M.S. in Data Science

Alyssa Peterson portrait small - 180 x 250The M.S. degree in Data Science prepares students to pursue careers in the emerging and high-growth fields of data science and big data. It combines in-depth understanding with hands-on skills, technologies, techniques, and analysis tools for data science.

Graduates of this program will have the theoretical, practical, and comprehensive knowledge to manage and analyze large-scale, complex data to enable efficient data-driven discoveries and decisions.

To complete the requirements for the Master of Science in Data Science, students must successfully complete 12 courses (36 graduate semester credits) and maintain a GPA of 2.7.

Current and inactive students who enrolled in this program prior to fall 2018 may opt to remain with the graduate program requirements from their current catalog, or move forward to the newest graduate program requirements for the M.S. Degree in Data Science.

M.S. Degree in Data Science Program Requirements for catalogs prior to fall 2018:

 

 

Complete a total of 12 three-credit courses (36 graduate credits).

Required Courses [11 courses]:

  • SEIS 603 Foundations of Software Development - Python (waived for appropriate prior programming experience) 
  • SEIS 610 Software Engineering
  • SEIS 615 DevOps and Cloud Infrastructure
  • SEIS 630  Database Management Systems and Design
  • SEIS 631 Foundations of Data Analysis
  • SEIS 632 Data Analytics and Visualization
  • SEIS 732 Data Warehousing and Business Intelligence
  • SEIS 736 Big Data Engineering
  • SEIS 737 Big Data Management
  • SEIS 763 Machine Learning
  • SEIS 764 Artificial Intelligence

Electives [1 or 2 courses]:

Choose one elective (or two electives if SEIS 603 is waived) from any course listed in the Graduate Programs in Software course catalog

Transfer courses: Students may request a transfer of up to two graduate courses (six semester credits) from their previously attended, regionally accredited institution(s) towards their GPS Master of Science degree. The transfer courses must have been taken at the graduate level. The transfer school must be regionally accredited. For more information on transfer courses, please see 'Transfer Courses' listed under Academic Policies and Procedures.

View SEIS Course Catalog.

Suggested course sequence* with SEIS603 required: 

Semester 1: SEIS 603 and SEIS 610 
Semester 2: SEIS 630 and SEIS 631
Semester 3: SEIS 632 and SEIS 763
Semester 4: SEIS 764 and SEIS 615 
Semester 5: SEIS 732 and SEIS 736
Semester 6: SEIS 737 and 1 Elective 

 Suggested course sequence* with SEIS603 waived:

Semester 1: SEIS 610 and SEIS 631
Semester 2: SEIS 630 and SEIS 632
Semester 3: SEIS 615 and SEIS 763 
Semester 4: SEIS 736 and SEIS 764
Semester 5: SEIS 737 and 1 Elective 
Semester 6: SEIS 732 and 1 Elective

* Course sequences assume a fall semester start. Please consult with your advisor if you have questions.

  1. A bachelor's degree in any discipline from a regionally-accredited educational institution in the U.S. (or international equivalent). 
  2. An overall grade-point-average (GPA) of at least 2.7. (Applicants with a GPA less than 2.7 will be considered for provisional admission with their professional experience factored into the decision.)

Take the Next Step:

Data Science Job Outlook

According to Glassdoor’s 2019 rankings, data scientist is the “Best Job in America” for the 4th year in a row, with a median base salary of $108,000.*  It is estimated that job demand will soar by 8% by 2020 with 39% of data scientist positions requiring a master’s degree.**

** “Data Scientist is the Best Job in America According to Glassdoor’s 2019 Rankings”, Forbes, Jan 23, 2019.

*** “IBM Predicts Demand for Data Scientists Will Soar 28% by 2020”, Forbes, May 13, 2017.

Center for Applied Artificial Intelligence

Learn how industry can collaborate with St. Thomas faculty and graduate students to work on real-world problems using data science through the Center for Applied Artificial Intelligence.