Data Science is such a diverse field with people from so many backgrounds working in probably all the domains you can think of. As a result of being all over the place, it had lead to quite a lot of misconceptions and myths about Data Science. This article will debunk some of the most common misconceptions in Data Science.

Data scientists will be replaced by Artificial Intelligence

There is quite a possibility that AI will carry some of the tedious and repetitive tasks in data science like data preparation and data cleaning. However, a ‘Data scientist’ will always be required to carry out the advanced operations and to tell the machine what needs to be done. There is a drive to automate data science. People are building more sophisticated algorithms with a tendency to eliminate the need for a dedicated data scientist. However, that is highly unlikely to happen as even the most advanced AI systems work with what we tell them to work with and requires human guidance.

The AI or machine learning systems cannot judge what is right for the business problems and what kind of domain knowledge will help them. And they do not know what the trends and predictions mean in the real world context and how it can benefit the business as a whole. Hence the Data Scientist is here to stay and the demand for these skills is at an all-time high.

Data Science is all about building models

As much I wish this was true, but this is not the case. Building Machine Learning Models is one of the small pieces of the Big data science pipeline which includes Data Acquisition, Data Cleaning, Data Preparation, Data Wrangling, Visualization, Analysis, Model Deployment, and much more. Generally, building all the Machine Learning & Deep Learning models amount to about 15-25% of total time in Data Science pipeline. More than 50-60% of the time in Data Science pipeline goes into Data Cleaning, Data Preparation & Data Collection.

Business/Domain knowledge is not so important

This is a big myth in the industry and the Data Science aspirants don't focus on this part. But having a particular Business or Domain Knowledge is so crucial. This is what differentiates an experienced Data Scientist with the Data Scientist who have just started their career. With the domain knowledge, you can uncover and discover so many important things which a world-class Data Scientist will not be able to do. In each and every step in the Data Science pipeline having the domain knowledge for eg. you can discover good Data Featurization and Data Visualization & Analysis. Also, it makes you better frame the business objective for the problem to solve.


Learning a tool is most important in Data Science

Mastering data science is not just about learning the fancy tools and languages as you are not just a programmer. Though the tools and languages help in so many ways. But it requires analytical and business acumen along with the understanding of the applications of statistics, machine learning and AI for solving business problems. Data Scientist should have good problem-solving and know how of where and when to apply a tool or an algorithm for the given business objective. Also one should have the crucial ability to communicate results to the stakeholders in a simpler and intuitive way.

Data Science is just a Hype

A lot of people debate that Data Science too much hyped up and it won't last long. Data Science has become one of the most important aspects in determining the success of any organization irrespective of the domain. According to IBM, we are generating data at the rate of 2.7 Quintillion bytes per day and 90 percent of the world's data (that's 90 percent of all the data ever created) had been created in the previous two years. With so much data being generated from such diverse sources Data Science will help us to structure data, analyze data, draw hidden patterns for business and build solutions to solve crucial real-world problems.


Written in Collaboration with Aditya Gupta, Data Scientist at Board Infinity

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