Data Science

Program Overview

The Data Science program, jointly offered by Computer and Information Science in Engineering and Mathematics in Arts & Sciences, prepares students for leadership positions in data analytics, information management, and knowledge engineering. Upon completing the program, graduates will have skills in computer programming, statistics, data mining, machine learning, data analysis, and visualization that enable them to solve challenging problems involving large, diverse data sets from different application domains.

Program Goals

Meet the growing regional and national demand for high-level information systems/science skills;
Provide a path for individuals from diverse fields to rapidly transition to data science career paths;
Enable established information technology and computing professionals to upgrade their technical management and development skills;
Prepare graduates to apply data science techniques for knowledge discovery and dissemination to assist researchers or decision makers in achieving organizational objectives;
Establish stronger ties to alumni to enhance opportunities for continued learning and leadership;
Create innovators, entrepreneurs, business professionals who will lead the development of next generation information systems.

Learning Outcomes

At the time of graduation, students will:

Have developed core data science skills in computer programming, statistical learning, data mining, machine learning, predictive modeling, data analysis, and visualization.
Be able to apply data science skills to all aspects of the data life cycle: (1) data collection and processing techniques, (2) data management, (3) exploratory data analysis, (4) statistical learning and predictive modeling, and (5) communication of the insights and knowledge learned through the dataset.
Be able to apply data science methods to data-rich fields and think critically about data-driven insights, problem-solving, and decision-making within these application domains.
Be able to effectively communicate data findings to both technical and general audiences using written and oral formats.
Be prepared for technical careers in academics or industry, including science, engineering, business, government, and social organization.

Requirements

Students in the MS in Data Science program must complete a total of 30 credits via a combination of required (core) and elective courses. During the registration and Add/Drop period, students may consult pre-assigned academic advisors to select courses from approved list that fit their domain interests.

Required Courses

Required courses consist of 1 mathematics course and 2 computing courses.

Mathematics required course options: choose 1 out of the following two:

1. MTH522 Mathematical Statistics (3 credits)
or
2. MTH601 Mathematics of Deep Learning (3 credits)

Computing required course options: choose 2 out of the following three:

1. DSC520/EAS520 High-Performance Scientific Computing (3 credits)
       2. CIS 552** Database Design (3 credits)
           or
           DSC 530/CIS 568*** Data Visualization (3 credits)
       3. CIS 530/DSC 531 Advanced Data Mining (3 credits)
           or
           CIS 550 Advanced Machine Learning (3 credits)

**CIS 452 (Database Systems) may substitute for CIS 552 if the student has not taken an undergraduate database course before.
***CIS 468 (Data Visualization) may substitute for CIS 568 if the student has not taken an undergraduate data visualization course before.

Courses above that are not counted towards core requirements may count as electives.

Complete Required Project or Thesis

Students must complete one of the following three options:

Option #1: Complete DSC 550 – Master’s Project 3 credits
Option #2: Complete DSC 550 – Master’s Project 3 credits followed by DSC 690 – Master’s Thesis 3 credits (upon completion of DSC 550)
Option #3: Complete DSC 690 – Master’s Thesis 6 Credit’s.

Elective Courses

Depending on whether students choose project or thesis options, they must select five or six additional approved 400-, 500-and above elective courses. Please see the additional requirements below regarding the 400-level courses that can be counted toward the degree.

Select five additional approved 500- or 600-level courses

Approved courses from another department may be substituted, with prior approval from the Graduate Curriculum Committee or Director(s) of the Data Science Program.

Additional Requirements

As many as two (six credits) approved 400-level undergraduate courses may substitute for graduate required courses or electives, only with prior approval of the Graduate Curriculum Committee.
No more than 9 credits of coursework taken as a non-degree special student may subsequently be credited toward the graduate degree.
Students must meet the university graduate requirement of at least a 3.0 grade point average; For 500- and above level courses, only courses graded C or better may count toward the degree. For 400- level courses, only courses graded B or better may count toward the degree.
Students working on a Master’s Thesis will prepare and defend their thesis to a faculty committee.
Per federal mandate, international students can only take at most one online course (OCE) per semester.