Data Science

Data Science, The Past, Present, And Future!

Space Station Moon Landing Apollo 15 James Irwin

Data Science is the field of collecting, mining, analyzing, and finding business solutions from data. Science is applied to business to make better decisions and grow personally and professionally. As a result, data science is required more and more today, in every company department, and is in great demand.

Steps involved in Data Science

First, the data is collected from various users through surveys, interviews, etc. The data is then stored where it is cleaned and then stored again in the appropriate format. Data cleaning means removing redundancies or remove data that is irrelevant for the outcome or not filled properly. For example, if there is a form of 7 fields and the user fills only six or fills the form with gibberish, those columns have to be removed to get clear and accurate final results. The next step is mining this data. Data mining is the process of acquiring information from the data that is received. There are various types of data mining, namely Classification Analysis, Association Rule Learning, Anomaly or Outlier Detection, Clustering Analysis, Regression Analysis. From data mining, there are various things that we can perceive about the past, present, and future. 

We can find out if some event occurred in the past to make an impact on why such a thing happened in the first place. We can find out why something is happening in the current situation and what can happen in the future based on the past and present information. For example, we can find trends, likes, and dislikes of people. In business, data mining is very important as we can find what our customers like the most, and we can increase our sales and grow our business. Then the data is fed to some tool to check the accuracy of the above steps we just carried out, and then we can build visualizations based on which we can predict and make efficient business decisions.

Examples of data science

One example of Data Science in everyday life would be in a supermarket where an owner observes and keeps data of what type of people are buying what, their gender, etc., to identify and increase his sales. For example, if a male is buying shaving cream, he is bound to buy a razor. So the owner will keep these two things together so that the male picks the two things simultaneously, whereas, in the previous scenario, he would have to search for two different things and would have to let go of one if he didn’t find it. But now, it is natural for him to buy both and increase sales. Or if a girl is buying nail polish, she is bound to buy a nail polish remover, and even if she was not going to buy the nail polish remover today when she sees it next to the nail polish, she remembers and picks it up and in turn increases sales as well. So, this research, pattern, and prediction come under Data Science.

Data Science is required everywhere in life today and makes our lives easy. Python and R are the two main languages used in Data Science, and there are also many tools available to make the life of developers easy.

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