Innovative Curriculum Tailored to the Needs of Industries and Employers
The Master of Science in Applied Data Science (MS-ADS) curriculum is designed to equip graduates with the technical strategies and skills they will use to apply powerful and modern analytical tools to real-world applications.
The program culminates in a Capstone experience that pairs them with fellow students, instructors and potential industry partners on an in-depth project that hones their ability to apply their skills in the workplace.
Designed to be completed in 20 months over five semesters, the program requires students to complete 30 academic units plus 6 prerequisite units (note: the prerequisites may be waived depending on a candidate’s education and experience in mathematics, engineering, programming, computer science and analytics). Of the 30 academic units:
6 units focus on introductory topics and fundamentals
21 units cover technical aspects essential to the field of data science
3 units are dedicated to the comprehensive Data Science Capstone Project
Each course is seven weeks long (except for the Capstone), with two courses offered each semester; the entire program can be completed in 20 months. The master’s degree you will earn as an online graduate student is the same as that earned by campus-based students.
The MS-ADS program has been developed by data science experts in close collaboration with key industry and government stakeholders to provide in-depth practical and technical training designed to position graduates for career success in this vitally important and fast-growing field.
All courses in the program are instructor-led and asynchronous, enabling you to work on your assignments on your own schedule while still meeting deadlines. If you are balancing coursework with a full-time job or other time commitments, asynchronous learning offers you a great deal of flexibility. Materials needed for assignments are readily accessible so you can access them and do your classwork when the time is right for you.
The courses are specially designed for online learning with the course content prepared by your professor with input and support from the academic director and the program’s board of advisors. In addition, professors may choose to offer live virtual events such as office hours; however, such events are optional, and all live sessions will be recorded. Attendance during live events is always optional in consideration of students who may not be available during those designated times. Students will interact with their peers asynchronously through weekly discussion posts, and some courses may include practical team projects.
Program Learning Outcomes
Successfully create, apply, and practice the mastery of data science methods, tools, and programming abilities (technical skill sets) for the analysis of structured and/or unstructured datasets.
Learn, develop, and demonstrate non-technical skill sets, such as problem-solving, statistical thinking, creativity, critical thinking, storytelling, presentation ability (written and oral), and other soft skills in addressing data-driven projects. Build high-performing and compelling data science teams.
Apply data science and data engineering related to AI, machine learning, and predictive modeling for the development of data-driven products, business strategies, and actionable insights.
Evaluate ways data science positively impacts enterprise and society. Recommend strategies for partitioning and protecting data based on ethics, regulatory, and other requirements.
Serve as active leaders and managers of data science multiple functions within organizations.