What is the Purpose of Data Science?
In this article, we will go through the role that a Data Scientist plays. There is a veil of mystery surrounding Data Science. While the buzzword of Data Science has been circulating for a while, very few people know about the real purpose of being a Data Scientist. We will go through the various responsibilities that a Data Scientist must fulfill and understand as to what industries seek from employing Data Scientists.
Purpose of Data Science
The principal purpose of Data Science is to find patterns within data. It uses various statistical techniques to analyze and draw insights from the data. From data extraction, wrangling and pre-processing, a Data Scientist must scrutinize the data thoroughly. Then, he has the responsibility of making predictions from the data. The goal of a Data Scientist is to derive conclusions from the data. Through these conclusions, he is able to assist companies in making smarter business decisions. We will divide this blog in various sections to understand the role of a Data Scientist in more detail.
Why Data Matters
Data is the new electricity. We are living in the age of the fourth industrial revolution. This is the era of Artificial Intelligence and Big Data. There is a massive data explosion that has resulted in the culmination of new technologies and smarter products. Around 2.5 exabytes of Data is created each day. The need for data has risen tremendously in the last decade. Many companies have centered their business on data. Data has created new sectors in the IT industry. However,
a) Why do we need Data?
b) Why do industries need Data?
c) What makes data a precious commodity?
The answer to these questions lies in the way companies have sought to transform their products.
Data Science is a very recent terminology. Before Data Science, we had statisticians. These statisticians experienced in qualitative analysis of data and companies employed them to analyze their overall performance and sales. With the advent of a computing process, cloud storage, and analytical tools, the field of computer science merged with statistics. This gave birth to Data Science.
Early data analytics based on surveying and finding solutions to public problems. For example, a survey regarding a number of children in a district would lead to a decision of development of the school in that area. With the help of computers, the decision-making process has been simplified. As a result, computers could solve more complex statistical problems.
As Data started to proliferate, companies started to realize its value. Its importance reflected in the many products designed to boost customer experiences. Industries sought experts who could tap the potential that data holstered. Data could help them make the right business decisions and maximize their profits. Moreover, it gave the company an opportunity to examine and act according to customer behavior based on their purchasing patterns. Data helped companies boost their revenue model and helped them craft a better quality product for clients.
Data is to products what electricity is to household gadgets. We need data to engineer the products that cater to the users. It is what drives the product and makes it usable. A Data Scientist is like a sculptor. He chisels the data to create something meaningful out of it. While it can be a tedious task, a Data Scientist needs to have the right expertise to deliver the results.
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