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 there being so much hype around data science, there have also been a lot of myths about Data Science.
This article will debunk some of the most common myths about Data Science.
1. Data Scientists will be replaced by Artificial Intelligence
This is the most common data science myth. 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.
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 require 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 they can benefit the business as a whole.
Hence, a ‘Data scientist’ will always be required to carry out the advanced operations and to tell the machine what needs to be done.
2. Data Science is all about building models
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 the total time in the Data Science pipeline. More than 50-60% of the time in the Data Science pipeline goes into Data Cleaning, Data Preparation & Data Collection.
3. Business/Domain knowledge is not so important
This is a big myth in the data science industry. This is why aspirants don't focus on this part. But having business or domain knowledge is so crucial in data science. This is what differentiates an experienced Data Scientist from a Data Scientist who has 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. It's easier for you to know the main business objective when you have knowledge of the domain.
4. Learning a tool is most important in Data Science
Although this is a prerequisite, mastering data science is not just about learning the fancy tools and languages. You are not just a programmer, though the tools and languages help in so many ways.
It requires analytical and business acumen along with an understanding of the applications of statistics, machine learning, and AI for solving business problems.
A Data Scientist should have good problem-solving skills and know 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 more intuitive way.
5. Data Science is just a Hype
A lot of people debate that the Data Science hype is too much and it won't last long. The fact is, 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% of the world's data (that's 90% 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.
Watch this bonus video to learn about the Top 4 Data Science Resume Myths.
Written in Collaboration with Aditya Gupta, Data Scientist at Board Infinity
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