Data Science, Analytics and Engineering (Computational Models and Data), MS

Program Overview

This MS program in Data Science, Analytics, and Engineering focuses on computational models and data. It equips students with advanced skills in machine learning, data analysis, and optimization. Graduates are prepared for careers in data science, analytics, and engineering in various industries, including technology, finance, healthcare, and government. The program is STEM-designated and offers flexibility in electives and culminating experience options.

Program Outline

Degree Overview:

This program provides an advanced education in data science, analytics and engineering, with a focus on computational models and data. It aims to equip students with the skills needed to analyze, model, and control complex systems that generate large datasets.

Objectives:

• Provide a deep understanding of the algorithms and mathematical concepts behind machine learning, deep learning and optimization.
• Develop an ability to apply data science tools to solve real-world problems in various domains.
• Equip students with the skills to conduct rigorous mathematical modeling using data.

Outline:

Required Core (9 credit hours):

• Statistics: Covers topics such as probability and random variables, statistical inference, and regression analysis.
• Data Processing: Explores data management techniques, including data cleaning, transformation, and storage.
• Machine Learning: Introduces the core concepts and methods of machine learning, including classification, regression, and dimensionality reduction.

Concentration (9 credit hours):

• Linear Algebra: Covers the fundamental concepts of linear algebra, including vector spaces, matrices, and eigenvalues.
• Regression Analysis: Provides an in-depth understanding of various regression techniques, including linear, logistic, and nonparametric regression.
• Optimization: Explores optimization algorithms for different types of problems, including linear programming, convex optimization, and gradient-based methods.

Electives (6 or 9 credit hours):

Students have the flexibility to choose electives from a range of topics, including artificial intelligence, big data analytics, computational finance, and stochastic processes.

Culminating Experience (3 or 6 credit hours):

• Data Science Capstone: A project-based course where students apply their knowledge and skills to solve a real-world problem.
• Thesis: An original research project that contributes to the field of data science and computational modeling.

Assessment:

Students are evaluated through a combination of assignments, exams, and the culminating experience. The specific assessment methods vary depending on the courses and the instructor.

Teaching:

The program is taught by faculty from the School of Mathematical and Statistical Sciences, who are experts in their fields. The teaching approach is interactive and emphasizes hands-on learning through projects and case studies.

Careers:

Graduates can pursue various careers in data science, analytics, and engineering. These roles include data scientist, data analyst, machine learning engineer, and research scientist. The program prepares graduates for a variety of industries, including technology, finance, healthcare, and government.

Other:

• The program is STEM-designated, making international students eligible for an extended OPT period of up to 36 months.
• The program curriculum is designed to prepare students for industry certifications, such as the Google Data Analytics Certificate.
• Admission Requirements: The program requires a bachelor's degree in a related field, a minimum GPA of 3.0, and relevant coursework or experience in statistics, probability, linear algebra, and programming.