Data Analytics, MSDA

Master of Science in Data Analytics Overview

The well-publicized proliferation of data across all sectors of the global economy has fueled demand for professionals with deep skills in accessing and analyzing data and communicating the resulting information to drive data driven decision-making. The Master’s of Science in Data Analytics (MSDA) is a STEM credential enabling our students to access this large, growing, and financially rewarding career.  It is clear that the emergence of data infused computer applications, internet-based financial and consumer transactions, the need for increased out-come analysis and attention to industry and consumer trends that careers in data analytics are here to stay. It is hard to identify a sector of our society that is not being touched by big data.

Master of Science in Data Analytics Requirements

The Master’s of Science in Data Analytics, MSDA, has a curriculum map of 10 (1 unit) courses.  Nine (9) courses are required and 1 course may be taken from numerous options. Incoming students with a strong math or programming background (i.e., candidates holding a B.S. in Computer Science) may waive up to two of the required courses and replace them with two other course options. Required courses follow:

• MSDA 3045 - Mathematical Statistics  
• MSDA 3055 - Linear Regression and Time Series  
• MSIT 3090 - Python Programming  
• MSIT 3860 - Data Management for Information Technology  
• MSDA 3040 - Fundamentals of Data Engineering  
• MSIT 3350 - Data Mining With Splunk  
• MSDA 3050 - Applied Machine Learning  
• MSDA 3060 - Data Visualization and Story Telling  
• MSDA 3999 - Capstone Practicum  

Optional Courses:

MSPC 3900 - Research and Marketing Analytics   

MSDA 3070 - IoT: Securing Communication Technologies through Data Analysis  

MSIT 3141 - Health Data and Record Systems   

MPA 3110 - Applied GIS For Decision Makers   

MSIT 3450 - Health Informatics    

MSDA 3940 - Internship  

MSDA 3950 - Independent Study   To be approved by advisor (Sentiment analysis, AI, text recognition, etc…)

Learning Outcomes

The Master of Science in Data Analytics is designed to equip students with advanced skills and knowledge to find practical trends, insights and actionable knowledge through the analysis and presentation of all types of data. Students learn pragmatic machine learning, data engineering and data visualization skills. These skills form necessary foundations for solving practical problems that arise in business, industrial, governmental, and other organizations, as well as for pursuing doctoral studies in data science. We are confident that our degree and its concentrations will provide graduates with the skills necessary to work as professionals and take on leadership roles in their organizations. This program offers students the opportunity to develop operational competencies in 5 foundations: 

1. Core Technologies Necessary to Meet STEM Industry Standards - MSIT 3090 - Python Programming  AND MSIT 3350 - Data Mining With Splunk  
2. Foundational Elements for STEM Programs - MSDA 3055 - Linear Regression and Time Series  AND MSDA 3045 - Mathematical Statistics  
3. Ethics and Social Responsibility - MSIT 3860 - Data Management for Information Technology  
4. Applied Research: MSDA 3999 - Capstone Practicum  
5. Theoretical Grounding: MSDA 3050 - Applied Machine Learning  

More detailed information about the Core Competencies and Learning Outcomes can be found on the MSDA webpage.