It’s been established by many experts that data scientist is currently the most in-demand profession in the industry. As the industry is maturing with new-age technologies and solutions, the demand for data science skills will be on the rise. Businesses, despite their sizes, are looking at leveraging data to drive efficiencies, and therefore, companies are continually looking to hire people who can collect, read and analyse data. In fact, according to the report, in 2020, the job requirements for data science and analytics is expected to increase by 364,000 openings to 2,720,000.
These numbers show that there is a massive requirement of data scientists; however, the supply is not matching the demands of the industry. This increasing demand is a lot due to the advent of big data, and the way data is being generated and consumed nowadays by companies. And, for businesses to collate and analyse this huge volume of data, they require data science professionals with specialised skills. Alongside, at this competitive edge, companies are continuously looking to use predictive analytics to improve their customer experience, and data scientists can help businesses understand their data and use it to predict their customer behaviour.
However, there are some specific reasons that have emerged due to which there is a constant shortage of data science professionals for your businesses.
More Companies Working With Data
One main reason for the shortage of data scientists in the industry is because all companies, nowadays, are leveraging the value of big data in order to make an informed business decision. The rising demand for analytics in the companies has given rise to an exponential shortage of data scientists. Although several individuals are pursuing data science as a career, there aren’t merely enough skilled labour to fill in the jobs. The number of STEM graduates every year isn’t in sync with the market requirement of data scientists in the industry.
Small companies can do with one software engineer and an analyst who can analyse data to maintain the businesses, however, for a large corporation, a team of data experts will not only include programmers, developers, but also data visualisation specialists, as well as project coordinators. This shortage of data scientists, in turn, creates pressure on existing data scientists of the company with added projects and more business problems to solve. And therefore, it is crucial for data science enthusiasts to join in data science boot camps, build in projects as well as find other resources to gain the necessary knowledge to fill in this gap of skill shortage.
Upskilling Required At A Rapid Pace
Considering technology is rapidly evolving, the need for an organisation to hire a data scientist is also evolving. With the increasing demand for emerging technologies, businesses are looking for data scientists to be skilled with new-age technologies rather than older programming languages like R, Ada, C, Haskell etc. Currently, companies are looking for newer skills like data visualisation, machine learning, to name a few, in order to make a more informed decision in this competitive landscape.
Data scientists should now know advanced statistical tools and quantitative methods along with ways of integrating large data sets. So, as the skills are getting redundant and the data science skills gap increases, which, in turn, creates a shortage in the market. Companies are also hiring based on projects and portfolios rather than degrees, and therefore data scientists should create personal projects in order to enhance their portfolio. Nowadays, data scientists also require to be knowledgeable about businesses and should have great communication skills for sharing ideas and approaches. Alongside, the oncoming recession due to this pandemic outbreak will bring a wave of automation for businesses, and therefore if data scientists don’t upskill and re-skill themselves, there will be a huge shortage of skilled data scientists in the industry. In fact, in a recent report, it has been revealed that 64% of Indian professionals believe that upskilling is required to survive in a tough job market, which in turn will also solve the scarcity issue.
MOOCs & College Courses Aren’t Self Sufficient
Data science is a vast field; learning from online courses isn’t sufficient for data scientists to land on the desired job. Online courses can help in learning the mechanics of the field but will not help in meaningful interpretation which is vital in solving complex business problems. With an ample amount of online courses available in the market, it also gets difficult for data scientists to pick the right one required for their job profile. Despite being the popular choice among newcomers, online courses mostly provide theoretical knowledge; however, drastically lack in providing in-depth practical hands-on experience in the field.
Also with so many of them emerging to provide online courses for data scientists, a lot of them even work without proper curriculum and license, and thus, do not provide quality of education that is required for landing on a data science job. In fact, in an interview, Srinivas Atreya, Chief Data Scientist at RoundSqr has stated his concerns on the current approach of students who are willing to quickly learn data science without the fundamental knowledge. He said that it is impossible to grasp the entire knowledge of data science in a few months that various online course providers claim. Instead, job seekers must stay patient and learn over the years.
With colleges and universities recently started to provide full-time data science courses since the last few years, and therefore the subject hasn’t been a mainstream career choice for a long time. Alongside, engineering colleges usually cover descriptive statistics, however, in order to work with machine learning models, data scientists must learn inferential statistics, which hasn’t been part of many university courses. Experts also believe that only half of the enrolled learners manage to finish an online course, which could be attributed to their lack of guidance.
Lack of Guidance
Another primary reason for the shortage of data scientists in the industry is the lack of guidance among them. Data science is a vast subject, and learners trying to engulf everything makes them only a jack of all trades and master of none, which isn’t something business are currently looking for. Many companies have a specific requirement related to data, which requires candidates to have in-depth knowledge about various aspects of data science. And therefore, even with the certifications, they fail to crack the interview. In an interview with a data scientist, influencer and blogger Rahul Agrawal stated that “newcomers shouldn’t quickly get into implementing fancy deep learning and other machine learning techniques. However, they forget that the building block of all these is still logistic regression. Therefore, aspirants should build strong fundamentals before moving to advanced techniques.” Newcomers end up learning a wider range of techniques but never take time to understand it thoroughly, and therefore they fail to impress the recruiters in job interviews.