Data Analytics, MSDA

Master of Science in Data Analytics Overview

The  exponential  growth  of  data  across  industries  has  created  an  urgent  need  for  skilled professionals who can transform raw data into actionable insights. The Master of Science in Data Analytics  (MSDA)  program  equips  students  with  cutting-edge  knowledge  and  hands-on experience to meet this demand.

Through  a  carefully  designed  curriculum,  students  gain  expertise  in  Python  programming, Machine Learning, Data Engineering, and advanced analytics. The program emphasizes practical applications, enabling graduates to extract meaningful patterns, forecast trends, and communicate findings effectively to facilitate data-driven decision-making. The MSDA prepares students to confidently  lead  organizations,  leveraging  data  to  achieve  strategic  objectives  and  deliver impactful results.

Learning Outcomes

Upon completing the MSDA program, graduates will be able to:

1. Analyze complex datasets using advanced statistical, exploratory data analysis (EDA), and machine learning techniques to uncover insights and support decision-making.  
2. Develop efficient Python scripts and workflows to automate data analysis and processing tasks.  
3. Design and implement scalable data engineering solutions, including data pipelines, architectures, and frameworks for batch and real-time processing, to handle large-scale data efficiently. 
4. Design and conduct data-driven research, synthesize findings and contribute to evidence-based decision-making. 
5. Demonstrate effective communication, presentation, and collaboration skills to work successfully in multidisciplinary teams and present data-driven insights. 
6. Apply ethical principles and governance frameworks to ensure the responsible use of data and artificial intelligence in analytics and decision-making. 
7. Integrate knowledge from coursework into a comprehensive, real-world analytics project, demonstrating mastery of the field.

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

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.  Inquire with your academic advisor.

Required core courses:

• MSDA 3045 - Mathematical Statistics  
• MSDA 3090 - Python for Data Analytics  
• MSDA 3055 - Regresion Analysis and Time Series  
• MSDA 3050 - Applied Machine Learning  
• MSDA 3040 - Modern Data Engineering  
• MSDA 3035 - Data Management for Analytics  
• MSAI 3110 - Knowledge Discovery and Data Mining  
• MSDA 3999 - Capstone Practicum  

Electives

• MSDA 3060 - Data Visualization and Story Telling  
• MSDA 3100 - Applied Deep Learning  
• MSDA 3051 - Natural Language Processing (NLP)  
• MSAI 3125 - Large Language Models (LLM’s)  
• MSAI 3120 - Reinforcement Learning  
• MSDA 3440 - Special Topics: Data Analytics  
• MSCS 3451 - Generative Artificial Intelligence  
• MSDA 3950 - Independent Study  

Internship

Completing an internship is a requirement in this graduate program.  Students are responsible for securing their own internship.  While students will gain valuable experience, they will not earn academic credit for their internship.

Internship requirements:

• Must include a minimum of 120 hours
• Must be directly related to the student’s program of study
• Must be approved by the Director of Career and Professional Development

Exceptions:

• Students who have seven (7) or more years of professional work experience directly related to their program of study may seek to waive the internship requirement.
• Students who currently work full-time in a field/industry directly related to their program of study.

Students seeking to waive the internship requirement must receive permission from the Director of Career and Professional Development by the end of their second semester in the master’s program.

Review the Internship Handbook for more information. Please connect with your academic advisor to plan your studies accordingly.