Applied Data Science Master’s Degree

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Use data to gain insight and solve problems in the real world.

We’re living in an age where data powers our daily lives, and organizations of all sizes are using data to drive critical business decision-making.

Gathering this data raises questions about accuracy, privacy, equality, and ethics. Analyzing it requires deep analytical and technical expertise. And using it to inform business decisions requires communication and management skills.

These skills have never been in higher demand. In fact, Glassdoor has ranked data scientist as the best job for the last five years in a row.

Our program, developed in collaboration with the Whitman School of Management, prepares you for this rewarding career. You’ll learn how to analyze and operationalize all sizes of data sets. Understand information science and management principles. And apply data science to enterprise operations and processes.

The ADS degree program is 36 credits and is typically completed within 2 years. The curriculum combines a primary core, analytics application core, and electives to give students a strong data science and analytics foundation with a secondary focus of their choosing.

The 36 credits are distributed as follows:

  • Primary Core – 18 credits
  • Analytics Application Core – 3 to 6 credits
  • Elective Courses – 12 to 15 credits
    • Some elective credits can also be used toward a CAS in Information Security Management.
  •  Exit Requirement – Portfolio Milestone
    • In their final term, students will be required to submit a personal portfolio of projects which demonstrate full competency of the learning outcomes to a panel of program faculty.

View Curriculum in Course Catalog

As an interdisciplinary program, the master’s in Applied Data Science provides students the opportunity to learn in a broad range of areas related to data science. Successful students in our program will be able to:

  • Describe a broad overview of the major practice areas of data science.
  • Collect and organize data.
  • Identify patterns in data via visualization, statistical analysis, and data mining.
  • Develop alternative strategies based on the data.
  • Develop a plan of action to implement the business decisions derived from the analyses.
  • Demonstrate communication skills regarding data and its analysis for managers, IT professionals, programmers, statisticians, and other relevant professionals in their organization.
  • Synthesize the ethical dimensions of data science practice (e.g., privacy).

A vibrant learning community.

Connect with leaders in the data science field, forge friendships and connections with peers in your industry, and gain a lifelong network. Life here is active in and outside of the classroom.