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Traditionally we have viewed data as describing what happened and it has helped us categorize the outcomes of events or actions that evaluate whether we are successful or not. It’s like being shown the history in detail. However, at the moment when technology is changing a lot, the combination of software and statistics has helped the data show us not only the past but also the future. This method is known as data science and was introduced to the world after the word “big data”.

Science is still in its infancy, but the speed at which it’s taking over corporations and corporations, Glassdoor and Harvard, don’t unfairly bode for the best careers of the future.

What role do data scientists play?

Those who are experts at managing data and manipulating it to solve business problems are referred to as data scientists. Their tasks include the following:

Understanding problems and data collection: A problem is considered half-solved when it is well understood. For example, if a beverage company wants to expand its business, data scientists need to understand ways that can increase profits. They collect data on the company’s infrastructure, capital, target customers, the population for which new stores are opened, etc.
Data manipulation: The collected data is often not clear or points are missing. It may seem extreme, but that’s the beauty of data science. Data scientists have many tools at their disposal to help them predict missing data and eliminate anomalies. This process includes installation, cleaning, finding hidden information, etc.
Transforming data: After transforming the data, it’s time to work on it and find solutions. This is where data scientists transform and analyze data to create a model. This is definitely the most important part of data science, and machine learning tools like Python are proving to be of great help.
Presentation and communication of data: Once the model is created, it needs to be translated into a form that can be read and understood by others. Data scientists often try to create more than one model so they can be tested before deploying them to the enterprise. The most commonly used tools for this purpose are Tableau, R, etc.
Companies are looking for data science
Today, the business environment that sets its process needs to look to the future and be prepared for it, and since scientists are professionals who do certain things, their demand will increase. Big industries like e-commerce, social media, retail, etc. are the kind that consumes data science. But not only these people since aviation, health care, sports, education, public administration and agriculture, etc. also understand that science is the magic key to the future.

Credit: Schalini M