Master of Science in Data Science
Course Descriptions
MSDS 5103 – Probability and Inference for Data Science – 3 credit hours
In this course, students will be introduced to inferential tools for applications in data science. Topics covered include hypothesis testing, confidence intervals, probability distributions, central limit theorem; and interval estimation.
MSDS 5113 – Introduction to Statistical Modeling – 3 credit hours
This course is an introduction to foundational concepts, theories, and techniques of statistical analysis for data science. Students will begin with descriptive statistics and probability and advance through multiple and logistic regression. Students will also conduct analyses in R. Additional topics covered include descriptive statistics, central tendency, exploratory data analysis, probability theory, discrete and continuous distributions, statistical inference, correlation, and multiple linear regression.
MSDS 5123 – Fundamentals of Data Science – 3 credit hours
This course provides an introduction to foundational concepts, technologies, and theories of data and data science. Students will gain a foundational understanding of the concepts and techniques used in data science and machine learning.
MSDS 6203 – Data Systems & Algorithms – 3 credit hours
In this course, students will work to develop their programming skills and learn the fundamentals of data structures and the practice use of algorithms. Students will review a variety of useful algorithms and analyze their complexity and gain insight into the principles and data structures used in algorithm design.
MSDS 6213 – Programming for Data Science – 3 credit hours
This course introduces students to programming language (Python, R, etc.) and its application in data science. Students will be introduced to platforms such as Jupytr Notebooks to learn the practical aspects of data manipulation, data cleaning, and exploratory data analysis.
MSDS 6403 – Data & Database Management with SQL – 3 credit hours
In this course, students will focus on understanding how data can be organized, cleaned, and managed within and between data sets. Students will be introduced to database design and to the use of databases in data science applications with an emphasis on SQL.
MSDS 6413 – Presentation & Visualization of Data – 3 credit hours
In this course, students are introduced to computational tools for building interactive graphics and dashboards as well as commercial visualization software. Students will use visualizations techniques to identify the patterns, trends, correlations, and outliers of data sets.
MSDS 6503 – Machine Learning & Artificial Intelligence – 3 credit hours
This course introduces students to relevant machine learning methods, communicating results, and the ethical considerations in machine learning. Students will build, train, and test machine learning models such as logistic regression and neural networks. Throughout the course, students experiment with the concepts of the data science process and apply them to real-world datasets.
MSDS 6903 – Applied Capstone Project – 3 credit hours
In the capstone project, student teams will work to demonstrate their ability to apply and communicate data science concepts and processes to create a digital project of their choosing. Students may produce a website, platform, tool, or other digital project.
MSTM 5033 – Information in Infrastructure – 3 credit hours
This course introduces fundamental concepts of data communication and networking, such as network structure, cybersecurity issues, and trends in communications and networking. Practical application of content is made through case study analysis.
MSTM 5900 – Internship – 0.5 credit hour
This course provides students with an opportunity to gain practical work experience that is connected to graduate coursework for the MSTM program. This for-credit internship requires students to document work experiences, including hours worked and tasks completed, in a job placement related to the field of study. In addition to internship responsibilities, students will complete written research to connect practical experiences with graduate coursework. Students generally are expected to work a minimum of 20 hours per week to complete expectations for this course and maintain a minimum GPA of 3.0. This course may be taken for 0.5 credit hour per term with a minimum of 3 credit hours. Students must be continuously enrolled in this course during the program.