In just a few years, jobs in artificial intelligence (AI) may be just as common as marketing or operations roles. As a recent study noted, there’s been a 270% increase in businesses adopting AI practices, creating a demand for pros with AI experience that’s gone through the roof.
This might leave you wondering—why should I pursue a master's degree in computer science when AI jobs are so plentiful? For one, you'll have greater access to the lightning speed of ever-improving AI technology in a research-focused institution of higher education. Universities like Stanford and Carnegie Mellon are leading the way in AI, but colleges across the US are stepping up and enrollment in their programs is on the rise.
Acquiring this cutting-edge knowledge will help you skip over entry-level positions. For example, do you dream of working on self-driving cars? You'll need mechanical engineering courses with a focus on AI offered through a master's program to join and excel in this field.
In this guide, we'll go over what to expect after enrollment, and even after graduation. And you won't have to worry about spending two years on campus if this is not an option for you. Many of the top schools offer online and part-time programs for busy professionals.
Each university's degree requirements may differ, but they all provide a window into how you can shape your degree to serve your unique interests and career goals. We'll cover:
Top universities around the country offer graduate degrees in topics like artificial intelligence, machine learning, and robotics. In many cases, you'll find these concentrations nestled within a Master of Science in Computer Science (MSCS) degree.
Not only do these programs provide you with the tools you'll need to excel in AI, but they'll also leverage your ingenuity for pushing its boundaries. Institutions of higher education host some of the leading AI research centers around the world, and they often draw the attention of top tech firms who are looking to recruit graduates for some of their most coveted positions.
In such a constantly evolving field, an AI program ensures that its students stand at the cutting edge of machine learning knowledge. Many programs include coursework that covers the social and ethical ramifications of AI, as well as the business skills necessary to transform technology into something profitable.
Even if you opt for a flexible online program, embarking on a master's degree is a big decision. Let's take a look at what you need to know before applying.
The majority of online master's degree programs in artificial intelligence take between two and three years to complete. Students often balance their coursework with a busy career and family life, so both a flexible schedule and short commitment are key.
For example, the University of San Diego's artificial intelligence degree can be obtained in 20 consecutive months. Some programs, such as the one at Stevens Institute of Technology, also give applicants the choice of a thesis or non-thesis track.
If you're looking to complete your degree over several years, be aware that some programs have limits on the time you have to finish your degree. For instance, at Johns Hopkins you need to be done with your coursework within five years.
Securing a spot in a prestigious master's program requires a handful of educational requirements and often prerequisite classes. In order to dive into the deeper concepts of AI, the school needs each cohort to be on the same page. Here are the most common requirements you'll find.
Applicants are almost always required to hold a bachelor's degree to apply for an artificial intelligence and machine learning program. The school may require a minimum GPA as well. In some cases, the bachelor's degree must be in subjects such as computer science or engineering.
GRE and GMAT scores are not always required for admission. In some cases, however, they can stand in for a lower GPA from your undergraduate transcript. Additionally, many schools waived their GRE requirements during the COVID-19 pandemic.
Experience with programming languages, data science, and computer systems may give you an advantage in the admissions process. Some schools, such as Carnegie Mellon, require Python, C++, or Java experience. You may need to outline or prove these skills with a resume or letter of recommendation.
Some schools broaden admissions eligibility by allowing applicants to complete prerequisite coursework to secure admission. These courses may include basic algorithms, computation, and other information technology AI courses.
Johns Hopkins, for example, requires minimum requirements such as calculus, linear algebra, statistics, and programming.
Artificial intelligence programs typically split their requirements between core courses and electives. You may be required to take a set number of high-level courses in your area of concentration as well. Overall, credit requirements range between 30 and 45 credits for this master’s.
You'll also need to keep an eye on your grades to meet graduation requirements. Some schools will only allow you to apply one grade below a B- for credit, while others require you to stick with a minimum GPA.
Diving into the types of classes you can take with today's top AI master's programs is one of the most fascinating parts of the school search process. Classes offered in each computer science department will vary, so be sure you find courses that align with your desired concentration. Let's take a look at a few popular areas.
All AI programs start by making sure all students are on the same page. You'll explore topics that lay a foundation for software development, data science and data structures, statistics related to AI, human-computer interaction, and intro to AI research.
Beyond this, you'll explore some of the complex concepts behind machine learning. These may include natural language processing, informatics, deep learning, and knowledge representation.
Programs will typically require you to choose at least six elective courses related to your concentration or specialty. While you will find many interdisciplinary courses, this is where things become very specific. You’ll have the option to explore coursework in healthcare, automation, and cyber security. And can dive more deeply into algorithms and neural networks, as well as business tactics for large corporations and startups.
Choosing a specialization is key in the field of artificial intelligence. Your hands-on experience with specific programs or with unique applications will put you ahead of the game, and help you choose a sub-field. At Steven's, students can specialize in topics like biomedical engineering, software engineering, autonomous robotics, and computer vision.
In short, no, you don't technically need a master's to nab an AI-related job. But top companies like Amazon, Google, and Microsoft do often look to university research teams for new hires. Drexel also points out that, on average, you can make over $20,000 more with a master's in AI than without one.
Like all master's programs, students have the opportunity to make valuable industry connections; develop both hard and soft skills not as easily obtainable on the job; and work with cutting-edge technology in the country's top research centers alongside leading AI experts.
The research and development phase of AI requires a team with a highly diverse set of skills. And once they develop the product, they'll need professionals that can help integrate it all into the company's business goals.
Computer science pros find the AI field from a wide range of pathways, honing in on both the skills they've already developed and those they're passionate to explore in school.
Where can you work once you have your master's? Here are just a handful of popular options.
The general term researcher doesn't even scratch the surface of all the complex roles in research and development available in AI. Companies need both quantitative and qualitative researchers to come up with the tech and work to apply it to daily life. You’ll be able to find research positions that cover highly specific industries, AI technology, and engineering backgrounds.
Software engineers often act as the bridge between the research and product teams. In some cases, they'll build APIs that can use the newly developed AI technology so it's user-friendly. In other roles, an engineer may lead a larger team of data scientists, architects, and business analysts.
Predictive and descriptive data drives the AI research process. Data analysts pull and translate crucial data to ensure the product is working smoothly from its early development through to the final, customer-ready product.
Business developers ensure that the development of new tech will have a measurable and significant impact on business. They also may act as the bridge between private and public investors, the operations team, and all creative elements that go into making the AI tech marketable for the industry.
Artificial intelligence is steadily expanding its reach across industries worldwide. Even today, you have your pick of industries in which to employ your tech skills, from business management to software development and more.
The US government uses AI technology in everything from national security to helping citizens connect with the correct government services. You can earn your master’s to explore roles in research for military applications, cyber security, and communications, just to name a few.
AI helps support a medical expert's decision-making process and diagnosis, and identifies possible treatments. With access to massive amounts of data, this technology can present options to a doctor far more quickly than before. Healthcare and medical AI can assist with image analysis, crisis scenarios, and drug discovery.
As the call for cyber security experts increases—both in government and the private sector—industries of all kinds need cyber security professionals to safeguard their systems from hackers. AI can detect threats, proactively shut down affected networks, and find patterns in attacks otherwise missed by people.
AI has been making its way into our everyday lives for some time now, from home security to online shopping. Countless positions, from UX design to backend engineering, require in-depth training to both sell the product and translate challenges to the rest of the company.
Today's most well-known names in business need new AI specialists every day. Everyone from Google and Apple to IBM and Phillips hires positions across the globe. And if you secure one of the top research positions at these companies, the salaries are impressive.
According to AI Paygrades, some research scientists at Google make a base salary of $200K with bonuses and equity pushing their annual pay into the seven-figure range. Data scientists at Pinterest can make up to $550,000. Even lower-level AI scientists at Amazon can make over $150K after bonuses.
When you embark on a career path like AI, you want to have as many tools at your disposal as possible. The field will only continue to change. Developing a relationship with a top research university could be your ticket to securing one of these exciting, groundbreaking, and well-paying AI roles.
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