 | The course BUSI 731 can only be counted towards the Area of Specialization: Business Analytics if the course BUSI 732 is also counted towards the Area of Specialization: Business Analytics. |
Area of Specialization: Energy Transition and Sustainability
| Code | Title | Credit Hours |
|---|---|---|
| Select a minimum of 3 courses (minimum of 9 credit hours) from the following: | 9 | |
| ADVANCED COMPUTATIONAL METHODS FOR ENERGY | ||
| DATA SCIENCE METHODS AND ALGORITHMS | ||
| COMPUTATIONAL AND DATA SCIENCE IN THE ENERGY INDUSTRY | ||
| GEOPHYSICAL DATA ANALYSIS: INVERSE METHODS | ||
| Total Credit Hours | 9 | |
Area of Specialization: Image Processing
| Code | Title | Credit Hours |
|---|---|---|
| Select a minimum of 3 courses (minimum of 9 credit hours) from the following: | 9 | |
| DEEP LEARNING FOR VISION AND LANGUAGE | ||
| GENERATIVE AI FOR IMAGE DATA | ||
| INTRODUCTION TO COMPUTER VISION | ||
| COMPUTATIONAL PHOTOGRAPHY | ||
| Total Credit Hours | 9 | |
Area of Specialization: Machine Learning
| Code | Title | Credit Hours |
|---|---|---|
| Select a minimum of 3 courses (minimum of 9 credit hours) from the following: | 9 | |
| OPTIMIZATION: ALGORITHMS, COMPLEXITY, AND APPROXIMATIONS | ||
| REINFORCEMENT LEARNING | ||
| MACHINE LEARNING WITH GRAPHS | ||
| INTRODUCTION TO INFORMATION RETRIEVAL | ||
| GRADUATE SEMINAR ON INTERACTIVE MACHINE LEARNING | ||
| DEEP LEARNING FOR VISION AND LANGUAGE | ||
| DEEP LEARNING | ||
| NATURAL LANGUAGE PROCESSING | ||
| STATISTICAL MACHINE LEARNING | ||
| MACHINE LEARNING FOR RESOURCE-CONSTRAINED PLATFORMS | ||
| GENERATIVE AI FOR IMAGE DATA | ||
| DISTRIBUTED METHODS FOR OPTIMIZATION AND MACHINE LEARNING | ||
| NETWORK SCIENCE AND ANALYTICS | ||
| LEARNING FROM SENSOR DATA | ||
| A PRACTICAL INTRODUCTION TO DEEP MACHINE LEARNING | ||
| ADVANCED MACHINE LEARNING | ||
| Total Credit Hours | 9 | |
Area of Specialization: Sport Analytics
| Code | Title | Credit Hours |
|---|---|---|
| Select a minimum of 3 courses (minimum of 9 credit hours) from the following: | 9 | |
| INTRODUCTION TO SPORT ANALYTICS | ||
| ADVANCED SPORT ANALYTICS | ||
| SOCCER ANALYTICS | ||
| BASEBALL ANALYTICS | ||
| SEMINAR IN SPORTS ANALYTICS | ||
| Total Credit Hours | 9 | |
Professional Development
In order to fulfill the Professional Development requirement, students must select up to 1 course (up to 3 credit hours) from the following, or
- Complete a relevant internship10-weeks to 6 months in length. Students are responsible for obtaining and selecting an internship that best aligns with their career goals, or
- Complete current or past post-baccalaureate relevant work experience of at least 10 weeks.
| Code | Title | Credit Hours |
|---|---|---|
| Select up to 1 course from the following: | 0-3 | |
| ENGINEERING MANAGEMENT & LEADERSHIP THEORY AND APPLICATION | ||
| ENGINEERING PROJECT MANAGEMENT | ||
| ENGINEERING PRODUCT MANAGEMENT IN INDUSTRY 4.0 | ||
| ETHICAL-TECHNICAL LEADERSHIP | ||
| ENGINEERING ECONOMICS FOR ENGINEERING LEADERS | ||
Policies for the MDS Degree
Department of Computer Science Graduate Program Handbook
The General Announcements (GA) is the official Rice curriculum. As an additional resource for students, the department of Computer Science publishes a graduate program handbook, which can be found here: https://gradhandbooks.rice.edu/2024_25/Computer_Science_Graduate_Handbook.pdf.
Financial Aid
No financial aid is available from Rice University or the Computer Science Department for students in the MDS degree program.
Transfer Credit
For Rice University’s policy regarding transfer credit, see Transfer Credit. Some departments and programs have additional restrictions on transfer credit. Requests for transfer credit must be approved for Rice equivalency by the appropriate academic department offering the Rice equivalent course (corresponding to the subject code of the course content) and by the Office of Graduate and Postdoctoral Studies (GPS). Students are encouraged to meet with their academic program’s advisor when considering transfer credit possibilities.
Departmental Transfer Credit Guidelines
Students pursuing the MDS degree should be aware of the following departmental transfer credit guidelines:
- No more than 2 courses (6 credit hours) of credit from another U.S. or international universities of similar standing as Rice may apply towards the degree.
- Transfer courses must be comparable in content and depth to the corresponding course at Rice and must not have counted toward another degree.
Additional Information
For additional information, please see the Graduate Programs tab of the Computer Science website or contact the department at gradapp@rice.edu.
Opportunities for the MDS Degree
Fifth-Year Master's Degree Option for Rice Undergraduate Students
In certain situations and with some terminal master's degree programs, Rice students have an option to pursue a master’s degree by adding an additional fifth year to their four years of undergraduate studies.
Advanced Rice undergraduate students in good academic standing typically apply to the master’s degree program during their junior or senior year. Upon acceptance, depending on course load, financial aid status, and other variables, they may then start taking some required courses of the master's degree program. A plan of study will need to be approved by the student's undergraduate major advisor and the master’s degree program director.
As part of this option and opportunity, Rice undergraduate students:
- must complete the requirements for a bachelor's degree and the master's degree independently of each other (i.e. no course may be counted toward the fulfillment of both degrees).
- should be aware there could be financial aid implications if the conversion of undergraduate coursework to that of graduate level reduces their earned undergraduate credit for any semester below that of full-time status (12 credit hours).
- more information on this Undergraduate - Graduate Concurrent Enrollment opportunity, including specific information on the registration process can be found here.
Rice undergraduate students completing studies in science and engineering may have the option to pursue the Master of Data Science (MDS) degree. For additional information, students should contact their undergraduate major advisor and the MDS program director.
Additional Information
For additional information, please see the Graduate Programs tab of the Computer Science website or contact the department at gradapp@rice.edu.