Developed by the School of Information Studies in conjunction with the Martin J. Whitman School of Management, the M.S. in Applied Data Science draws insights from both the field of information studies and the field of management to help students effectively apply analytical concepts to gain insight from data. The interdisciplinary curriculum fosters collaboration, problem-solving and analysis with diverse professionals. As a result, students learn practical analytical and technical skills to make data-driven decisions using data capture, management, analysis and communication.
Why Earn a Master’s in Data Science?
Ranked as Glassdoor’s No. 1 Job of 2016 and 2017, data scientists are critical to the success of any organization. As the data science field evolves, the demand for analytics skills continues to grow. Employers are actively seeking candidates with the advanced technical expertise to make data-driven decisions.
A master’s in data science can help meet the demand in a variety of careers, including:
Business intelligence analyst
The M.S. in Applied Data Science prepares students to use applications of data science so they can effectively perform the following functions in a variety of contexts:
Understand major practice areas in data science
Collect, organize and manage data
Identify patterns in data using visualization, statistical analysis and data mining
Develop actionable insight based on data
Communicate data analytics and findings to people across a broad range of industries
Synthesize and understand data science ethics and privacy
The M.S. in Applied Data Science curriculum is 36 credits and can be completed in 18 months. Designed to provide students with critical-thinking and problem-solving skills, the curriculum is structured as follows:
Common Core Courses – 18 units
Common core courses build foundational knowledge and skills in preparation for more advanced data application and techniques.
Analytics application core courses provide students with an opportunity to choose one or two specializations so they can develop their preferred area of expertise. Students will learn to apply practical data science techniques to solve complex problems and pull insights from data.
Students choose specific projects throughout the program that best showcase their particular skills. Completed projects are included in the final portfolio and submitted to a panel of faculty for review in students’ final term.