Data Science, The term was coined when three different fields ( Marketing, Growth and Coding ) began clashing each other. When data in the world began to grow tremendously, it paved way for the term "Big Data" which eventually fell under one term which is called Data Science.

Multi-billion dollar companies began investing their assets in marketing and started collecting data from people. But is Data Science all about Marketing? No, not at all. For marketers to run a successful campaign, they need data. For Developers to develop the product according to client requirements, they need Data. From every problem, Data began to pave a solution and that is how Data Science, the term began to rule the market.

But Is Data Science similar to Information Technology? And again, the answer is No. Information Technology(IT) is all about developing a product for a client or a third party user but Data Science is about analyzing the data what the third party user needs and predicting a solution by a proper analysis with the collected data from the users.

So, Is Data Science only about collecting and analyzing the data. And again, the answer is No. There are many fields in Data Science from Gathering the data from the users to analyzing the data and predicting what the user might need in the upcoming days to matching the needs of every 2nd, 3rd party users.

Is "Data Science" really Dying after many automated tools in 2021? With pride, the answer is No. As per Scientific Seed, per day 2.5 quintillion bytes of data is been collected. Many tools like Spark, tableau, SAS, Matlab and so on.. many began to make the work easier for people working in Data Science domain but it isn't dying at all in 2021, it has increased to be honest. According to Glassdoor, two domains in Data Science field (Business Analyst and Data Scientist) stands in the top 10 most demanded jobs in 2021. This clearly paves to the conclusion that Data Science isn't dying.

What are the different types of field available in Data Science domain ?

a) Data Engineer - Main responsibility is to manage the Data Architecture and develop algorithms based yo make the featured raw data more in a usable manner especially for Enterprise field.

b) Data Scientist - The Myth everyone thinks when the word Data Science is used. Scraping and fetching meaningful information from the data and outcast in an insightful manner.

c) Business Analyst - Technically test the solution which is available in the market and advice the organization in the business standardized manner pushing them to cost efficiency methods. Sometimes they do assist Developer in the Business standards.

d) Data Analyst - Field where you should know the knowledge on Different Data viz tools like Tableau, Excel, Fusion charts and so on.. This is the field where you analyze the data using tools to fetch out insightful information on where you are standing among the organization.

e) Business Intelligence Specialist - The domain where you need to know about the analyzing tools like Microsoft Power BI, Qlik-sense, Zoho Analytics and so on. They talk part in Business meetings with a broad views on how a particular change can benefit the organization.

f) Machine Learning Engineer - This domain is where people invest much in developing some predictive algorithms so it can be used by DE and others in near future. It involves a lot of coding and a very good exposure.

g) Big Data Specialist - This role is a follow-up where the designed idea by Solution Architect, Data Scientist and Business Analyst are tested and confirmed whether it's feasible to the market. One of the very few roles which involves a lot of Data Involvement.

h) Solution Architect - One of the underrated and high-paid job but a minimum of 5+ experience is needed. This is where you are made to give a quick solution within a short span of time in a business standards. This solution only will be used by other members in organization.

i) Data Scraper - You can witness this role just by witnessing the role name. Data Scraper is about fetching the data from websites and sharing it either to open-source community or to the organization. This requires a Python knowledge and a very good listening skills.

These are one of the very few roles which comes under Data Science and it varies from organization to organization. If you work in a start-up, you will have an experience with the mix of 2-3 roles which is mentioned above.

For a Start-up or for a well established company to sustain in the market, Data Science is needed which will always pave them the path to every nook and corner of where they lag and where they are succeeding. So, my finally conclusion is Data Science isn't dead and it will be in the hot topic for next 10-15 years.