Data Science 101: What you expected vs What it really means

Written by Ajay Neman| Updated: August 18, 2020 8:59:02 am

According to the Harvard business review – Data science Is the “sexiest job” of the 21st century

What does data science actually mean? It is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals.

The one who wants to become a data scientist has expected a lot from it but who knows, in reality, they will meet it or not. In this article, we are going to highlight some of the expectations of data scientists and their reality in real life or in industry.

Expectation: People don’t know what “Data Science” does.

Reality: Some people think that it is all ML, AI, and/or custom algorithms. Others think it’s simply analytics. Many data scientists may spend a significant portion of their time on Extract-Transform-Load (ETL). The truth is – all of these things are possible! On the job, you may be asked to solve a hard problem and have minimal to no direction on how to reach the solution. Because people don’t know what data science does, you may have to support yourself with work in DevOps, software engineering, data engineering, etc.

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Expectation: Becoming a data scientist requires a Guinness brain or Ph.D. degree.

Reality: Now holding a Ph.D. Degree is an amazing achievement. It requires years of hard work and dedication. But it is not always necessary to get a ph. D. degree to become a data scientist. To understand these let’s divide it into two categories –Applied data science role and research role Applied data science is primarily about working with existing algorithms and how they work; you will not need a Ph.D. degree for it.

Most of the openings fall into this category. But what if you are interested in a research role, then yes you might need a Ph.D. degree. Creating new algorithms from scratch, researching them, writing scientific papers, etc. For eg. Ph.d. in linguistic will be immensely helpful to make a career in NLP( natural language processing )

Expectations: Data science is all about complex coding using different tools.

Reality: Data science is all about understating and solving the problems. Having some strong coding skills might be advantageous but it is not necessary. What is more important is your ability to frame business problems into actionable insights, collecting good data and understanding it, etc.

Companies hiring data scientists will not consider the tool expertise alone instead they look for a professional who has a quiet combination of mathematical skills, programming skills, and business skills, etc. Day to day job also requires less tangible hard skills such as the ability to look and understand bias, problem-solving with messy data mostly created by third parties, validating findings, working in a team, and communicating effectively to present results in simpler terms.

Expectations: Participating in data science competition translates to a Real-Life Project

Reality: Participating in a data science competition is an excellent part of your Data Science journey.

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