With technology growing at a rapid pace, staying abreast of the data science landscape is becoming increasingly difficult for aspirants. In this setting, part-time data science courses are becoming ineffectual for learners when it comes to mastering different techniques. Additionally, short-term courses quickly teach various data science concepts, but learners fail to retain them. This is mainly because students who enrol in these courses do not get enough time to do a deep dive on what they are studying.
In most cases, after learning new techniques, they stick to simple quizzes that edtech platforms ask them to solve. These questions are often easy and are devised keeping in mind the diversity of students taking the course. Thus, it fails to test the knowledge of learners.
To overcome such incompetencies in short or part-time courses, various institutions are offering full-time courses to encourage students to go in-depth into different data science techniques. While full-time courses were being offered for quite some time now, today, they have become more relevant due to the following reasons:-
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Hiring Freeze Due To COVID-19
Due to the proliferation of COVID-19 and the subsequent lockdown of cities, the stock market has crashed, thereby demonstrating that coronavirus has negatively impacted businesses across the world. This has led to layoffs by companies as almost all firms are expecting slower business growth. This will limit organizations from hiring not only data scientists, but also other IT professionals.
Nevertheless, it provides you with an opportunity to use the time by enrolling in full-time data science courses and learn advanced skills. And when companies resume their hiring processes in a few months from now, you will be able to differentiate yourself from other applicants since you went through rigorous learning and training. Therefore, this is the right time to take advantages of the current situation and invest your time and money in building strong foundations, as well as catch up with the data science space post-lockdown.
Building A Strong Foundation
The idea behind pursuing certifications of longer durations is to have enough time to learn and practice foundational as well as advanced techniques. While engineering graduates often gain the knowledge of calculus, linear algebra, and probability, they do not learn inferential statistics. However, statistics is essential to understand how machine learning models work. This enables aspirants to not just randomly implement fancy algorithms, but to assimilate the business problems and apply the most effective methods.
Undoubtedly, statistics are also included in short-term courses, but learners usually do not spend time implementing it in different use cases due to paucity of time. On the other hand, if you are in a full-time course, you will have enough time to practice and understand it well. “Tools in data science will keep changing every few years, but the core fundamentals of statistics and mathematics remain the same,” said Srinivas Atreya, Chief Data Scientist at RoundSqr.
Catching Up With The Data Science Space
Unlike others who have learned data science as the landscape grew, they had fewer techniques to learn. Today, aspirants have a lot of things to learn at once. This makes them susceptible to failing to gain proficiency in any methods. Learners who take short-term courses become a jack of all trades and master of none. Therefore, they struggle to land a job even though the demand for data scientists has been on the rise.
Consequently, one needs time to catch up with the data science space before getting their hands on new developments. This can only be possible if aspirants refrain from jumping from one technique to another in a short period. For learners to keep themselves updated, they should first be familiar with what has happened in the landscape over the last few years. This will bring clarity when they come across new developments in the space, thereby, simplifying the process of learning the latest advancements.
Also read: It takes years to become a data scientist
Importance Of Research
According to Analytics India Magazine, the data science landscape is projected to double its size by 2025, from 3.03 billion in 2019. This is because organizations will further integrate data science in every aspect of their businesses. For this, researchers need to come up with new techniques that can solve business problems effectively.
Although data science has seen unprecedented developments, there are still several challenges that need to be dealt with. This is where researchers play an essential role in the community. They are the driving force behind the rise of data science. Thus, aspirants can direct their career towards research if they learn from full-time courses. Learners should learn to think about implementing data science techniques, thereby, driving the space through research.
Full-time program’s certifications bring more value than part-time course certificates since institutes offer the former. Biswajit Biswas, a chief data scientist at Tata Elxsi, said to Analytics India Magazine that the company looks at certifications, especially paid ones, as it demonstrates their commitment to data science. However, certification should not be the prime objective of aspirants as, if they do not have the right skills, certifications would be of no help.