Here’s what the subjects are in MSc in computer science:
There are hundreds of different courses and subjects that could be part of a master’s in computer science.
The specific subjects for any one degree will depend on the learning institution, the exact nature of the degree, and the area of expertise for the student.
There are too many to fit on a short list.
So if you want to learn all about the subjects of a master’s in computer science, then this article is for you.
Keep reading!
What Constitutes a Master’s in Computer Science?
A master’s in computer science will take students who already have a strong background in the field and help them pursue specialized skills in various subcategories of the general topic.
While someone with a bachelor’s in computer science probably has a strong basic understanding of coding and computer systems, a master’s program will explore individual topics a lot more closely and deeply.
Like other master’s programs, computer science degrees often require 30-45 credits and take an average of 2 years to complete.
By the end of the degree, students should have an area of expertise within the broader computer science discipline.
What Are the Most Common Courses in an MSc in Computer Science? (18 Courses)
There are thousands of unique computer science degrees across many universities and colleges around the world.
There is no set-in-stone list of classes that one might take to pursue a master’s in computer science.
While completing such a degree program, students have to find areas of specialty, and those specialties will often dictate which exact classes are taken.
As an example, artificial intelligence is just one of many specialties one can obtain as a computer scientist.
If artificial intelligence is your primary pursuit, you’re likely to take multiple classes under that umbrella.
But, a computer scientist specializing in computer graphics might not study artificial intelligence at all.
So, I can’t give you a master list of all of the courses in computer science. It would be way too long.
Instead, I’ve reviewed about a dozen computer science programs from across the United States (looking at programs at Stanford, MIT, and many more).
From those programs, I’ve listed the most common courses and subjects that I found below.
#1 Computer Networks
A computer networks class is going to cover varying levels of networking concepts.
Depending on the course, it can get deep into the underlying principles of what constitutes a network, or it can focus on applications related to networking.
From a computer science perspective, networking topics will cover protocols, flow control, error detection, routing techniques, and other related topics.
Additionally, computer network classes can focus on network design as well.
All are reasonably covered under the greater umbrella of computer science, and which topics are favored will depend on your area of specialty and the focus of the course.
#2 Database Management
Database management courses usually look into specific systems for running databases.
These systems can include things like MySQL or Oracle.
By getting deep into the use and implementation of these systems, courses can show computer scientists practical applications and how to manage databases.
Another viable perspective is to get into the design elements of a database management system.
In such a course, students would learn about hierarchical, network, and/or object-oriented database systems and potentially even design a system from the ground up.
#3 Analysis of Algorithms
For anyone unfamiliar, an algorithm is a logical process that governs a computer program.
So, before you code a program, you will usually figure out the algorithm first.
This is the list of steps the computer needs to take to complete a task.
Once you have an algorithm, you can then write it out in a computer language to turn it into functioning code.
In the analysis of algorithms, computer scientists will explore techniques that judge algorithms for efficiency, resource allocation, and efficacy.
Basically, when you have reliable, systematic ways to analyze algorithms, you can find ways to improve them before you code them.
#4 Operating Systems
Operating systems are what make computers work.
Windows, Android, and iOS are very popular examples of operating systems.
At a master’s-in-computer-science level, courses on operating systems will look into key elements that make operating systems function.
While an undergrad computer class might show students how to interact with a popular operating system, a master’s in computer science would probably get deeper into how to build operating systems.
While coding an entire operating system from scratch could constitute an unreasonable workload for a single class, operating systems studies could get pretty deep into the mechanics and applications of building operating systems.
#5 Computer Language Theory
Computer languages are what programmers use to turn algorithms into functioning code.
Well-known computer languages include Python, Java, and SQL.
Ultimately, there are a lot of programming languages out there.
In computer language theory, students explore the elements of what makes a computer language function and how theoretical elements can add value to a language—especially in terms of niche uses and applications.
Like any study in theory, this is about breaking down the elements of the idea (such as what makes a computer language a computer language) and using those elements to construct new ideas and a deeper understanding.
#6 Software Engineering
Software engineering is the practice of building completely functioning programs.
Computer science can cover a wide range of topics and applications, but software engineering is more specific.
If you think of any of the software you use, from operating systems to TurboTax, the truth is that a lot of work goes into all of it.
What separates software engineering from other elements of computer science is understanding the systems-level engineering that is necessary to create functioning software.
Essentially, someone has to see the big picture in order to organize a bunch of different computer scientists into groups that can write all of the necessary code.
Software engineering classes tend to focus on the big-picture approach, and to do so, they often look at systems architecture, software systems design, and project management as key concepts.
#7 Computer Graphics
Computer graphics exists on a completely different part of the computer science spectrum.
While software engineers are looking at big picture concepts to build complete software packages, the study of computer graphics looks specifically at how to use computers to describe visual images.
Computer graphics classes can cover image rendering, multi-dimensional mechanics, and many other related concepts.
Ultimately, they are all in service of telling computers how to render images on a screen.
#8 Machine Learning
Machine learning covers a wide range of ways to approach artificial intelligence.
Neural networks, data mining, supervised learning, and unsupervised learning are some common ways of approaching machine learning.
While the scope of the class will vary from school to school, machine learning usually covers essential topics necessary to build systems that enable machines to improve their functionality over time.
There are two core components of machine learning: collecting data and training.
Both can be approached in many ways, and machine learning classes usually cover more than one of each.
#9 Biometrics
In computer science, biometrics is a way to use human characteristics in programming.
Arguably the most common application is to use biometrics for security and identification.
If you have ever used your thumbprint or face to unlock your phone, that was an application of biometrics.
Biometrics classes cover mechanisms to collect biometric data.
As an example, a class could show students how to teach a computer to differentiate different fingerprints.
Additionally, biometrics classes look at applications of biometric data in computer science.
Unlocking phones is nice, but it’s hardly the limit of what computers can do with biometric data.
#10 Artificial Intelligence
I took you through machine learning earlier.
Machine learning is really a subcategory of artificial intelligence (AI).
This is the process of teaching machines to do tasks.
Virtually all automation involves some form of artificial intelligence.
While machine learning focuses on mechanisms that allow computer systems to improve at tasks, artificial intelligence can be approached in other ways.
Heuristics, for example, is a method of programming long decision chains that allow computers to navigate a wide range of possible scenarios and outcomes.
Artificial intelligence classes typically look at the primary forms of programming AI, such as heuristics and machine learning, to expose students to these varying approaches.
#11 Cryptography
Cryptography is the study of secure communications.
In a broad context, cryptography includes making or breaking secret codes.
From the computer science perspective, cryptography is usually about securing information.
Encryption, hash functions, key design, and hand-offs are common topics that come up.
Blockchain, cryptocurrency, and even signing into an account with a password are all applications of cryptography.
There is a berth of topics to choose from, and no one cryptography class can cover it all in-depth.
The scope of the class will really depend on who wrote the curriculum.
#12 Big Data Analytics
Big data and analytics are popular in a lot of professional spaces.
With very large volumes of data and powerful analytical techniques, computer scientists can identify trends and patterns that don’t otherwise emerge.
But, to qualify as big data, the amount of information has to be so large that traditional systems are not capable of handling the job.
So, in big data analytics courses, computer scientists learn ways to leverage resources and push the boundaries of what current computer systems can accomplish.
Classes also often cover analytical techniques that can make use of very large data sets.
#13 Robotics
Robotics is the practice of designing machines that carry out physical tasks.
For many years, robotics has been used in automobile manufacturing to protect human workers from some of the most dangerous jobs.
In computer science, robotics is about writing programs, algorithms, or software to control physical robots.
These courses often cover the multitude of ways that robotic machines can take and process commands.
So, robotics courses often cover machine code and other basic forms of giving instructions to robotics.
#14 Human-Computer Interaction
Human-computer interaction is the study of ways that humans can physically use machines.
This can range from designing a mouse or keyboard to the advanced applications of Neuralink.
Ultimately, it’s a concept with a huge scope.
In computer science courses that specialize in human-computer interaction, the focus is generally on how to think about ways to interface humans with computers.
It often starts with concrete examples of interfaces that already work (like keyboards and screens), and advanced classes can delve deeper and pursue ways to think about interfaces that could lead to new advancements in the field.
#15 Cyber Security
Cyber security (or cybersecurity) is the study of protecting computer systems from unauthorized access.
Common topics that arise include authorization, encryption, and authentication.
With these ideas, cyber security experts can prevent unwanted access to the system and hide important information from prying eyes.
Cyber security classes often look at common security threats and discuss ways to design around them.
So, a class that focuses on authentication might look at different methods that would enable a computer system to adequately differentiate between benevolent and malevolent users.
#16 Cloud Computing
Cloud computing is overwhelmingly common in the modern world.
In general, the term refers to the use of internet-accessible server systems for the storage and sharing of information.
In simpler terms, cloud computing is all about making powerful computer systems available to many users across the internet.
In cloud computing classes, there are many topics to cover.
Computer scientists have to learn how to build the cloud architecture that runs everything.
They will have to think about how to allocate hardware resources in order to handle the stress often put on the system.
They’ll need to build databases, manage networking, and a lot more.
Cloud computing has crossover topics with a lot of the other subjects you’ve read about today.
#17 Enterprise Architecture
An enterprise is a group or organization with a large number of users.
That’s a bit of a vague description, but there is no magic number that makes something an enterprise as opposed to any other organization.
The main idea is that enterprises need to be able to handle lots of people and increase the scale on demand.
Enterprise architecture classes look into the structure of the computer systems that form an enterprise network.
This can include servers, routers, switches, mainframes, and more.
The important focus is on studying ways to make all of the systems in an enterprise work together and communicate clearly.
#18 Mobile Applications Development
Everyone uses mobile applications on their smartphone, and this is the discipline that looks into the elements of mobile application design and development.
Classes in this subject will look at ways to optimize applications for mobile hardware.
They might also explore various design elements for interfacing with mobile platforms.
User-friendliness, efficiency, and security are all topics that often come up in mobile application development.
