2022-2023 Academic Catalog 
    
    Nov 26, 2024  
2022-2023 Academic Catalog [ARCHIVED CATALOG]

Data Analytics, MSDA


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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, advanced statistical analysis, and data mining and warehousing. The MSDA will prepare you to lead your organization 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) courses are required and two (2) courses 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:

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 - 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.

 

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