MSDA Program
Our Master of Science in Data Analytics program focuses on combining high-quality expertise in computer science with business analytic skills so that massive volumes of data may be visualized and analyzed to reveal emerging business trends. The program also focuses on application of advanced data mining techniques to predict events important to the organization. The graduates of this innovative program will bridge gaps between accomplished business executives and the technological expertise that can help organizations derive value from data.
The UTC MSDA program is offered as a partnership between the Gary W. Rollins College of Business and the College of Engineering and Computer Science. This partnership means our students receive the best in instruction and support from both colleges.
Participants in our program have the opportunity to choose a business track or a computer science track.
Business Track
Complete the seven core MSDA courses and then choose three or four elective courses in prescriptive analytics, accounting for managers, organizational leadership, economics for managers, financial management or marketing management.
- Complete an internship or a practicum project.
Computer Science Track
Complete the seven core MSDA courses. Students pursue a thesis and choose three elective courses OR no thesis and choose four elective courses and a project.
- Elective options include: computing systems, structuring programs and data, cloud computing, structured data exchange, and advanced information security management.
Students who complete the program will:
- Understand the issues related to management of Big Data and apply Big Data analysis to real-life business problems.
- Learn ways of collecting and analyzing data from different data sources such as databases, flat files, web sites, web logs, blogs, and videos.
- Learn design and use of analytical databases through SQL queries.
- Demonstrate ability to perform effective data visualization and exploration by using state-of-the-art programming languages and tools, such as Python, R, Tableau, and D3.
- Be able to develop appropriate machine learning models for real-world problems, optimize the models learned and report on the expected accuracy that can be achieved by applying the models.
- Provide the theoretical and practical understanding of the key methods of data preparation, reduction and exploration that form the heart of analytics techniques for business decision making.
- Develop understanding of advanced data analytics and mining techniques to transform large and complex data into actionable information for the organization.
- Gain practical experience in applying knowledge and skills acquired throughout the program by internship and/or practicum project. As an alternative gain research experience and skills through thesis work.
