Program of Study

The ten course units for the Data Science degree are divided into three categories:

(As long as the prerequisites for the courses are met, students can complete these courses in any sequence.  Rules that submatriculation students should abide by are listed here. Submatriculation students should ensure that they only enroll for graduate versions of courses once they enroll in the DATS program.


1.  Foundations (two course units)

  • Programming Languages & Techniques (PL): Programming Languages & Techniques (CIT 590) or Introduction to  Software Development (CIT 591) 
  • Algorithms: Algorithms & Computation CIT 596

If students have taken these courses as part of another program, the requirement may be waived.

For submatriculants,  the programming requirement can be waived with successful completion of CIS 120. A student may also waive Foundation requirements with any other relevant course after getting department approval.  

Please understand that CIS 120/ESE 301/any other relevant undergraduate course can only be used to waive these requirements and CAN NOT be used as courses to count towards the master’s degree. Upon waiving these requirements, students must take Technical Electives or a course of their choice (subject to department approval) in lieu of them.


2. Core Requirements (three course units)

  • Math: Mathematical Statistics (STAT 512) or Linear Algebra/Optimization (CIS 515)  or STAT for Data Science (ESE 542)
  • Big Data Analytics: Big Data Analytics (CIS 545)
  • Mining and Learning: Intro to Machine Learning (CIS 519) or Machine Learning (CIS 520) or Modern Data Mining (STAT 571) or  Data-driven Modeling and Probabilistic Scientific Computing (ENM 531) or Data Mining: Learning from Massive Datasets (ESE 545)

3. Technical & Depth Area Electives (five course units)

Students must choose courses from at least 3 of the buckets listed below. Two courses must represent a depth sequence, which can be the thesis/practicum or two courses which build on each other (e.g. one is a prerequisite of the other).

BUCKETS for Technical & Depth Area Electives

Applications

Register for two credits of DATS 597/Master’s Thesis or two credits of DATS 599/Master’s Independent Study. Suggestions for projects will be provided to students. Students may choose from these suggested projects or may also come up with their own project/advisor ideas. Students will be mentored jointly by the Program Director and by an advisor in the area of the project, and must receive approval by Faculty Director.
  • Brain-Computer Interfaces (BE 521)
  • Network Neuroscience (BE 566)
  • Mathematical Computation Methods for Modeling Biological Systems (BE 567)
  • Introduction to Computational Biology and Biological Modeling (CIS 536)
  • Biomedical Image Analysis (CIS 537)
  • Theoretical and Computational Neuroscience (PHYS 585)
  • Bioinformatics (STAT 953)
  •  Econometrics I- Fundamentals (ECON 705) 
  •  Econometrics III: Advanced Techniques of Cross-Section Econometrics (ECON 721)
  •  Econometrics IV: Advanced Techniques of Time-Series Econometrics (ECON 722)
  •  Applied Probability Models in Marketing (MKTG 776)

Methods

  • Forecasting and Time-Series Analysis (STAT 910)
  • Sample Survey Methods (STAT 920)
  • Observational Studies (STAT 921)
  • Modern Regression for the Social, Behavioral and Biological Science (STAT 974
  • Accelerated Regression Analysis (STAT 621)
  • Molecular Modeling and Simulations (CBE 525
  • Computational Science of Energy and Chemical Transformations (CBE 544
  • Finite Element Analysis (MEAM 527
  • Computational Mechanics (MEAM 646)
  • Atomic Modeling in Materials Science (MSE 561
  • Multiscale Modeling of Biological Systems (BE 599)
  • Mathematical Computation Methods for Modeling Biological Systems (BE 567)
  • Advanced Linear Algebra (AMCS 514)
  • Algorithms (CIS 502)
  • Linear Algebra/Optimization (CIS 515)
  • Computational Learning Theory (CIS 625)
  • Randomized Algorithms (CIS 677
  • Numerical Methods (ENM 502)
  • Data-driven Modeling and Probabilistic Scientific Computing (ENM531)
  • Introduction to Optimization Theory (ESE 504)
  • Data Mining: Learning from Massive Datasets (ESE 545)
  • Simulation Modeling and Analysis (ESE 603
  • Convex Optimization (ESE 605)
  • Information Theory (ESE 674)
  • Stochastic Processes (STAT 533)

Information re: Data Science (DATS) Minor can be accessed here


More Information

  • Information re: Computer & Information Science courses and faculty can be accessed here
  • Information re: Statistics courses and faculty can be accessed here
  • Information re: Electrical & Systems Engineering courses and faculty can be accessed here
  • Information re: Bio engineering courses and faculty can be accessed here
  • Information re: Physics courses and faculty can be accessed here
  • Information re: Economics courses and faculty can be accessed here
  • Information re: Marketing courses and faculty can be accessed here
  • Information re: all courses offered at the University of Pennsylvania can be accessed here