Data Science, Analytics and Engineering (Materials Science and Engineering), MS

Program Overview

The program prepares students for careers in data science, materials science, and related fields, and qualifies for an Optional Practical Training (OPT) extension for up to 36 months for international students.

Program Outline

It combines a focus on probability and statistics, machine learning, and data engineering with materials science and engineering-specific courses to ensure both breadth and depth in these fields.

Objectives:

• Equip students with the data science skills needed to analyze and interpret large data sets in the context of materials science and engineering.
• Provide students with a strong foundation in statistical analysis, machine learning, and data engineering.
• Help students develop the ability to design and conduct research studies in data science and materials science and engineering.
• Prepare students for careers in data science, materials science, and related fields.

Program Description:

The Data Science, Analytics and Engineering (Materials Science and Engineering) MS program consists of 30 credit hours and a thesis. Alternatively, students can opt for 30 credit hours including the required applied project course (MSE 593).

Required Core Courses:

• STP 502: Theory of Statistics II: Inference (3 credits)
• DSE 501:
• CSE 511: Data Processing at Scale (3 credits)
• IFT 530:
• One course from the following:
• CSE 572:
Data Mining (3 credits)
• CSE 575:
Statistical Machine Learning (3 credits)
• EEE 549:
Statistical Machine Learning: From Theory to Practice (3 credits)
• IEE 520:
Statistical Learning for Data Mining (3 credits)
• IFT 511:
Analyzing Big Data (3 credits)
• IFT 512:
Advanced Big Data Analytics/AI (3 credits)
• MAE 551:
Applied Machine Learning for Mechanical Engineers (3 credits)
• STP 550:
Statistical Machine Learning (3 credits)

Concentration Courses:

Students choose 12 credit hours of concentration courses from a list of approved electives. The list of approved electives can be found by consulting the academic unit.

Electives:

Students can choose 3 or 6 credit hours of electives from a list of approved courses. The list of approved electives can be found by consulting the academic unit.

Culminating Experience:

Students can choose either a 3-credit or 6-credit culminating experience. The options are:

• MSE 593: Applied Project (3 credits)

Careers:

Career Opportunities:

Materials science engineers with a background in data science can pursue opportunities in various fields, including:

• Aircraft design
• Energy systems
• Manufacturing
• Product design
• Semiconductors

Other:

Program Highlights:

• Focus on high-demand data science and materials science and engineering
• Combines statistical analysis, machine learning, data engineering with materials science and engineering
• Prepares students for careers in data science, materials science, and related fields