Data Science & Analytics Master’s Degree
The DSA is designed with an in-depth, technically rigorous core curriculum followed by domain specialized emphasis area courses. Advanced Big Data and Analytics tools and techniques are used throughout the program coursework. Courses are delivered as 8-week/module online courses. Additionally, students spend one week each spring on campus with faculty during an executive session.
Data Science as a Collaborative Lifecycle
We teach data science as a collaborative lifecycle, where data and processes have provenance from the raw data stage through to the resulting business intelligence products. A data lifecycle starting with data curation, exploratory data analysis, statistical and machine learning modeling and refinements, and decision product production is used to tell the story of the data.
Two key philosophies drive this:
 Data scientists must be able to tell the story of data, including walking stakeholders backward from the final product, all the way to the beginning of the source data; including discussing the implications of all assumptions and transformations and decisions made along the way.
 Data scientists will be working increasingly in interdisciplinary teams, potentially distributed, where collaboration of ideas and exploration and processes are essential. Version control systems are central within our curriculum, with students using Git to fetch new modules and submit their completed work.
Classes are uniformly designed from the ground up with a consistent weekly schedule and pace, allowing working professionals to successfully manage their work-life-education commitments. Numerous students within this class have already begun transitioning to more analytical work-roles.
Students will work closely with faculty mentors, gain experience through solving real-word Big Data issues, explore concepts targeted to their emphasis and more.
The capstone project teams up students with faculty & members of industry for hands-on experience with large data sets and the latest technology and techniques.
Required Credits: (34 Hours)
- 19 Credit hours for Data Science Core Curriculum
- 9 Credit Hours for Emphasis Area courses
- 3 Credit Hours of Case Study
- 3 Credit Hours Capstone
“The biggest and most important thing
that I have learned is how to clean up data…how to figure out what is most important and how to tell that story with great visualization.”
Developing Soft Skills
One of the key targets of our training is to develop the students’ soft skills as it relates to data science and communication. To facilitate this, we use a cohort-based program that develops the students’ sense of community and collaborative group dynamics.
The diverse student backgrounds enable some lively discussions and numerous perspectives based on prior experiences and the diverse industry background. We strive to enhance student learning of technology in cooperative, team oriented settings.
Students are routinely (weekly) tasked to perform cooperative communication with fellow students to help them develop their communication and collaboration skills around the technical coursework and subject matter.