The Evolution of Data Visualization
History of Data Visualization
What started as a One-Dimensional Line representation of Longitudinal Distances between Toledo and Rome by a Flemish Astronomer, Michael Florent Van Langren in 1644 soon came to be known as the dawn of Data Visualization.
It was around the 18th century when various attempts to use Thematic Mapping such as geologic, economic, and medical data were made. Abstract graphs of functions, measurement error, and collection of empirical data along with William Playfair’s invention of most of the popular graphs used today such as Line, Bar, Circle, Pie chart and Histograms, Time Series plots, Contour plots, Scatter plots amongst others were also invented around this time.
Although most of the popular graphs were invented in the 18th century, it was not until the London Epidemic of 1854 that Data Visualization came to be known as the “Golden Age of Statistical Graphics”. The two most world renowned visualizations from this era included John Snow’s Map of Cholera Outbreak in London and Charles Minard’s chart showing stats of Napoleon’s 1812 infamous Russian Campaign.
In the beginning of the 20th Century, statistical visualizations hit a small roadblock; this era was called “Modern Dark Ages” of Data Visualization, where statisticians were increasingly concerned with exact numbers, and considered images to be overly inaccurate.
But in the second half of the 20th Century, the importance of Data Visualization increased with the emergence of computer processing. Computers gave statisticians the ability to collect and store data in increasingly larger volumes, as well as the ability to visualize the information quickly and easily.
Hal9 and Data Visualization
With the need to portray information in simpler ways, Data Visualization has become a go-to format to make it easier to identify patterns and trends than to look through thousands of rows on a spreadsheet.
And with this thought in mind, Hal9 Inc came up with an online and offline Data Science + Visualization platform that uses a simple drag and drop feature to build a Data Science Pipeline and at the same time visualize that data with ease. Hal9’s different features also give the user options to choose from. These Options include:
- Starting a new Data Science Project from scratch and building your own pipeline
- Various types of Informative Charts that act as guidelines to your new Project
- Hal9 also provides you a platform to learn new AI topics such as Image Classification
- Apart from just visualizing your Data, Hal9 gives you the option to build your own Predictive Model and also gives you different examples on how to use this feature.
All in all, Hal9’s innovative approach towards Data Science is not only accessible but also visually stimulating. Give it a try yourself at hal9.ai and let us know what you think. If you’re ready for a bigger challenge, you can create entirely new data sources, transformations, visualizations or predictive models, and contribute them to our open source GitHub repo.