July 1, 2023 Author: Matthew Renze

How do I choose a degree in AI?

In the last article in this series on Getting Started with AI, we discussed the top careers in AI.

In this article, we will discuss the top degrees in AI from accredited universities.


Some AI career paths do not require a degree at all.

However, many require a 4-year degree in an AI-related field.

Some require either a Master’s degree or a Ph.D. in an AI-related field.

So, to help those of you who want to get a degree in AI, here are the top degree programs to consider.

Computer Science

A Computer Science (CS) degree is the most common route to a career in AI. CS is a specialized degree in mathematics focusing on the Theory of Computation. It has more to do with the mathematics of computation than with computer hardware, and it’s not really a “science” in the empirical sense. In CS, you will study discrete math, logic, data structures, algorithms, programming languages, etc.

The famous computer scientist Edsger Dijkstra once said, “computers are about as important to computer science as telescopes are to astronomy”. Computers are just a tool that we use in CS to get at what we’re really interested in — the mathematical theory of computation. Unfortunately, many first-year CS students don’t know this and discover the hard way when they reach courses like Theory of Computation.

Once you understand the foundations of computer science, you can go much deeper into other branches of computer science. For example, operating systems, databases, networks, cybersecurity, software engineering, machine learning, and artificial intelligence. A CS degree opens many doors that you can’t really open by studying on your own or by learning a programming language during your free time.

Data Science

Unlike Computer Science, Data Science (DS) has a lot to do with “data” and is a “science” in the empirical sense. In a DS program, you will learn how to collect, clean, transform, and organize data. You will also learn how to analyze, visualize, and make predictions with data. In more advanced courses, you will learn how to work with Big Data, train machine learning models, and perform statistical analysis and modeling.

Some people equate a DS program to “Statistics with computers and more data”. However, it’s much more than that. The practices of data science are so applicable to all other branches of science that some data scientists have said that “all science is now data science“.

With a degree in DS, you can pursue a career as a data analyst, data engineer, machine learning specialist, or data scientist. In addition, a DS degree gives you a career path to working with AI through machine learning and the various AI algorithms that you will learn through either a CS or a DS degree.

Machine Learning

Machine Learning (ML) is a subfield of AI based on math and statistics. It involves using ML training algorithms to learn statistical models of data that can be used to make decisions, predict outcomes, and automate processes. This degree has a lot of overlap with the CS and DS curricula. However, it’s much more focused on ML than either a CS or DS degree — which can be either good or bad depending on your goals.

In ML, you will learn foundational concepts of math, statistics, and algorithms that are similar to CS and DS. However, you will focus more of your time on ML concepts and skills like supervised learning, unsupervised learning, deep learning, reinforcement learning. natural language processing, computer vision, and probabilistic models. This is a relatively new degree but growing quickly every year.

With a degree in ML, you will almost certainly be working towards a career as a machine learning specialist, an ML engineer, or some other role that involves training, optimizing, verifying, and deploying machine learning models. This can be a great way to get into AI; however, it might also limit your opportunities if the field of AI moves away from ML to something else in the future.

Robotics Engineering

Robotics is a field that studies the design, control, and analysis of robotic systems and their interactions with the environment. It covers topics such as robot kinematics, dynamics, control, and perception. It also covers industry-specific applications of robotics, such as manufacturing, healthcare, and transportation. Other focus areas include human-robot interaction, computer vision, haptics, etc.

Robotics is both an engineering discipline and a scientific discipline. So, if you are unable to get into a dedicated robotics program, you can gain experience with robotics through a degree in computer engineering, electrical engineering, mechanical engineering, or computer science. As AI becomes more powerful each year, we will progressively embed more AI into robotic systems and cyber-physical systems.

A degree in robotics can open up many career opportunities with AI that may not be available via the previous options we discussed. For example, careers like: robotics engineer, robotics programmer, robotics technician, or robotics research scientist. In addition, it can also open up a wider array of career options beyond traditional AI roles like automation engineer, control systems engineer, and mechatronics engineer.

Computational Neuroscience

Computational Neuroscience (CNS) is an interdisciplinary field that combines principles of Neuroscience and Computer Science to understand the function and structure of the brain. It uses mathematical models, computer simulations, and analytic techniques to study brains and their emergent properties like perception, consciousness, and intelligence. CNS is like building AI from the bottom up.

CNS is a relatively new field that is typically studied at the graduate level. So, you will typically need a Bachelor’s or Master’s degree in neuroscience, computer science, mathematics, engineering, or a related field to get into a CNS program. However, some universities offer a CNS minor or emphasis as part of other undergraduate degrees like computer science, electrical engineering, or physics.

A degree in CNS can open up a wide variety of career paths in AI that may not be available through the previous degrees we discussed. These careers include computational neuroscientist, brain-computer interface (BCI) engineer, and neuroinformatics analyst. Also, it can open new opportunities outside of traditional AI, including becoming a cognitive scientist, neuroscience researcher, and biomedical engineer.

Artificial Intelligence

The final degree to consider is a degree in Artificial Intelligence (AI). I saved this degree for last because it is a very new degree. In addition, it might sound like the obvious choice for someone interested in starting a career in AI. However, there are various reasons why this might not be the best choice for you. So, you need to understand the pros/cons of a degree in AI before choosing it as a path to a career in AI.

As of 2023, there are only a handful of accredited universities in the US that offer a Master’s in AI. There are currently no universities that I’m aware of that offer a Ph.D. in AI. As such, a Master’s in AI is considered a terminal degree. There is no direct path from a Master’s in AI to a Ph.D. in AI or related fields like CS, DS, etc. In addition, a Master’s in AI is focused on the practical aspects of AI rather than theoretical.

A Master’s degree in AI will provide you with the skills you need for various careers in AI. This includes careers that use AI algorithms and ML models to solve new problems, like an AI Developer, ML Specialist, and AI Consultant. However, this is likely not the best path if you’re interested in getting into AI research. A Master’s in CS or DS is a much safer route to a Ph.D. if you’re interested in doing research in AI.


To learn more, please read the next article in this series: Learning Basic AI Skills.

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