5 Reasons why the Majority of the Data Science Positions are vacant today

By POOJA BISHT |Email | May 29, 2019 | 9219 Views

The reason why many of the data scientists position remains vacant today in spite of a number of candidates applying for the position is the "lack of skills". According to a study conducted by Great Learning 97,000 analytics and data science positions remain vacant in India due to the dearth of the skills. But the bigger question is- "How can the dearth of skills exist when the number of online sources existing today is far more than ever existed before? When the number of platforms providing certifications has increased to the double and the vast amount of information is available over the internet about Data Science. One of the reasons that I account for this is the "Mistakes"- Mistakes that a Data Scientist aspirant commits while preparing for a Data Scientist position, which must be addressed soon and obviously quickly.  The article highlights the major mistake that a Data Scientist commits at the beginning of his career which lead to his downfall.

  • Trying to Learn quickly and applying shortcuts
The vast syllabus that a Data Scientist needs to cover often makes him stressed and fatigued while learning and building concepts. Also, as the competition is rising and every other platform providing the best methods for learning concepts, aspirants have become quite lazy and have started to apply shortcuts in the learning process. It is true that you should learn some of the concepts through shortcuts which will save your time but it is completely wrong that you are applying shortcuts to every other concept and trying to learn the overview only without going into depth. These shortcuts may complete your syllabus in lesser time and can make you learn many concepts much before than you had expected but the end result will be like- "you have learned many concepts but do not possess in-depth knowledge about the subject". This will obviously not work for you in the long learn.

  • You rarely apply your theoretical concepts
Learning theory and remembering it is one thing, but if you do not apply that theory in the day to day problems than there is no point in the Learning. The role of a Data Scientist demands analytical thinking in addition to theoretical knowledge. Yes, you should have an excellent command to the theoretical part, but implementing those concepts in the worldly challenges is the most important thing that you should do. As a Data Scientist, you will be required to solve major business problems and will be working in exploring the huge and complex datasets. This requires strong analytical skills. All your statistical knowledge will be tested when you will apply all your statistical knowledge in statistical computing. The only theoretical knowledge will not make you a Data Scientist.

  • You do not work on your Data Visualization skills
Data Visualization is an important skill of a Data Scientist. A Data Scientist requires Data Visualization in exploring the complex datasets which are hard to analyze otherwise. Many of the beginners today focus on all other qualities like using Python, R, and Mathematics but do not put emphasis on Data Visualization techniques. Some are not even aware of the knowledge to use the latest and commonly used Data Visualization software, which is a lapse on the aspirant part. Even if some are aware of the software, they o not possess the skill to visualize data using statistical computing.

  • Your Communication skills are not yet Excellent
 Communication skills are one of the most essential skills of a Data Scientist. A Data Scientist needs to communicate a lot to the stakeholders and to the business managers. He needs to attend the meetings presenting his insights and his methods for solving business problems. There is a wide discussion on the different online platforms about the need of the various technical skills, which I will not condemn, But, Communication skills are the skills that need to be focused as well.  As a Data Scientist, you must focus on your communication skills apart from working on your technical skills. 

  • You are not learning from the Good sources
These days the Internet is filled with lots and lots of data science platforms telling beginners a lot about the various concepts in data science, presenting videos and providing online courses. There are millions of platform which have risen on Data Science today. But, if I will seriously tell you one thing about these online platforms, it is that, that many of the online platforms are fake, not providing the quality information that you should be learning in Data Science. As a beginner and a learner, you should discuss among your community about the different trusted online platforms. GitHub, Kaggle, Udacity, Edureka, are some of the platforms that I have found useful for Data Science. You should explore some more.

Source: HOB