Penn Engineering’s accelerated MCIT Online degree combines Ivy League quality with the flexibility of a virtual experience.
Designed specifically for students who are new to computer science, MCIT Online offers the same innovative curriculum and high-quality teaching as Penn’s on-campus program. Regardless of what you studied as an undergraduate, motivated students will build a strong foundation in computer science and gain real-world coding skills.
Core courses and electives blend computer science theory and applied, project-based learning. You’ll use real-world tools and environments such as TensorFlow and Amazon Cloud, and you’ll have ongoing access to Penn Engineering faculty and TAs through live office hours and discussion forums.
“Working with an instructional designer improved the way I teach my courses on campus. Since incorporating some of the online learning methodologies into my course, I found I am connecting with students more than I have in years.”
“My course teaches two of the most popular programming languages in the world to people who have never coded before. After taking my course, students have the ability to create a desktop or mobile app, or use the concepts in fields such as data science and visualization.”
“Teaching in this program has been incredibly rewarding. I am blown away by how motivated our students are, especially given that many of them are working full time and taking classes at the same time.”
To earn a master’s degree, you’ll complete ten courses – six core courses and four electives. All courses are fully online, and there are no required real-time sessions.
We recommend that you take the core courses in sequential order, but it is not required. You must take CIT 591 in your first semester and complete four core courses before registering for electives.
This course is an introduction to fundamental concepts of programming and computer science for students who have little or no experience in these areas. Students learn how to read and write to files, connect to databases, and use regular expressions to parse text. This course includes substantial programming assignments in both Python and Java, and teaches techniques for test-driven development and debugging code.View Full Course Description
This course introduces students to math concepts that form the backbone of the majority of computer science. Topics covered include sets, functions, permutations and combinations, discrete probability, expectation, mathematical induction, and graph theory. The goal of the course is to ensure that students are comfortable enough with the math required for most of the CIS electives.View Full Course Description
This course provides an introduction to fundamental concepts of computer systems and computer architecture. Students learn the C programming language and an instruction set (machine language) as a basis for understanding how computers represent data, process information, and execute programs.View Full Course Description
This course focuses on data structures, software design, and advanced Java. The course starts off with an introduction to data structures and basics of the analysis of algorithms. Important data structures covered include arrays, lists, stacks, queues, trees, hash maps, and graphs. The course also focuses on software design and advanced Java topics such as software architectures, design patterns, and concurrency.View Full Course Description
This course is a continuation of CIT 593 and introduces students to fundamental concepts in computing systems. The course will use the C program language, and will develop your knowledge on C system calls, and libraries for process/thread creation and manipulation, synchronization, and network communication.View Full Course Description
This course focuses primarily on the design and analysis of algorithms. It begins with sorting and searching algorithms and then investigates graph algorithms. In order to study graph algorithms, general algorithm design patterns like dynamic programming and greedy algorithms are introduced. A section of this course is also devoted to understanding NP-Completeness.View Full Course Description
You’ll complete four graduate-level electives. Here are the options:
There are hardly any machine learning problems whose solutions do not make use of linear algebra. This course places emphasis on linear regression, data compression, support vector machines and more, which will provide a basis for further study in machine learning, computer vision, and data science.View Full Course Description
This course investigates algorithms to implement resource-limited knowledge-based agents which sense and act in the world. Topics include, search, machine learning, probabilistic reasoning, natural language processing, knowledge representation and logic. After a brief introduction to the language, programming assignments will be in Python.View Full Course Description
This course focuses on the fundamentals of scaling computation to handle common data analytics tasks. You will learn about basic tasks in collecting, wrangling, and structuring data; programming models for performing certain kinds of computation in a scalable way across many compute nodes; common approaches to converting algorithms to such programming models; standard toolkits for data analysis consisting of a wide variety of primitives; and popular distributed frameworks for analytics tasks such as filtering, graph analysis, clustering, and classification.View Full Course Description
This course provides a rigorous and hands-on introduction to the field of software analysis – a body of powerful techniques and tools for analyzing modern software, with applications to systematically uncover insidious bugs, prevent security vulnerabilities, automate testing and debugging, and improve our confidence that software will behave as intended.View Full Course Description
Study today’s state-of-the-art wireless technology (4G LTE), next-generation wireless technology (5G NR), Wi-Fi technologies and the Internet of Things. You’ll build a simple IoT service with an IoT client device emulator and a real IoT server platform on the Internet.View Full Course Description
Structured information is the lifeblood of commerce, government, and science today. This course provides an introduction to the broad field of information management systems, covering a range of topics relating to structured data, from data modeling to logical foundations and popular languages, to system implementations.View Full Course Description
This is an introduction to topics in the security of computer systems and communication on networks of computers. The course covers four major areas: fundamentals of cryptography, security for communication protocols, security for operating systems and mobile programs, and security for electronic commerce.View Full Course Description
This is an introductory course to computer vision and computational photography. This course will explore four topics: 1) image feature detection, 2) image morphing, 3) image stitching, and 4) deep learning related to images. This course is intended to provide a hands-on experience with interesting things to do on images/pixels.View Full Course Description
This course presents the fundamental kinematic, dynamic, and computational principles underlying most modern robotic systems. The main topics of the course include: rotation matrices, homogeneous transformations, manipulator forward kinematics, manipulator inverse kinematics, Jacobians, path and trajectory planning, sensing and actuation, and feedback control.View Full Course Description
Introducing the fundamentals of cryptography and distributed systems that underpin modern blockchain platforms — including collision-resistant hash functions, digital signatures and classical consensus algorithms and examining the architecture of modern blockchain platforms, and develop tools to analyze and interact with them in Python.View Full Course Description
Learn a broad range of statistical and computational tools to analyze large datasets through a solid foundation of data science, statistics and machine learning to make data-driven predictions via statistical modeling and inference. Using case studies and hands-on exercises, practice and increase data analysis skills using Python to extract meaningful information from large datasets.View Full Course Description