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Data Science – Getting Started ”; Previous Next Data Science is the process of extracting and analysing useful information from data to solve problems that are difficult to solve analytically. For example, when you visit an e-commerce site and look at a few categories and products before making a purchase, you are creating data that Analysts can use to figure out how you make purchases. It involves different disciplines like mathematical and statistical modelling, extracting data from its source and applying data visualization techniques. It also involves handling big data technologies to gather both structured and unstructured data. It helps you find patterns that are hidden in the raw data. The term “Data Science” has evolved because mathematical statistics, data analysis, and “big data” have changed over time. Data Science is an interdisciplinary field that lets you learn from both organised and unorganised data. With data science, you can turn a business problem into a research project and then apply into a real-world solution. History of Data Science John Tukey used the term “data analysis” in 1962 to define a field that resembled current modern data science. In a 1985 lecture to the Chinese Academy of Sciences in Beijing, C. F. Jeff Wu introduced the phrase “Data Science” as an alternative word for statistics for the first time. Subsequently, conference held at the University of Montpellier II in 1992 participants at a statistics recognised the birth of a new field centred on data of many sources and forms, integrating known ideas and principles of statistics and data analysis with computers. Peter Naur suggested the phrase “Data Science” as an alternative name for computer science in 1974. The International Federation of Classification Societies was the first conference to highlight Data Science as a special subject in 1996. Yet, the concept remained in change. Following the 1985 lecture at the Chinese Academy of Sciences in Beijing, C. F. Jeff Wu again advocated for the renaming of statistics to Data Science in 1997. He reasoned that a new name would assist statistics in inaccurate stereotypes and perceptions, such as being associated with accounting or confined to data description. Hayashi Chikio proposed Data Science in 1998 as a new, multidisciplinary concept with three components: data design, data collecting, and data analysis. In the 1990s, “knowledge discovery” and “data mining” were popular phrases for the process of identifying patterns in datasets that were growing in size. In 2012, engineers Thomas H. Davenport and DJ Patil proclaimed “Data Scientist: The Hottest Job of the 21st Century,” a term that was taken up by major metropolitan publications such as the New York Times and the Boston Globe. They repeated it a decade later, adding that “the position is in more demand than ever” William S. Cleveland is frequently associated with the present understanding of Data Science as a separate field. In a 2001 study, he argued for the development of statistics into technological fields; a new name was required as this would fundamentally alter the subject. In the following years, “Data Science” grew increasingly prevalent. In 2002, the Council on Data for Science and Technology published Data Science Journal. Columbia University established The Journal of Data Science in 2003. The Section on Statistical Learning and Data Mining of the American Statistical Association changed its name to the Section on Statistical Learning and Data Science in 2014, reflecting the growing popularity of Data Science. In 2008, DJ Patil and Jeff Hammerbacher were given the professional designation of “data scientist.” Although it was used by the National Science Board in their 2005 study “Long-Lived Digital Data Collections: Supporting Research and Teaching in the 21st Century,” it referred to any significant role in administering a digital data collection. An agreement has not yet been reached on the meaning of Data Science, and some believe it to be a buzzword. Big data is a similar concept in marketing. Data scientists are responsible for transforming massive amounts of data into useful information and developing software and algorithms that assist businesses and organisations in determining optimum operations. Why Data Science? According to IDC, worldwide data will reach 175 zettabytes by 2025. Data Science helps businesses to comprehend vast amounts of data from different sources, extract useful insights, and make better data-driven choices. Data Science is used extensively in several industrial fields, such as marketing, healthcare, finance, banking, and policy work. Here are significant advantages of using Data Analytics Technology − Data is the oil of the modern age. With the proper tools, technologies, and algorithms, we can leverage data to create a unique competitive edge. Data Science may assist in detecting fraud using sophisticated machine learning techniques. It helps you avoid severe financial losses. Enables the development of intelligent machines You may use sentiment analysis to determine the brand loyalty of your customers. This helps you to make better and quicker choices. It enables you to propose the appropriate product to the appropriate consumer in order to grow your company. Need for Data Science The data we have and how much data we generate According to Forbes, the total quantity of data generated, copied, recorded, and consumed in the globe surged by about 5,000% between 2010 and 2020, from 1.2 trillion gigabytes to 59 trillion gigabytes. How companies have benefited from Data Science? Several businesses are undergoing data transformation (converting their IT architecture to one that supports Data Science), there are data boot camps around, etc. Indeed, there is a straightforward explanation for this: Data Science provides valuable insights. Companies are being outcompeted by firms that make judgments based on data. For example, the Ford organization in 2006, had a loss of $12.6 billion. Following the defeat, they hired a senior data scientist to manage the data and undertook a three-year makeover. This ultimately resulted in the