Relevance of Data Science --

Relevance of Data Science --


Data Science is one of the most popular emerging domains and most sought-after career options. According to a report by LinkedIn, Data Science dominates its emerging job ranking with a whopping 37% hiring growth over the past three years. Data Science is revolutionizing almost every industry and gaining popularity day by day.


1. Why Is Data Important In Science?

Data is a precious asset of any organization. It helps firms understand and enhance their processes, thereby saving time and money. Wastage of time and money, such as a terrible advertising decision, can deplete resources and severely impact a business. The efficient use of data enables businesses to reduce such wastage by analyzing different marketing channels’ performance and focusing on those offering the highest ROI. Thus, a company can generate more leads without increasing its advertising spend.

2. What Is Data Science & Why Is It Important?

Data is meaningless until its conversion into valuable information. Data Science involves mining large datasets containing structured and unstructured data and identifying hidden patterns to extract actionable insights. The importance of Data Science lies in its innumerable uses that range from daily activities like asking Siri or Alexa for recommendations to more complex applications like operating a self-driving car.

The interdisciplinary field of Data Science encompasses Computer Science, Statistics, Inference, Machine Learning algorithms, Predictive Analysis, and new technologies.

3. History Of Data Science--

In 1962, John Tukey wrote about the convergence of Statistics and computers to devise measurable outputs in hours. In 1974, Peter Naur mentioned the term ‘Data Science’ multiple times in his review, Concise Survey of Computer Methods. In 1977, the International Association for Statistical Computing (IASC) was formed to link modern computer technology, traditional statistical methodology, and domain expertise to convert data into knowledge. In the same year, Tukey composed a paper, Exploratory Data Analysis, that briefed the importance of using data.

By 1994, organizations had started gathering tremendous individual data for new showcasing efforts. In 1999, Jacob Zahavi stressed the need for new devices to deal with the gigantic chunk of organizational data. In 2001, William S. Cleveland presented an activity plan depicting how to create a specialized understanding and scope of Data Scientists and indicated six regions of studies for offices and colleges.

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