MS in Data Science

Curriculum 

The MSDS is a 32-credit flexible program designed to meet the goals of students looking to pursue either academic or professional careers in data science, and can be completed in as little as 9 months. Students will declare either a Core Methods Focused Concentration or Applied Methods Focused Concentration. In addition to the core curriculum and concentration courses, the MSDS program offers students a unique opportunity to enhance their learning through an optional summer internship or master’s thesis course. As a result, the program can be extended and completed over 12 or 16 months. All students begin the program once every year in September; Spring entry term is not offered.

Degree Requirements

Eight semester courses (32 credits) approved for graduate study are required. 

Course requirements include 5 competency courses. Students are expected to take ONE course in each of the following areas (5 courses total):

• A1 Modeling and Predictive Analytics

• A2 Data-Centric Computing 

• A3 Machine Learning and AI

• A4 Social Impact

• A5 Security and Privacy

Plus 3 additional courses:

• CDS DS 701 Tools for Data Science (The only required course for the MSDS program)
• Concentration Elective 1
• Concentration Elective 2

Concentrations

All MSDS students must declare either a core methods concentration or an applied methods concentration, both of which consist of 3 courses (12 credits)

Core Methods Option

Methods focused students are expected to be interested primarily in the development of general (application agnostic) data science methods and will most likely come from STEM undergraduate majors.  

Students will take DS 701 and 2 additional courses from any of the A1, A2, or A3 competencies. 

Applied Methods Option

Applied Methods focused students are expected to be especially interested in the development and application of special-purpose data science in applied areas such as Management, Public Health, Cybersecurity, etc.  Such students may also be transitioning into data science from one of those fields. 

Students will take DS 701 and 2 additional courses from any of the approved applied methods courses.

Additional Degree Components

Please note that both additional degree components come with an additional tuition cost. The program tuition number shown on the tuition website does not include those additional degree components. Adding the additional degree components will also extend the total length of the program.

Master Thesis Course (4-credits)

The Master thesis course will take place during the 3rd semester. If an MS student wants to add this additional degree component, they must have a faculty member to serve as their Thesis Advisor and complete a thesis application. The Thesis Advisor must be a faculty member from CDS, either core or affiliated. Students who are approved for a thesis will be registered for a Master's Thesis course (DS799). If the MS thesis spans more than one semester, the student must receive approval from their advisor. Students must declare their intention to pursue a thesis by the end of the add/drop period in their 2nd semester. At this point students should identify an advisor and the advisor should agree to supervise the thesis.

Summer Internship Course (1-Credit)

This is NOT a Co-op course and does not provide internship placements. However, career resources are provided for MSDS students during the job searching process.

The summer internship should be in the industry area of focus and completed during both summer terms for a total of 12 weeks. International students will have the opportunity to apply for CPT for their internships as well. The summer internship will happen after you finish the program in both Fall and Spring semesters. During the spring semester, you will need to submit an application, which will include information about your summer internship, to the graduate affairs office. Once your application is approved, you will be registered for a one-credit summer internship course for CPT purposes.

Learning Outcomes

• • Mastery of the principal tools of data decision making, including defining models, learning model parameters, management, and analysis of massive datasets, and making predictions.

  • Demonstrated competence in application of data science tools to address substantive 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.

  • Ability to extend tools of data decision making, including building specialized computational pipelines, automating data workflows, and developing human-computer interfaces.

  • Ability to interpret and explain results, including assessing uncertainty and developing data visualizations.

  • Gain awareness 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.