Data Science and Data Visualization

The expression “data science” (initially used reciprocally with “datalogy”) has existed for more than thirty years and was used at first as a substitute for software engineering by Diminish Naur in 1960. In 1974, Naur published Concise Survey of PC Methods, which unreservedly used the term data science in its survey of the contemporary data processing methods that are used in an extensive variety of applications.

In 2013, the IEEE Task Power on Data Science and Progressed Analytics was propelled, and the first global gathering: IEEE Worldwide Meeting on Data Science and Progressed Analytics was propelled in 2014. In 2014, the American Statistical Association section on Statistical Learning and Data Mining renamed its diary to “Statistical Analysis and Data Mining: The ASA Data Science Diary” and in 2016 changed its section name to “Statistical Learning and Data Science.” In 2015, the Global Diary of Data Science and Analytics was propelled by Springer to publish unique work on data science and big data analytics. In 2013, the first “European Gathering on Data Analysis (ECDA)” was sorted out in Luxembourg, establishing the European Association for Data Science (EuADS) in August 2015. In September 2015 the Gesellschaft für Klassifikation (GfKl) added to the name of the Society “Data Science Society” at the third ECDA meeting at the University of Essex, Colchester, UK.

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Data visualization is closely identified with information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics. In the new thousand years, data visualization has turned into a dynamic zone of research, instructing and development. As per Post et al. (2002), it has joined scientific and information visualization.

The history of data visualization begins in the second century A.D. with data plan into columns and rows and advancing to the underlying quantitative representations in the seventeenth century. As indicated by the Collaboration Design Establishment, French philosopher and mathematician René Descartes laid the groundwork for Scotsman William Playfair. Descartes built up a two-dimensional organize system for displaying values, which in the late eighteenth century Playfair saw potential for graphical correspondence of quantitative data. In the second 50% of the twentieth century, Jacques Bertin used quantitative graphs to represent information “naturally, unmistakably, precisely, and proficiently.”

Edward Tufte and John Tukey pushed the bounds of data visualization; Tukey with his new statistical approach of exploratory data analysis and Tufte with his book “The Visual Display of Quantitative Information” made ready for refining data visualization techniques for more than statisticians. With the progression of technology came the progression of data visualization; starting with hand-drawn visualizations and advancing into more specialized applications – including intuitive designs prompting software visualization.

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Softwares like SOFA, SAS, R, Cornerstone, Minitab and more consider data visualization in the field of statistics. Other data visualization applications, more focused and interesting to individuals, programming languages such as D3, Python, and JavaScript help to make the visualization of quantitative data a possibility. Non-public schools have also created programs to take care of the demand for learning data visualization and associated programming libraries, including free programs like The Data Hatchery or paid programs like General Assembly.



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