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. Students in the Master of Science in Data Analytics (MSDA) gain knowledge and skills in advanced analytics and data operations to translate data into a usable asset. You will delve deep into Python programming, data mining, machine learning and data visualization. The MSDA will prepare you to lead organizations to better business decisions and outcomes with confidence and skills to tell the story behind vast amounts of data.

Master of Science in Data Analytics Requirements

The Master of Science in Data Analytics, MSDA, requires ten (10) courses.  Eight (8) are required core courses and two (2) electives.

Incoming students with a strong math or programming background (i.e., candidates holding a B.S. in Computer Science) may substitute up to two of the required courses.

Required core courses:

• MSDA 3045 - Mathematical Statistics  
• MSDA 3055 - Linear Regression and Time Series  
• MSDA 3090 - Python for Data Analytics  
• 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  

Internship

Completion of an approved non-credit internship is required. Exceptions are available for those who qualify.  Please refer to our academic policies and visit a member of our Experiential Learning team for more information.

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 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. Our degree and concentrations 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 - MSDA 3090 - Python for Data Analytics  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.

Electives

• MSC 3900 - Research and Marketing Analytics
• MSDA 3051 - Applied Natural Language Processing (NLP)
• MSDA 3070 - IoT: Securing Communication Technologies through Data Analysis
• MSDA 3100 - Applied Deep Learning
• MSIT 3141 - Health Data and Record Systems
• MSDA 3440 - Special Topics: Data Analytics
• MSIT 3450 - Health Informatics
• MSDA 3950 - Independent Study  (Must be approved by Program Director)
• MSDA 3999 - Capstone Practicum