MOOCs or Massive Open Online Courses for data science is one of the most preferred options to upskill and learn new technologies in this rapidly evolving industry. Not to confuse with online courses, MOOCs are more context-driven, and its dynamic building up of context around content makes it unique. Usually aimed at micro-learning, they are useful when getting a deeper understanding of the subject is not the main focus. 

Mostly available for free, many companies such as Google and Microsoft have MOOCs that promise to provide high-quality training courses. There are academies such as Udemy, Coursera that offer data science MOOCs. With the increasing number of MOOCs now available, the number of people who have utilised it has grown dramatically over the years.

While it gives the liberty to learn at their own pace, there is also a widespread view that MOOCs are nothing more than superficial content, and cannot help you in establishing a career in data science. 

Let us dig in deeper in this context with this article, where we try to find out if MOOCs can help make a career in data science or are they merely superficial. 

MOOCs For Establishing Data Science Career

There are many data science professionals today who claim to have achieved what they have by entirely relying on MOOCs. 

Data scientist, influencer and blogger, Rahul Agrawal, from Walmart Labs in an interview with Analytics India Magazine had said that he had relied entirely on MOOCs to learn and master data science techniques. He emphasised on the fact that it allows for learning without being in the classroom, and that it is still an essential part of his learning strategy. With more than 30 certifications, he still looks for courses that he could learn that might prove helpful in his work. 

Echoing similar views, Saurabh Jha, who is the Director of data science at Dell, entirely relied on MOOC for making a career in data science. With the initial struggle of making out what is best and what is not, he finally cracked up the concept of making the most out of MOOCs. He vouches for the course by Andrew NG as it is simple and easy to understand. 

Similarly, Rajiv Hiremagalur started learning from the courses provided by MOOCs when he first learnt R programming in 2014 and then moved on to Python and so on. To stay abreast of the new advancements, he used to create a list of techniques to master. This helped him in learning one skill at a time while avoiding burnouts by learning everything together. 

Considering these opinions, one of the significant advantages of MOOCs, unlike full-time or part-time data science courses, is that it keeps up with the ever-changing data science landscape. It comes handy when adding one skill at a time. 

On The Flip Side

Many recruiters believe that MOOCs alone cannot help in landing a data science job. Ramasubramanian Sundararajan, Head AI Lab at Cartesian Consulting, believes that MOOCs are suitable only for continuing education and upskilling for those who have established themselves in the industry. For freshers or those trying to set out a foot in the industry might find classroom coaching as a better alternative than MOOCs. 

Babak Beheshti, IEEE Member, professor and dean at College of Engineering and Computing Sciences, New York Institute of Technology, shared that while MOOCs have proved to be an innovative paradigm in education and providing an alternative for specialisation while gaining micro-credentials, relying solely on them may not help in establishing a career in data science. It is best suited for those looking for affordable education. 

Going through various online forums, many recruiters believe that MOOCs may not have much corporate value. While it is good to add another certification in your profile, getting a specific career boost through this is less significant than those by executive premium courses from the likes of IITs or IIMs. 

Another popular thought is that MOOCs are a good option for those who want to upskill or switch careers but not an ideal way for students to go about. MOOCs are usually faced-paced and short and may not give an in-depth understanding of the subject as a full-time course does. 

“Someone who has learned through online courses rigorously tries to apply random techniques. But someone who has thought deeply while pursuing M.Tech or PhD knows how to look at problems in a more detailed way instead of applying whatever is available readily at hand; It’s basically using the tool versus knowing exactly how the tool works,” said  Manish Gupta, Principal Applied Scientist at Microsoft, in an interview with Analytics India Magazine. 

Data science cannot be learnt in a few months; it’s a long process. Thus, full-time courses are the best way to go in-depth and differentiate from others. Nevertheless, Gupta said that working professionals are left with fewer options and should leverage online courses to learn in a guided manner instead of learning everything by themselves.

Given the fact that MOOCs are too easy to accomplish makes it not as likely an option as full-time courses among recruiters. It may not provide a deeper understanding of data analysis techniques. For instance, it cannot make you strong with math skills without having a strong background already. So, while it may help professionals to upskill, they may not be enough to get a data science job. 

Wrapping Up

MOOCs are an excellent way to keep the skills relevant and fresh as it may help in boosting your career but depending solely on it may sometimes work in your favour and at other times not. Especially if there is no thorough understanding of the skills acquired, investing time in MOOCs may go wasted entirely. It also depends on where you have taken it from, the credibility of it and how much knowledge you have been able to acquire from it. 

Data science is a practice-driven profession in many ways, and working on projects and internships are instead looked upon as a credible resource of your understanding in the subject than just adding MOOCs certifications. Recruiters might consider looking at the real-time data quality issues that you might have encountered and how you dealt with them, communication, data storytelling skills, and more. 

Data science is a multifaceted subject and having a thorough understanding of math, statistics and programming are highly critical. MOOCs may be of great value if you already have some experience and degree in data science, looking to upskill in a few areas of data science and if you do not have that kind of money to spend on regular full-time courses.