How to Become a Data Scientist?

Data Science has taken every sector and industry in the world by storm. There are countless applications of data science in every field and every sphere in the world. It has progressed to become the most rapidly expanding field of computer science. New breakthroughs are being made and new developments are being made in this field on a daily basis.

Now business and organizations have woken up to the fact that the mounds of electronic data that they generate on a daily basis have lasting value. Having realized this, they are now actively seeking skilled and competent data scientists who can turn this data into a valuable asset. So it has become all the rage among technical professionals ask the question, “How to become a data scientist?” This is the reason why so many individuals opt to take Data Science certification training. 

What are the ways to learn data science?

  • University or college – In order to learn data science, one can take the aid of a 4 year university or college program. One will find that the course study materials are of a high quality and good for engaging the mind. But the classes will follow a fixed schedule and will not be repeated even upon request. One should also remember that among hundreds of students they will not be able to arrange for one-on-one interactions with the teachers.
  • Bootcamp – These have a much lesser duration than university degree programs, typically not exceeding a couple of months. They have a much lower cost than a university degree program but have a very fast pace of teaching. One has to complete very extensive application procedures and follow a very fixed and inflexible schedule of classes.
  • MOOC – MOOCs offer excellent instructors and very well designed course study materials. They don’t charge anything and one only has to pay for a certificate of course completion. But this is where the advantages end. There are thousands of learners who enroll in MOOCs and these large numbers don’t allow for any one-on-one interactions with the instructors.
  • Online data science certification course – This is the recommended way to learn data science. They offer excellent instructors, comprehensive and well-designed course study materials, and up to date course curriculums which cover all the most relevant and pertinent topics related to the field of data science. Another good thing about online data science certification courses is that the learners can get their doubts cleared from the instructors in one-on-one interaction sessions.

What kind of education should a data scientist get?

These are the educational qualifications a data scientist should have -

  1. They should have a degree at the bachelor level in one of the following fields – Physics, Mathematics, Computer Science, IT.
  2. They should get a degree at the masters level in computer science or data science. Most professionals who work in the field of data science have this degree.
  3. Obtain a data science certification online by joining a data science certification training course.
  4. Get some practical experience as data scientists by finding employment in the field of data science.
  5. (Optional) Qualify for and obtain a doctorate degree. A large percentage of professionals working in the data science field possess a doctorate degree.
  6. (Optional) Obtain a doctorate degree by appearing for the doctorate exam and qualifying in it. Many of the professionals who work in the field of data science possess a degree of this kind.

What comes after becoming a data scientist?

As mentioned above, data science is rapidly transforming every sector and industry it enters into. So data scientists will face no difficulties in finding employment in any sector or industry they wish to enter into. They simply have to put their general skills and knowledge into practice to solve domain specific problems in various industries. Some of those industries are -

  • Retail
  • Medicine and Pharma
  • Banking and Finance
  • Construction
  • Transportation
  • Communications, Media, and Entertainment
  • Education
  • Manufacturing and Natural resources
  • Government
  • Energy and Utilities
  • Outsourcing industry


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