Program Description

How one communicates, socializes, spends leisure time, and conducts business has moved to the Internet. This has resulted in an explosion of data that is changing the world. How one uses this data is now a societal concern. Emboldened by our Jesuit heritage, Regis University’s Master of Science in Data Science degree program empowers students to develop techniques for the discovery of information in data in a socially responsible manner.

Total Degree Requirements 36 SH

Course List

Code	Title	SHs
Degree Requirements
MSDS 600	Introduction to Data Science	3.00
MSDS 610	Data Engineering	3.00
MSDS 640	Ethics/Prvcy/Soc Justice-Data Science	3.00
MSDS 650	Data Analytics	3.00
MSDS 660	Statistical Methods- Experimental Design	3.00
MSCC 697	Information Technology Research Methods	3.00
Degree Electives
Select twelve semester hours of the following:		12.00
MSDE 620	Data Collection and Preparation
MSDS 655	Business Intelligence
MSDS 662	Exploratory Data Analysis
MSDS 664	Predictive Analytics
MSDS 670	Data Visualization
MSDS 674	Geographic Information Systems
MSDS 680	Machine Learning
MSDS 682	Text Analytics
MSDS 684	Reinforcement Learning
MSDS 686	Deep Learning
MSDS 688	Artificial Intelligence
Degree Completion Requirement
MSDS 692	Data Science Practicum	3.00
MSDS 696	Data Science Practicum II	3.00
Optional Data Engineering Specialization 1
Data Engineering Specialization		0-18
MSDE 620	Data Collection and Preparation
MSDE 621	Data Wrangling
MSDE 630	Big Data Architecture
MSDE 631	SQL and NoSQL
MSDE 692	Data Engineering Practicum I
MSDE 696	Data Engineering Practicum II
Total SHs		36-54

¹

An optional specialization may be completed in Data Engineering.  The optional Data Science specialization will fulfill the required elective and degree completion credits for the Master of Science-Data Science degree.

The MS in Data Science program outcomes are as follows

• Describe data science as a discipline of study and explain its methodologies.
• Assemble various data stores in preparation for data analysis.  
• Evaluate the utility of data stores for applied data analysis problems.
• Use statistical methods and experimental design to construct data experiments.
• Formulate techniques for the generalization of knowledge from data. 
• Create data products (visualizations and infographics) that explain data and its analysis.
• Judge data science projects for their ethical concerns.
• Appraise data science projects that contribute to the social good of society.