M.S. in Data Science
M.S. in Data Science (30 Credits)
In today’s AI-driven economy, there is a strong demand for data scientists equipped with computational skills to develop, design and apply models and tools for data-driven decision making. Companies use data science and AI for marketing decisions, targeted customer recommendations, determination of profitable insurance coverage as well as for providing personalized financial advice.
The M.S. in Data Science covers basic and advanced methods in statistical inference, machine learning, data visualization, data mining, and big data, all of which are essential skills for a high-performing data scientist. To be admitted to the program, we require a basic background in Mathematics (calculus, linear algebra), Statistics (probability and basic stats) and Software Development (programming, data structures and algorithms). This part-time degree program involves 10 courses of 3 credits each, taught over 5 semesters of 15 weeks each (including summer). Courses consist of formal lectures as well as hands-on programming projects.
The program curriculum uses the Python programming language with its data science libraries and features tools like R for statistical analysis and Tableau for data visualization. Students work on homework assignments and projects covering both theory and applications on real data with guidance from the professor and teaching assistants.
Recommended part-time credit schedule: Two courses (6 credits) per semester over five consecutive semesters, including Summer. Start is possible in Fall, Spring or Summer semesters.
|“Dr. Bader shares his nationally renowned work with us in a small class environment. These are experiences you will not find in any textbook.” – Ganesh Raut, M.S. in Data Science (Computational Track)|
Students are required to take (4) core courses from the following list:
|DS 675||Machine Learning|
|DS 644||Introduction to Big Data|
|DS 636||Data Analytics with R Program|
|DS 677||Deep Learning|
|MATH 661||Applied Statistics|
Students may choose an elective outside the list after approval from their respective advisor.
|Advance Database System Design|
|CS 643||Cloud Computing|
|CS 659||Image Processing and Analysis|
|CS 670||Artificial Intelligence|
|CS 708||Advanced Data Security and Privacy|
|CS 732||Advanced Machine Learning|
|CS 735||High Performance Analytics for Data Science|
- Be able to acquire, clean, and manage massive data sets.
- Play an analytical role in your company where you design, implement, and evaluate advanced statistical models and approaches for application to your company’s most complex problems.
- Be able to provide econometric and statistical models for a variety of problems including projections, classification, clustering, pattern analysis, sampling and simulations.
- Research new ways for predicting and modeling end-user behavior as well as investigating data summarization and visualization techniques for conveying key applied analytics findings.
- Apply modern artificial intelligence and deep learning methods to complex prediction and recognition tasks.
Students in the Master of Science in Data Science (MSDS) program must successfully complete 30 credits based on any of the following options:
10 Courses (30 credits)
9 Courses (27 credits) + MS Project (3 credits)
8 Courses (24 credits) + MS Thesis (6 credits)
Independent of the chosen option, 4 out of 5 core courses are required.
If a student chooses the MS thesis option, the thesis must be related to Data Science and requires approval from a professor in the Data Science Department who will be the MS thesis advisor.
Tuition, University Fee, and GSA Fee for ALL non-F1 students per consecutive semester, regardless of residency and visa status, assuming two courses (6 credits) at 2022-2023 rates:
Summer 2022: $5,942
Fall 2022: $6,900
Spring 2023: $6,900
Total Tuition, University Fee, and GSA Fee for degree, assuming two courses (6 credits) per consecutive semester at 2022-2023 rates:
Summer Start: $32,584
Fall Start: $33,543
Spring Start: $32,584