When large chunks of data are presented to our faces we cringe behind our board seats and allow our minds to wander and eventually switch off. At the sight of ginormous data, our brains get distracted to look for easier options to digest information. We get attracted to lighter methods to understand data – which is why graphs create a better appeal as compared to pure prose.
In Data Science, data visualization is without doubt one of the top words today. No matter what type of data you want to analyse, doing data visualization seems to be a necessary step. It is one of the steps of the data science process developed by Joe Blitzstein, which is a framework for approaching data science tasks.
After data is collected, processed, and modeled, the relationships need to be visualized so that a conclusion can be made. Data Visualization is also a component of the broader discipline of data presentation architecture which seeks to identify, locate, manipulate, format, and present data in the most efficient way. But many people don’t have an idea of how data visualization came into existence, despite them getting Data Science and Analytics Jobs
Over the years, the idea of presenting information – a skill that is easily learnt in our Data Science programs – in colorful pictorial representations or applications as well that can easily and quickly be understood by the layman has continued to be adopted in the field of statistics. Human brains show to remain captivated by a subject when images are used as opposed to endless pages of prose. The mathematical field is one in which these representations continue to be used to present financial data in graphs, pie charts and the likes. Here is a brief history of how data visualization evolved from what it was more than 200 years ago, to the Microsoft Excel then Python’s map drawer and many more others that can be accessed at 4IR Club.
Maps arguably remain to be among the first ways in which data was first represented pictorially. See business intelligent mapping app. As it was back then, maps function to provide navigation paths, represent border lines, locate features and show the approximate distance between two points. Simple navigation apps we now enjoy such as Google Maps were in earlier days a collection of eye witness accounts and a fair amount of guess works from the then locals. Out of it evolved the drawings of the surface of the spherical Earth on a flat piece of paper.
Local geography was presented on straight lines on the map, translating to lines of constant bearing when looking at a compass. It proved popular, with several hundred map copies being printed to help with seafarers in navigation.
William Playfair’s Charts
William Playfair, being heavily known for accounting, inventing, metalwork, investment broking, economics, banking and among other many fields he engaged in, has a lasting legacy is in the field of statistics, with the charts he designed forming the core of data visualisation today.
In Playfair’s day, data were typically displayed in dry tables, presented with little thought for their interpretation. If you wanted to understand something, there were no intuitive shortcuts, only the laborious task of poring back and forth through the numbers, remembering, copying and comparing figures as you went.
When Joseph Priestley came up with a display chart in 1765 that showed the overlapping lifetimes of various classical statesmen and philosophers, he listed their names, birth and death year. Playfair, advancing on this idea, decided to use the listing method to plot his first bar chart that showed Scotland’s trade economic metrics with various territories in Europe and the New World. This success first appeared in his Commercial and Political Atlas.
The Onset of The Pie Chart
Fifteen years later, Playfair was at it again, this time with the sometimes-controversial pie chart and various creative combinations. It’s astonishing to think, that more than two hundred years later, the ideas of one man still make up the bulk of the chart options in state of the art data visualization software.
Charles Joseph Minard
A few decades after Playfair’s pie charts, French civil engineer Charles Joseph Minard made significant innovations in combining the fields of statistics and cartography. In 1845 he created a flow map displaying traffic data collected on roads through Eastern France. Robotic and Programming Course will help your kids to learn Data Visualization at a tender age of 9 years and above.
Despite being commonly celebrated in the field of modern nursing, Nightingale was a talented mathematician and pioneer in the graphical representation of statistics. Building on the ideas of Playfair, she incorporated charts into many of her publications and is credited with the invention of the Polar Area Chart, or “Coxcomb”. This chart depicts the different causes of death by month in the Crimean War, with the area of each wedge representing the size of the statistic. This type of chart lends itself well to cyclical data, although in this case Nightingale provided separate charts for the two years she covered.
The scatter plot is a mainstay of bi-variate analysis, an invention of statistician Francis Galton. In analysing the relationships between two variables, Galton devised a graphical technique where frequencies of each combination are plotted on a grid. Over this grid, contour lines are overlaid, showing the density of the data. For two correlated, normally distributed, variables, these contours should form an ellipse with the long axis acting as a form of linear regression.
The Information Age
While progress staggered in the first half of the twentieth century, the advent of computers sped up the introduction of the first commercial Graphical User Interface (GUI), and with it, applications like spreadsheets having the ability to automatically generate graphics from tables of information. Our One on One Coaching Sessions will furnish you with skills that will enable you perform your tasks by a few clicks of the mouse, along with added ease of editing, formatting and updating proved of great help to the advancement of visualization tools.
From there, the variety of charting techniques and styles has exploded, with countless software packages offering an array of methods for presenting your data.
Data visualization is a huge field with many disciplines. It is precisely because of this interdisciplinary nature that the visualization field is full of vitality and opportunities. Where business intelligence (BI) tools like Predictive Analytics Lab Business App can take huge swaths of data and parse that into digestible data points, data visualization is the presentation portion of that equation.
The main purpose of any imagery functions is to quickly transfer information from the machine to the human brain, not only efficiently but also in the most meaningful manner possible. Therefore, it is not the artistic value of a visualization that counts but the clarity of the message it conveys. To get skills on Data Visualization Click here