Program Overview

Computer & Information Technology, MCIT

The Master of Computer and Information Technology (MCIT) program is a rigorous graduate-level program that gives talented students who have no prior experience in Computer Science an opportunity to embark on a highly successful career in computing and technology, often in coveted interdisciplinary roles or even in purely technical software development positions. The MCIT program stems from a belief that advanced computer science coursework for people with undergraduate degrees in other diverse fields leads to highly successful career paths. Our graduates and their careers today stand as strong proof to this philosophy. Within the realm of academia, pursuing further advanced study in computer science is also an option open to MCIT graduates. Due to the goals and nature of the MCIT program, it is strictly for applicants with limited or no past experience in computer science. Candidates with an undergraduate major in Computer Science or closely related fields should consider applying to the Master of Science in Engineering (MSE) program instead.

Program Requirements

• A total of 10 course units are required for the MCIT degree.
• The following courses are required:
  • CIT 5910: Introduction to Software Development
  • CIT 5920: Mathematical Foundations of Computer Science
  • CIT 5930: Introduction to Computer Systems
  • CIT 5940: Data Structures and Software Design
  • CIT 5950: Computer Systems Programming
  • CIT 5960: Algorithms and Computation
• Students must also complete four graduate-level (5000 or higher) electives.
  • At least three must be a CIS course; one may be an approved non-CIS course.
  • The following courses are recommended for all MCIT students:
    • CIS 5500: Database and Information Systems
    • CIS 5550: Internet and Web Systems
    • CIS 5730: Software Engineering
    • CIS 5570: Programming for the Web
  • Additional suitable electives include:
    • CIS 5020: Analysis of Algorithms
    • CIS 5050: Software Systems
    • CIS 5190: Applied Machine Learning
    • CIS 5510: Computer and Network Security
    • CIS 5520: Advanced Programming
    • CIS 5530: Networked Systems
    • CIS 5540: Programming Paradigms
    • CIS 5590: Programming and Problem Solving

Continuing in the Master of Science in Computer & Information Science (CIS/MSE) Program

MCIT students may apply to continue in the Master of Science in Engineering in Computer & Information (CIS/MSE) program. Students pursuing this dual degree route need to satisfy a minimum GPA requirement, have earned a high grade in at least one CIS elective, and have completed the majority of the MCIT requirements at the time of applying. In addition to the ten courses required for the MCIT program, students continuing in the CIS/MSE program need to take six additional CIS graduate courses for a total of sixteen credits; satisfactory completion results in the awarding of the MCIT and CIS/MSE degrees.

Computer & Information Technology, MCIT - Online

The online Master of Computer and Information Technology (MCIT Online) graduate-level program enables students from non-computer science backgrounds to participate in a rigorous program in which they gain a foundation in Computer Science and cultivate ways of thinking as an engineer. MCIT Online allows students from around the world to benefit from the research and teaching expertise of Penn’s world-renowned experts. While studying from the comfort of home, students take part in assignments that engage real-world tools and environments. MCIT Online provides a pathway to embark on a highly successful career in computing and technology, often in coveted interdisciplinary roles or even in purely technical software development positions.

Curriculum

• Required Courses:
  • CIT 5910: Introduction to Software Development
  • CIT 5920: Mathematical Foundations of Computer Science
  • CIT 5930: Introduction to Computer Systems
  • CIT 5940: Data Structures and Software Design
  • CIT 5950: Computer Systems Programming
  • CIT 5960: Algorithms and Computation
• Elective Courses:
  • Students must complete 4 Elective CUs from the list below:
    • CIS 5150: Fundamentals of Linear Algebra and Optimization
    • CIS 5210: Artificial Intelligence
    • CIS 5300: Natural Language Processing
    • CIS 5450: Big Data Analytics
    • CIS 5470: Software Analysis
    • CIS 5490: Wireless Communications for Mobile Networks and Internet of Things
    • CIS 5500: Database and Information Systems
    • CIS 5510: Computer and Network Security
    • CIS 5530: Networked Systems
    • CIS 5550: Internet and Web Systems
    • CIS 5690: GPU Computing for Machine Learning Systems
    • CIS 5810: Computer Vision & Computational Photography
    • DATS 5750: Cloud Technologies Practicum
    • ESE 5410: Machine Learning for Data Science
    • ESE 5420: Statistics for Data Science
    • ESE 5460: Principles of Deep Learning
    • EAS 5160: Mathematical Foundations for Machine Learning I: Probability
    • EAS 5240: Technology Ethics and the Legal Landscape
    • EAS 5740: How to Use Data
    • EAS 5850: Imaging Informatics
    • EAS 5860: Medical Image Analysis
    • EAS 5830: Blockchains
    • CIT 5820: Blockchains and Cryptography
    • HCIN 6022: Digital Health 2

Computer & Information Technology, MCIT (online) and Learning Analytics and Artificial Intelligence, MSEd Dual Degree

• Dual Degree Requirements:
  • Computer and Information Technology Requirements:
    • CIT 5910: Introduction to Software Development
    • CIT 5920: Mathematical Foundations of Computer Science
    • CIT 5930: Introduction to Computer Systems
    • CIT 5940: Data Structures and Software Design
    • CIT 5950: Computer Systems Programming
    • CIT 5960: Algorithms and Computation
    • Two Electives
  • Learning Analytics and Artificial Intelligence Requirements:
    • EDUC 6191: Core Methods in Educational Data Mining
    • EDUC 6116: Master's Foundations of Teaching and Learning
    • EDUC 6190: Feature Engineering
    • EDUC 6195: Capstone Seminar: Learning Analytics
    • EDUC 5918: Large Language Model Seminar
    • EDUC 6XXX - Deep Learning and Transformer Models
    • Two EDUC Electives
  • Other Requirements:
    • Masters Capstone Project, supported by EDUC 6195

Suggested Plan of Study - MSED Start

• First Year:
  • Fall: EDUC 6191, EDUC Elective 1
  • Spring: EDUC 6190, EDUC Elective 2
  • Summer: EDUC 6195
• Second Year:
  • Fall: EDUC 5918
  • Spring: EDUC 6116, EDUC 6XXX - Deep Learning and Transformer Models
  • Summer: CIS Elective 1, CIS Elective 2
• Third Year:
  • Fall: CIT 5910, CIT 5920
  • Spring: CIT 5930, CIT 5940
  • Summer: CIT 5950, CIT 5960

Suggested Plan of Study - MCIT Start

• First Year:
  • Fall: CIT 5910, CIT 5920
  • Spring: CIT 5930, CIT 5940
  • Summer: CIT 5950, CIT 5960
• Second Year:
  • Fall: CIT Elective 1, CIT Elective 2
  • Spring: EDUC Elective 1, EDUC 6116
• Third Year:
  • Fall: EDUC 5918, EDUC 6191, EDUC Elective 1
  • Spring: EDUC 6190
  • Summer: EDUC 6195