Data science courses have been keeping many industry professionals and enthusiasts busy amid the lockdown. While data scientists are looking to shield themselves against an oncoming recession by actively upskilling, others are embracing it to make a career shift for the opportunities the field provides.
Irrespective of the reasons for this mass shift to online learning, edtech firms are responding to this demand by opening up access to some of the premium course materials, launching additional data science courses, and even making some of them available for free.
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Although this trend aligns well with the need to continuously learn to enhance career prospects and stay relevant in these uncertain times, it may also indicate an overdependence on e-learning. Distance education, for all its benefits, has its limitations, especially in a field like data science, where practical implementation is paramount. Upskilling should be a part of any data scientist’s career path, but are they relying too much on online courses?
Online courses can be a contributing factor, but they cannot build a robust data science portfolio by itself. That is not to say that they are unhelpful, but depending solely on e-learning platforms may not be prudent. Let us attempt to understand why:
Difficult To Select Right Courses & Classes
Some online courses on well-known platforms may be offering up-to-date content by highly experienced faculty to teach complex concepts, but choosing the right one for your current skill sets and objectives amid an avalanche of free classes can be challenging.
There are various online courses and programs available for almost everything across the data science landscape. Moreover, data science covers a wide range of disciplines and is often loosely used by many platforms to convey a few things under this umbrella term. This demands that learners spend a lot of time searching for courses that are suitable for them.
Additionally, they have to factor in programs that provide the expected depth in coverage as well as hands-on case study-based learning, with adequate individual attention by instructors. Which is a good segway into the next point.
Limited Practical Experience
Online courses may equip data scientists with the skills needed to understand its applications in the real-world, but merely having this knowledge without implementing it is not worth much. While these courses may pack a lot of relevant information, learners should not lose sight of the fact that applying those concepts and getting hands-on experience should be the ultimate goal.
Despite its benefits, there is no dodging the fact that online learning can be difficult if it is meant for subjects that involve practice, as in the case of data science. Very few courses go beyond theoretical content to provide simulators that allow learners to practice their skills.
One way of overcoming this barrier is by implementing learnings on real-time datasets from various domains through internships. Working on industry-based projects on platforms like Kaggle can also provide ample opportunities for practical experience while making data science portfolios more competitive. Employers know that such an exposure will force data scientists to think deeply about a problem, which may be difficult to do with e-learning.
Lack Of Direct Interaction
While some courses may provide live and interactive classes with the use of webinars and video chats, most – especially those available for free – mostly take recourse to pre-recorded videos and study material. Studying in silos might boost some learners’ productivity, but data science is inherently collaborative in nature and relying on self-study alone is not desirable.
Most programs enable discussions between learners as well as instructors, but the scope of these interactions are quite narrow. This may not be the case with full-time data science courses. And without asking and answering questions, participating in discussions, and clearing doubts, learners will miss out on a critical experience that is valuable in data science.
The role that collaboration and teamwork plays in the field of data science is often underplayed. But the truth is that data scientists can learn and develop the right way only together – boosting interest and stoking passion for pursuing a career in the field by competing or partnering with each other.
Questions Of Credibility
As online courses become ubiquitous, questions around the credibility of some of these programs are causing confusion among learners. While not the right approach, some data scientists enrol for courses with the prime objective of updating their resume to seem more qualified than they actually may be.
There are plenty of scams that offer certifications for a fee, but may not be licensed. The prevalence of such scams in the online learning market has led companies to especially keep track of accreditations during job interviews.
To ensure that you do not sign up for programs from institutes that may not be accredited, conduct thorough research on its background, and if possible, connect with other learners who may have applied for the course.
Challenging To Condense Data Science In A Few Courses
Owing to the immensity of the field, relying on online learning without adequate support can be challenging for learners. Data science is a mix of various disciplines, including statistics and computer science, and mastering each – or even several – using online tools may not be enough despite the fact that many courses claim to fill the skill gap that the industry has been facing.
Online learning can be helpful in picking up specific skills, but putting together a program that condenses complex competencies may be difficult to accomplish. Data science is an ever-changing field that necessitates continuous learning – while much of it can be achieved with the help of digital courses, much more needs to be done for one to be proficient at it.
What is more, since it requires a large amount of domain knowledge (given the vastness of the field), learners studying in silos have to prepare for a tough road ahead. However, some courses provide mentorship as well which can help overcome some of these challenges.
The drawbacks of online learning in a field like data science may be many, but some data scientists firmly believe that if leveraged wisely, online courses can be helpful in acquiring the right set of skills to be successful.It may not be a substitute for a regular degree for most jobs in India, but ultimately, what is important is what learners take from it. Treating it as just one of the channels to learn more might be a better approach to take, against collecting online certificates to make resumes more attractive. The projects that one participates in to implement those skills will be more helpful.