Computational and Data Science Computational and Data Science

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Programs of Study

Developing mathematical models, employing numerical methods and using data visualization has become increasingly important for a variety of businesses and industries. The Computational and Data Science Track incorporates coursework to reflect the breadth of computational and data science employed to solve real-world problems.

There are two focus areas within the Track:

  • Computational Science
  • Data Science

Graduate Students in the Computational and Data Science Track take advanced science, math and engineering courses within these two focus areas and choose electives based on their professional goals.

Students complete a minimum of 36 credits of graduate-level course work.

15 credits of STEM graduate-level course work in one of the two focus areas are required

Computational Science

Data Science
Core Courses (3 required):

MATH 6805  (or 5010) Introduction to Probability
MATH 6824/6828 Statistical Inference I, II
or
MATH 5080/5090 Statistical Inference I, II

MATH 6860/6865 Introduction to Numerical Analysis I, II
or
MATH 5610/5620 Introduction to Numerical Analysis I,II

MATH 6610/6620 Analysis of Numerical Methods I, II
MATH 5710 Introduction to Applied Mathematics

Electives  (2 required):

CS 6269 Programming for Engineers
MATH 5040 Stochastic Processes and Simulation I
MATH 5050 Stochastic Processes and Simulation II
MATH 5075 Time Series Analysis
MATH 6010 Linear Models
MATH 6070 Mathematical Statistics
MATH 5410 Introduction to Ordinary Differential Equations
MATH 5440 Introduction to Partial Differential Equations
MATH 5470 Chaos and Nonlinear Systems
MATH 5500 Calculus of Variations with Applications
MATH 5750 Optimization
MATH 5110 (or 6770) Mathematical Biology I
MATH 5120 (or 6780) Mathematical Biology II
MATH 5760 Introduction to Mathematical Finance I
MATH 5765 Introduction to Mathematical Finance II

Graduate-level courses from the Department of Mathematics or from the School of Computing

Example programs of study can be found here
Core Courses (4 CS courses required)

MATH 6805 (or 5010) Introduction to Probability
MATH 6828 Statistical Inference I
CS 6150 Advanced Algorithms
CS 6530 Database Systems
CS 6140 Data Mining
CS 6350 Machine Learning
CS 6630 Visualization for Data Science

Electives
(1 required; can take an additional course from Core courses above):

Algorithmics
CS 6160 Computational Geometry
CS 6170 Computational Topology
CS 6180 Clustering

Analytics
CS 6190 Probabilistic Modeling
CS 6210 Advanced Scientific Computing
CS 6300 Artificial Intelligence
CS 6340 Natural Language Processing
CS 6961 Structured Prediction Systems

Management
CS 6230 High-Performance Computing and Parallelization
CS 6235 Parallel Programming for Many-Core Architectures
CS 6480 Advanced Computer Networks
CS 6490 Network Security
CS 6963 Distributed Systems

The Data Science focus area is aligned with the School of Computing Big Data Certificate. Students are expected to apply for this graduate certificate porgram in addition to the PSMT program to ensure they meet course requirements.

Students considering the Data Science focus area should review course prerequisites which typically include undergraduate classes: CS 1410 Introduction to Object-Oriented Programming, CS 2420 Introduction to Algorithms & Data Structures, CS 3500 Software Practice, MATH 2270 Linear Algebra.

Advanced Quantitative Skills (6 credits)

MST 6600 Applied Statistical Techniques

CS 6630 Visualization for Data Science
CS 6269 Programming for Engineers

MATH 5600 Survey of Numerical Analysis
MATH 5740 Mathematical Modeling
MATH5750 Topics in Applied Mathematics
MATH 6820 Time Series Analysis

STAT 6003 Survey of Statistical Computer Packages
STAT 6571 Foundations of Applied Data Analytics and Visualization
STAT6573 Practical Data Science

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Transferable Skills (12 Credits)

MST 6100 —Policy and Regulatory Considerations for Scientist and Engineers
(3 credits)

MST 6110 Business Development for Scientist and Engineers
(3 credits)

MST 6200 Professional Development for Scientist and Engineers
(3 credits)

MST 6210 Operations and Project Management for Scientist and Engineers
(3-credits)

electives: students can request to take 3-credits of graduate coursework from the David Eccles School of Business or an approved elective (contact program director for a list of transferable skills electives)

Professional Experience Project (Internship; 3 Credits)

An essential component of the PMST degree is a Professional Experience Project (internship) working with a local company, government agency or non-profit organization. These activities engage students in real-world work situations involving technical problems, teamwork, communication skills, and decision-making.

Questions regarding the admission process?

Notes: Course availability is subject to change. Substitute classes may be taken upon approval. Courses may have pre-requisites which are published in the University Course Catalog; students are responsible for confirming they meet course requirements and pre-requisites.