Welcome to the world of Visualization!!!
By: Puja Sarkar
Tags: Visualization, Charts, Analytics, Storytelling, Insights

The potential synergy of text and graphics can be appreciated by talking through your own graphics, explaining them to others. Why have you drawn those graphics? How have you drawn them? What can be seen? Are there interesting patterns? What could be changed and improved? Which other graphics might be drawn? How can conclusions be checked? There should be more talking about graphics and less relying on the graphics to speak for themselves.

Selecting from the wide range of graphics wisely, and understanding how to gain insights, are not trivial tasks. The lack of a theory of data visualization to guide and build on is a key issue.

Better software has meant easier and more flexible drawing, consistent themes, and higher standards. There are many Web pages where graphics are discussed and debated. This is a huge improvement over the situation of even 20 years ago. Not long ago, the ability to create smart data visualizations, or dataviz, was a nice-to-have skill. For the most part, it benefited design- and data-minded managers who made a deliberate decision to invest in acquiring it. That’s changed. Now visual communication is a must-have skill for all managers, because more and more often, it’s the only way to make sense of the work they do.

Data is the primary force behind this shift. Decision making increasingly relies on data, which comes at us with such overwhelming velocity, and in such volume, that we can’t comprehend it without some layer of abstraction, such as a visual one.

Impact of visualization:

Our eyes are drawn to colors and patterns. We can quickly identify red from blue, square from circle. Our culture is visual, including everything from art and advertisements to TV and movies.

Data visualization is another form of visual art that grabs our interest and keeps our eyes on the message. When we see a chart, we quickly see trends and outliers. If we can see something, we internalize it quickly. It’s storytelling with a purpose. If you’ve ever stared at a massive spreadsheet of data and couldn’t see a trend, you know how much more effective a visualization can be.

Data Visualization is now one of the major pillars of Data Science. It uses shapes, lines and other figurative approaches to portray numbers. There are multiple techniques that can be used to present data based on what kind of data is available. Here the focus have been made on: bar chart, pie chart, scatter plot, histogram and line chart.

Bar Chart

A bar chart is a graph with rectangular bars. The graph usually compares different categories. Although the graphs can be plotted vertically (bars standing up) or horizontally (bars laying flat from left to right), the most usual type of bar graph is vertical.

The horizontal (x) axis represents the categories; The vertical (y) axis represents a value for those categories. In the graph below, the values are percentages.

Bar Graph Examples (Different Types)

A bar graph compares different categories. The bars can be vertical or horizontal. It doesn’t matter which type you use — it’s a matter of choice (and perhaps how much room you have on your paper!).