Although jobs in data science are plenty, getting a job has been a challenging aspect for aspirants. Since the data science landscape is vast, ambitious data scientists try to obtain several certifications and carry out various projects to make an impact on recruiters with their achievements.
Undoubtedly, taking advantage of all the platforms and tools is essential to differentiate oneself, but an aspiring data scientist should also prioritise and embrace the best approach. Today, however, aspirants haphazardly adopt numerous strategies without a clear vision of the desired target, which only leads to burnout.
One pressing issue that many aspirants fail to clarify is to have more certifications or work on several projects. Consequently, most of them indulge in taking certifications and rely on the projects that the data science programs include in their courses. This has enabled aspirants to apply for jobs, but recruiters fail to find promising data scientists, who can solve business challenges.
Rise In Enrollment For Certification
As per a Coursera report, the e-learning platform witnessed a considerable rise in enrollments for AI and data science courses. Recruiters find it challenging to hire skilled data scientists, which is evident by the fact that 85% of AI projects fail due to the absence of right candidates. Certifications do help in getting the job interview but do not necessarily guarantee jobs.
As the tweet mentions, although almost all applicants have learned through different programmes, the skill gap still exists. Massive Open Online Courses (MOOCs) design data science programs in a way that makes it easy for learners to complete the courses. Besides, they include projects that are straightforward or less tricky, and consequently, it doesn’t make one proficient in the competitive data science landscape. And not just MOOCs, even the university programs do not help either, as it fails to stay abreast of the changing data science landscape.
“The problem with aspiring data scientists is that they use LinkedIn like Facebook,” said Parul Pandey, a data science evangelist at H2O.ai. “People post about their certification on LinkedIn instead of their real work on problems and projects,” she added. She further stressed the importance of communicating about their projects on various platforms to get notices for jobs.
How Important Are Projects
“Not all organisations deal with the same problems. Thus, it becomes difficult for aspirants to focus on particular projects. And, therefore, applicants should participate in hackathons and compete on Kaggle to showcase their expertise,” said Santosh Rai, head data scientist and AI architect at ProVise Consulting. However, aspirants can still diversify their portfolio by doing a wide range of projects.
Rai also said that certificates are only required to get through the first phase of job search by receiving a job interview, but projects are most essential in the interview. However, Rai cautioned that neither certificates nor projects would help in getting a job unless one knows how to convert business challenges into data science problems. Nevertheless, projects demonstrate how proficient aspirants are in solving real-world challenges which certifications can never exhibit.
Working on strenuous projects is where aspirants should focus rather than putting their efforts in doing certifications to get a job. However, as per a Sharath Kumar, the head data scientist at IBM, today along with projects, communication skills have become a differentiating factor for applicants as data scientists in organisations often have to explain their outcome to non-technical experts.
Consequently, one should put his/her efforts on projects and communicate using platforms like LinkedIn, Medium, and more, instead of certificates. However, certifications have their advantages as recruiters look for their educational background to assimilate aspirants capabilities.