Master of Computational Science and Engineering (MCSE) Degree

Program Learning Outcomes for the MCSE Degree

Upon completing the MCSE degree, students will be able to:

  1. Acquire broad, advanced knowledge in modern computational techniques.
  2. Possess skills to identify, formulate, and solve advance technical problems related to one of the three focus areas.
  3. Communicate technical ideas effectively.

Requirements for the MCSE Degree

The MCSE degree is a non-thesis master's degree. For general university requirements, please see Non-Thesis Master's Degrees. Students pursuing the MCSE degree must complete:

  • A minimum of 30 credit hours to satisfy degree requirements.

The Master in Computational Science and Engineering (MCSE) degree in the School of Engineering is a non-thesis degree program designed to provide training and expertise in computational science and engineering and in data analytics. The MCSE degree program is intended for students interested in technical and managerial positions such as computational scientist, computational engineering, and data analyst. The program offers students opportunities to specialize in areas such as high-performance computing, data analytics, data science, machine learning, software engineering, and distributed systems.

The departments of Computational and Applied Mathematics, Computer Science, Electrical and Computer Engineering, and Statistics jointly offer the MCSE degree program. Based on preferences indicated in their applications, MCSE students are admitted to one of the following home departments: Computational and Applied Mathematics (CAAM), Computer Science (COMP), Electrical and Computer Engineering (ELEC), or Statistics (STAT).

Summary

Total Credit Hours Required for the MCSE Degree30

Degree Requirements

Core Distribution Requirements
Select 1 course from 3 of the following 4 groups:
Group 1 (CAAM) 1
Select 1 from the following:3
COMPUTATIONAL SCIENCE I
COMPUTATIONAL SCIENCE II
NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS
NUMERICAL ANALYSIS I
ADVANCED NUMERICAL ANALYSIS I
NUMERICAL OPTIMIZATION
LINEAR AND INTEGER PROGRAMMING
Group 2 (COMP) 2
Select 1 from the following:3-4
GRADUATE OBJECT-ORIENTED PROGRAMMING AND DESIGN
COMPILER CONSTRUCTION FOR GRADUATE STUDENTS
DISTRIBUTED SYSTEMS
OPERATING SYSTEMS AND CONCURRENT PROGRAMMING
MULTI-CORE COMPUTING
ADVANCED COMPUTER NETWORKS
DATABASE SYSTEM IMPLEMENTATION
INTRODUCTION TO DATABASE SYSTEMS
STATISTICAL MACHINE LEARNING
INTRODUCTION TO COMPUTER SECURITY
LARGE-SCALE MACHINE LEARNING
ARTIFICIAL INTELLIGENCE
GRADUATE DESIGN AND ANALYSIS OF ALGORITHMS
Group 3 (ELEC) 3
Select 1 from the following:3-4
COMPLEXITY IN MODERN SYSTEMS
VIRTUALIZATION AND CLOUD RESOURCE MANAGEMENT
HIGH PERFORMANCE COMPUTER ARCHITECTURE
STATISTICAL SIGNAL PROCESSING
INTRODUCTION TO RANDOM PROCESSES AND APPLICATIONS
INTRODUCTION TO COMPUTER VISION
COMPUTER VISION
COMPUTATIONAL PHOTOGRAPHY
MOBILE AND EMBEDDED SYSTEM DESIGN AND APPLICATION
COMPUTER SYSTEMS ARCHITECTURE
DIGITAL SIGNAL PROCESSING
LEARNING FROM SENSOR DATA
A PRACTICAL INTRODUCTION TO DEEP MACHINE LEARNING
FUNDAMENTALS OF MEDICAL IMAGING I
Group 4 (STAT) 4
Select 1 from the following:3-4
NEURAL MACHINE LEARNING I
PROBABILITY
STATISTICAL INFERENCE
MULTIVARIATE ANALYSIS
NEURAL MACHINE LEARNING AND DATA MINING II
R FOR DATA SCIENCE
REGRESSION AND LINEAR MODELS
ADVANCED STATISTICAL METHODS
GRAPHICAL MODELS AND NETWORKS
Elective Requirements
Communication, Leadership, Management and Ethics 5
Select up to 6 credit hours from the following: 62-6
ENGINEERING PROJECT MANAGEMENT AND ECONOMICS
TECHNICAL AND MANAGERIAL COMMUNICATIONS
LEADING TEAMS AND INNOVATION
ENGINEERING ECONOMICS
ETHICS AND ENGINEERING LEADERSHIP
COMMUNICATION FOR ENGINEERS: BUILDING A PRACTICAL TOOLBOX
STRATEGIC THINKING FOR COMPLEX PROBLEM SOLVING
MANAGEMENT FOR SCIENCE AND ENGINEERING
LEARNING HOW TO INNOVATE?
LEADERSHIP COACHING FOR ENGINEERS
Additional Electives
Select additional courses from departmental CAAM, COMP, or STAT course offerings at the 500-level or above. 13-20
Total Credit Hours30
Footnotes and Additional Information

Policies for the MCSE Degree

Application Information 

Students must have completed a BA or BS degree in an engineering or science discipline, with training in engineering mathematics, statistical foundations, and programming methodology to be admitted to the program.

Transfer Credit 

For Rice University’s policy regarding transfer credit, see Transfer Credit. Some departments and programs have additional restrictions on transfer credit. Students are encouraged to meet with their academic program’s advisor when considering transfer credit possibilities.

For additional information, please see the Computational Science and Engineering website: 
https://engrprofmasters.rice.edu/

Opportunities for the MCSE Degree

For additional information, please see the Computational Science and Engineering website: 
https://engrprofmasters.rice.edu/