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 from different mathematical disciplines to reflect the breadth of computational and data science employed to solve real-world problems.

The degree program is offered through the College of Science with students earning the Master of Science and Technology (MST) in Computational Science. There are three focus areas within the Track:

- Data Science
- Applied Mathematics
- Scientific Computing

Graduate Students in the Computational and Data Science Track take advanced science courses within these three focus areas and choose electives based on their professional goals. Core courses and electives total 18 credits. These areas of study include:

- Data Science
- Statistics
- Applied Math
- Computational Math, Biology
- Financial Math
- Scientific Computing
- Graphics and Visualization
- Artificial Intelligence and Robotics

## Data Science

Core courses:

MATH 5080 Statistical Inference I

MATH 5090 Statistical Inference II

Students considering the Data Science focus area should review course pre-requisites. Typical undergraduate coursework for CS classes: CS 1410 Introduction to Object-Oriented Programming, CS 2420 Introduction to Algorithms & Data Structures, CS 3130 Engineering Probability and Statistics, CS 3500 Software Practice, MATH 2270 Linear Algebra, MATH 3170 R Lab I

## Applied Math

### Statistics

Core courses:

MATH 5010 Introduction to Probability

MATH 5080 Statistical Inference I

MATH 5090 Statistical Inference II

Electives:

MATH 5030 Actuarial Mathematics

MATH 5040 Stochastic Processes and Simulation I

MATH 5050 Stochastic Processes and Simulation II

MATH 5610 Introduction to Numerical Analysis I

MATH 5620 Introduction to Numerical Analysis II

MATH 5075 Time Series Analysis

MATH 6010 Linear Models

MATH 6020 Multilinear Models

MATH 6070 Mathematical Statistics

### Applied Math

Core courses:

MATH 5610 Introduction to Numerical Analysis I

MATH 5620 Introduction to Numerical Analysis II

or

MATH 5710 Introduction to Applied Mathematics I

MATH 5600 Survey of Numerical Analysis

or

MATH 6610 Analysis of Numerical Methods I

MATH 6620 Analysis of Numerical Methods II

Electives:

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 6790 Case Studies in CES

### Computational Math, Biology

Core courses:

MATH 5010 Introduction to Probability

MATH 5110 Mathematical Biology I

MATH 5120 Mathematical Biology II

Electives:

MATH 5470 Chaos and Nonlinear Systems

MATH 5040 Stochastic Processes and Simulation I

MATH 5050 Stochastic Processes and Simulation II

MATH 5080 Statistical Inference I

MATH 5090 Statistical Inference II

MATH 6770 Mathematical Biology I

MATH 6780 Mathematical Biology II

### Financial Math

Core courses:

MATH 5010 Introduction to Probability

MATH 5760 Introduction to Mathematical Finance I

MATH 5765 Introduction to Mathematical Finance II

Electives:

MATH 5030 Actuarial Mathematics

MATH 5040 Stochastic Processes and Simulation I

MATH 5050 Stochastic Processes and Simulation II

MATH 5075 Time Series Analysis

MATH 5080 Statistical Inference I

MATH 5090 Statistical Inference II

MATH 6010 Linear Models

ECON 5969 Special Topics in Economics

## Scientific Computing

Core Courses:

CS 6210 Scientific and Data Computing I

CS 6220 Scientific and Data Computing II

Electives:

CS 6610 Interactive Computer Graphics

CS 6630 Visualization for Data Science

CS 6640 Introduction to Digital Image Processing

CS 6660 Physics-based Animation

CS 6665 Character Animation

### Graphics and Visualization

Core Courses:

CS 5600 Introduction to Computer Graphics

CS 6600 Mathematical Foundations of Computer Graphics and Visualization

Electives:

CS 6610 Interactive Computer Graphics

CS 6630 Visualization for Data Science

CS 6640 Introduction to Digital Image Processing

CS 6660 Physics-based Animation

CS 6665 Character Animation

### Artificial Intelligence and Robotics

Core Courses:

CS 6300 Artificial Intelligence

CS 6310 Robotics

Electives:

CS 6320 3D Computer Vision

CS 6340 Natural Language Processing

CS 6350 Machine Learning

CS 6360 Virtual Reality

### Advanced Quantitative Skills (6 credits)

MST 6600 Applied Statistical Techniques

AND one of the following five courses:

MATH 5010 Introduction to Probability

MATH 5600 Survey of Numerical Analysis

MATH 5710 Introduction to Applied Mathematics I

MATH 5740 Mathematical Modeling

CS 5010 Software Practice

### Transferable Skills (12 Credits)

MST 6010 Effective Communication (1)

MST 6012 Accounting and Finance (1)

MST 6020 Leadership and Management (1)

MST 6021 Strategic Planning and Marketing (1)

MST 6022 Production & Operations Management (1)

MST 6023 Entrepreneurship and New Product Development (1)

MST 6500 Scientific Reasoning (3)

electives: 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.

(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.)