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Quantitative & Numerical Competency Makes Or Breaks A Good Data Scientist: Joydeep Dam, Bridgei2i

Quantitative & Numerical Competency Makes Or Breaks A Good Data Scientist: Joydeep Dam, Bridgei2i

Srishti Deoras

Our next interaction for this month’s theme is with Joydeep Dam who is the director of algorithms and artificial intelligence at Bridgei2i Analytics Solutions. He has over 16 combined years of experience in the field of analytics, algorithm development, and quantitative disciplines. Prior to this, he has held various key positions in multiple different organisations. Here are his views on what it takes to be a good data scientist.

Analytics India Magazine: What are the key skill sets that you look for while hiring for data science roles? What are the languages and technical skills they should know?

Joydeep Dam: Some of the key skill sets are:

  • Quantitative and numerical competency and aptitude are critical to becoming a good data scientist. Clarity of fundamental concepts is extremely critical.
  • Regarding technical skills, there are far too many to be named. Good knowledge in a few of those is preferable. Whatever techniques they know, they should have a lot of depth in those.
  • In terms of language (programming), Python, R and C/C++ is necessary.
AIM: What are the non-technical skills and traits that a good data scientist should have? How important is effective communication and business mindset for being a good data scientist?

JD: Some of these are:

  • Ability to communicate effectively is extremely important to be a good data scientist. Quite often a data scientist will need to explain abstract concepts to a non-technical audience. It will be almost impossible to do that without good communication skills.
  • Without a business mindset, the solutions tend to become more detached from the real problems and become more academic in nature. Since a data scientist is expected to tackle real-life problems most of the times, a business mindset or a genuine appreciation of practical problems is crucial.
AIM: Is it educational qualification or experience that matters more to be a data scientist in companies?

JD: The right educational qualifications build the foundations to be a good data scientist. The right kind of exposure and experience allows him to get the proper perspective and understanding of where and how to use his skill sets.

AIM: What are the best resources for data scientists and what are the subjects they should master during the early days of his/her education to be a good data scientist?

JD: There are multiple resources that a data scientist can use: peers, universities, open source contents, books, etc. Some of the subjects such as maths, stats, programming are good skills to pick up in the early days.

AIM: What is more important, technology tools or algorithm concept learning?

JD: Both are equally important. Without tools, the algorithms can’t be implemented, and without conceptual understanding, you won’t know what you are doing with the tools. It can be thought about as the engine and the wheels of a car. The car won’t move ahead unless both are there.

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AIM: How important is the sector known for being a good data scientist?

JD: For a data scientist focusing specifically on a particular sector, it’s very important to have deep knowledge of that sector.

AIM: In a nutshell, what are the 3 must-have skills to be a data scientist?

JD: Knowledge and conceptual clarity regarding the fundamentals, knowledge of appropriate tools and platforms, deep understanding of the problems.

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