MS in Data Science

The MS in Data Science is a flexible program designed to meet the goals of students looking to pursue either academic or professional careers in data science. Upon completion of the program, students will be prepared to pursue careers in which they will become leaders in their chosen areas, whether in academia, through advanced graduate work in a PhD, or in industry, by collaborating, directing, and effectively managing diverse teams of practitioners working at the forefront of industrial R&D.

Learning Outcomes

• At least one of the following:
  • Core Methods–Focused: Mastery of the principal tools of data decision-making, including defining models, learning model parameters, management and analysis of massive datasets, and making predictions.
  • Applied Methods–Focused: Demonstrated competence in application of data science tools to address substantive questions in one or more applied areas. Students will demonstrate meaningful engagement with multiple questions in one or more applied areas and will address those questions through sophisticated use of data science tools, including tools specifically appropriate for each applied area.
• At least one of the following:
  • Core Methods–Focused: Ability to extend tools of data decision-making, including building specialized computational pipelines, automating data workflows, and developing human-computer interfaces.
  • Applied Methods–Focused: Ability to interpret and explain results, including assessing uncertainty and developing data visualizations.
• Awareness gained of the social impacts of data-centered methods, including ethical considerations, fairness, and bias.
• Ability to understand and adhere to policy, privacy, security, and ethical norms.

To meet the learning outcomes as described above, students will be required to declare and follow a Core Methods–or Applied Methods–focused concentration through the program.

Course Requirements

Eight term courses (32 units) approved for graduate study are required. Requirements include five core competency courses, with at least one in each of the following areas:

• Modeling
• Data-Centric Computing
• Machine Learning and AI
• Social Impact
• Security and Privacy

For a full list of approved courses, see the department website.

Students are also required to complete 12 concentration units in either the Core Methods or Applied Methods Concentration.

Master’s Thesis

While not required, MS candidates may complete a master’s thesis carried out with the approval, and under the supervision, of a faculty member.