The 10 Course Units in the degree are divided into four categories.

Foundations (2 Course Units)

  • Probability (ENM 503 Intro to Probability & Statistics or STAT 510 Probability or MATH 546 Advanced Probability)
  • Programming Languages and Techniques (CIT 590 Programming Languages & Techniques or CIT 591 Introduction to Software Development)

Students who have taken courses in these or equivalent areas may petition to have Foundations course requirements waived.  The CU(s) will be reallocated to the Technical Electives.

Core Requirements (3 CU)

  • Statistics (CIS 515 Linear Algebra/Optimization or CIS 625 Computational Learning Theory or STAT 512 Mathematical Statistics)
  • Machine learning (CIS 519 Intro to Machine Learning or 520 Machine Learning or STAT 571 Modern Data Mining)
  • Big data analytics (CIS 545 Big Data Analytics)

Thesis / Practicum (2 Course Units)

Technical and Depth Area Electives (3 Course Units)

Students may take one course in an approved Technical Elective and two additional courses from a Depth Area, in consultation with the Program Director.

Potential depth areas for the Master’s degree include:

  • Computer and information science
  • Electrical and systems engineering
  • Network and social science
  • Digital humanities
  • Biomedicine

Practicum Projects

Through a matching process, highly proficient students will have the opportunity to participate in data science projects proposed from across campus, particularly through the various institutes and centers that Penn offers.  Students should have taken at least one elective course in the target application domain before embarking on the project.  The student will be mentored jointly by the Program Director and by an advisor in the area of the project.

Click here for more detailed information re: the Master of Science in Engineering in Data Science curriculum.