Online Degree In AI: What Does It Mean To Employers

The developments in the field of data science and artificial intelligence are evolving at a rapid pace, and to keep up with the technological developments, professionals often resort to upskilling. But there are so many questions that come on the way — full time or part time, online or offline, short duration or long duration, private or government institutes, among several others. While we have earlier covered an article on how professionals like to prefer full-time courses over part-time, it is often debated as to what companies prefer. There was a very interesting Twitter conversation in December last year, where a project manager from Google tweeted about someone who would not want to hire a person who is online educated in machine learning. It led to a thread of heated conversation on whether the statement made sense or not. While many believed that a strong educational background such as a degree in computer science, engineering or statistics is a must, others were of an opinion that these skills were acquired on the basis of practical engagements and not just attaining a degree.

What Do Companies Prefer — An Academic Or Online Degree?

Many organisations that are looking for candidates in artificial intelligence roles at a senior level typically look for candidates that have a strong academic background in areas such as computer science or engineering from reputed universities such as MIT, IITs, Stanford and others. Degrees from these institutes are highly regarded by companies and startups, with many believing that one could probably not even pass the resume screening if the candidate doesn’t have an MS or PhD.

Specific Academic Requirements:

For instance, while screening many job openings in Indian companies, there were a lot of companies that required qualifications such as MS, PhD in computer science, computer engineering, electrical engineering and related areas. Whereas there were a few other job openings that mentioned a degree from top tier institute with a solid foundation in AI methodologies such as machine learning, deep learning, NLP, neural networks, NLG, NLU etc. The companies that stress on a masters degree in computer science or mathematics are basically looking for candidates who have a strong grasp of numbers, statistics, mathematical concepts, and more. Many other companies even demand just bachelors degree with some working or practical experience in building AI-based solutions.

"Companies will always value an AI degree from a university more, as students doing this would get an opportunity to go in-depth in AI. There is always a difference between the ability to just use AI and actually understanding it. Hence rather than an online course or a graduate degree in analytics, AI aspirants must get a degree from a reputed university," says Parnil Mhatre, Founder of Quantum Four Analytics.

Open-Ended Requirements:

On the other hand, there are several other companies in India with AI-related job openings that do not specify education as a key requirement but in-depth knowledge and understanding of core skills in AI related concepts such as NLP, deep learning, Jave, machine learning, JavaScript, Python and others. There are a lot of companies that are looking for a deep understanding of software development, good programming skills, understanding of machine learning frameworks such as AutoML, Sagemaker, Tensorflow, Caffe, NoSQL, Hadoop, Spark and other skills, suggesting that there are companies who are looking for skill-based hiring rather than just degrees. These skills can be acquired as a part of online training that focuses on hands-on experience. These comparisons suggest that it is not the degree that makes a difference while recruiting. AIM got in touch with Srinidhi Rao, Senior Partner at TheMathCompany, who says, “What matters more than how a candidate has acquired his/her subject understanding and skills from, is how well he/she understands concepts, and how he/she can use those concepts to solve business problems. As long as the candidate achieves this goal, whether he/she has taken an online course, has obtained a university degree or learned the skills on the job, is irrelevant.”

Online Courses Are Offering Good Resources

The institutes that are offering online courses is at an all-time high. MOOCs have become a trend these days and many renowned researchers such as Andrew Ng are opening their own AI institutes offering online courses for AI enthusiasts. In an earlier article, we had covered why business leaders should take this course, as it not only lays a better picture of the concepts but helps them make better judgements for better outcomes. The Deeplearning.ai “AI for Everyone” project by Ng is one of the best online resources for deep learning enthusiasts. There are courses from top tier institutions such as Stanford, MIT, Columbia and others that are available online. There are several other courses offered by global platforms such as Coursera and Udacity aimed at strengthening the ML community. There are several institutes in India such as UpGrad, Great Learning, TalentSprint, Jigsaw Academy, Manipal ProLearn, and others that are offering online courses with industry-relevant training to get into the AI industry. We had done ranking courses and training programmes in AI here.

Who Wins?

There are many professionals in the AI industry who have proven that it is not just the degree from universities that make them fit the AI jobs bill. For instance, the founder of fast.ai and former Kaggle President Jeremy Howard had shared that he never had a formal technical education in the area and didn’t actually go to any lectures or tutorials. Another AI researcher David Ha while sharing his personal experience says that he was online-educated in deep learning. The bottom line is that there is no online or offline, formal or informal education, the best researchers are mostly self-taught, whether they have a degree from an Ivy League college or no. There is no denying that formal education in institutes is the best, but online courses can also provide the same knowledge required to practically solve problems. What ultimately matters is the skills that a candidate pose to be fit in the tech-driven AI industry and bring newer innovations.