Field Experience Concentration Requirements

Expanding opportunities for practical training for entry into the workforce.

Expanding opportunities for practical training is an essential part of training students for entry into the workforce and improving their ability to interview effectively upon graduation. Students completing the concentration will satisfy the requirements of our existing master's program with the addition of conducting an internship, co-op, practicum, or other similar experience with a commercial company, government agency, or similar entity. Students will gain experience in applying knowledge from their courses in "real world" situations.

Students will apply knowledge gained in a classroom setting in an industrial, governmental, or other practical setting.  This will help them learn about the process of taking ideas from an academic setting and implementing them in a robust and maintainable fashion. Each internship is unique, but they will all help students understand what it takes to translate ideas and algorithms into working systems.

This exposes students to practical implementations of algorithms and systems from late-stage undergraduate courses and introductory MS courses. Students will then take that implementation experience and use it for a project in a graduate-level computer science course after the field experience. This creates a feedback loop of theory and practice.

A requirement of this concentration is to complete this practical experience early on in the degree program. This practical experience happens after at least two courses, one in systems and one in AI. These courses provide students with the foundational knowledge applied during the practical experience. After the practical experience, students will apply what they learned to at least one project-based course.

If you are participating in this concentration, please complete the pre-application form at the link below.* This form allows CICS staff to ensure students are on the correct path for this concentration. You can access this form via your UMass email; if you are logged into any other Google accounts the form will not work, so please log in to your UMass email.

Submit a pre-application

*New MS students, please do not fill out the pre-application until you are physically in the U.S. 

Questions about the Field Experience Concentration can be directed to Elizabeth Parolski, master’s advisor, at eparolski [at] umass [dot] edu (eparolski[at]umass[dot]edu).

Before the practical experience, students must complete at least two introductory courses that will provide foundational knowledge for the practical experience. Those courses must include two of our MS core courses, one from systems and one from AI, from the following list: 

Systems:

  • COMPSCI 520: Theory and Practice of Software Engineering 
  • COMPSCI 532: Systems for Data Science
  • COMPSCI 560:  Introduction to Computer and Network Security
  • COMPSCI 578: Distributed Computing and Systems
  • COMPSCI 630: Systems 
  • COMPSCI 645: Database Design and Implementation
  • COMPSCI 653: Advanced Computer Networking
  • COMPSCI 660: Advanced Information Assurance
  • COMPSCI 677: Distributed and Operating Systems 
  • COMPSCI 690AB: Systems for Deep Learning

AI:

  • COMPSCI 685: Advanced Natural Language Processing
  • COMPSCI 603: Robotics
  • COMPSCI 646: Information Retrieval
  • COMPSCI 670: Computer Vision
  • COMPSCI 589 or 689: Machine Learning 
  • COMPSCI 682: Neural Networks: Modern Intro

Additional and/or alternate courses, AI and Systems only, will be approved on a student by student basis only. Students must endeavor to take the above classes before seeking special approval for an alternate class.

Students must pass both the AI and the Systems courses before they can carry out their internship/CPT. If one course is passed and the other is failed, students can no longer carry out their internship.

Conduct an internship at an industrial company, government agency, or similar entity prior to starting the final semester of the MS CS program. The internship may be conducted during the summer after one semester or during the second or third semester, but not during the final semester. The internship will not earn you credit towards your MS degree. All internships must be approved by the college. If the internship is during the semester, students will need to pay for the 1-credit CPT course at the semester tuition rate. If the internship is during the summer and or winter, the CPT course is billed at the summer rate. 

After the field experience, students must take at least one course that has been approved by the graduate programs team, that has a major project component. Independent study (CompSci 696 or CompSci 696DS or CompSci 696E) and master's project (CompSci 701) will automatically qualify. Any other course that has a major project component involving implementation will also qualify. Students should consult with instructors to find out if a major project component exists in the course and the department will maintain a list of courses that typically do.

Approved Project-Based Courses:

  • COMPSCI 520: Theory & Practice of Software Engineering
  • COMPSCI 528: Mobile & Ubiquitous Computing
  • COMPSCI 532: Systems for Data Science 
  • COMPSCI 571: Data Visualization & Exploration
  • COMPSCI 576: Game Programming
  • COMPSCI 590U: Mobile and Ubiquitous Computing
  • COMPSCI 596: Independent Study*
  • COMPSCI 596E: Machine Learning Applied to Child Rescue
  • COMPSCI 621: Advanced Software Engineering: Analysis and Evaluation
  • COMPSCI 645: Database Design & Implementation
  • COMPSCI 646: Information Retrieval
  • COMPSCI 682: Neural Networks
  • COMPSCI 685: Advanced Natural Language Processing
  • COMPSCI 696DS: Data Science Industry Mentorship
  • COMPSCI 696: Independent Study*
  • COMPSCI 696E: Machine Learning Applied to Child Rescue
  • COMPSCI 701: Master's Project
  • STAT 535: Statistical Computing (ONLY when a project is part of the course, check the syllabus, and have MS Advising sign off on this class)

*Can be done with departments other than computer science. If done outside of CS, the course listing will be the name of the department and then 696; for example, ECE 696, Physics 696, KIN 696, etc. 

The list of project-based courses is based on courses and their syllabi from the 2022-2023 academic year. This list can and will change as courses and instructors change.