Data visualization is about emphasizing the valuable information from a dataset by using visual representation tools. Charts, graphs, maps, and even simply colored texts are tools to visualize data for highlighting patterns, similarities, differences, and outliers in a set of raw data.
Dan Ariely (Professor of Behavioral Economics) said that;
Vision is our best system. We have lots of practice with it (we see many hours in the day and for many years) and more of our brain is dedicated to vision than to any other activities. So consider this — if we make mistakes in vision, what is the chance that we would not make mistakes in other domains?
In real life, you have to analyze the huge amounts of data and make profitable decisions for your business. In order to do that, you would need a visual that provides precise information without distractions.
Today, data visualization is more important than ever. Because we are dealing with big data. And, streaming data even makes it more complex to understand while we want to see what is going on easily.
Think about a data table that contains 100 employees with the following information; name, working hours, age. And this table is populated randomly. If you decide to promote your best employee, you have to check all 100 rows. On the other hand, if you use one of the most simple data visualization techniques and showing this table sorted by working hours, you would see the lucky one easily.
A good data visualization tells you the summary of the data by saving your time. Instead of reading tons of lines, you just see patterns and outliers. Not everyone is a visual designer or data scientist, but almost everyone wants to understand any kind of data without wasting much time. A good visualization removes noises from the data and indicates valuable information that makes sense.
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Types of Data Visualizations
There are tons of different data visualization types. Mostly, they vary by purpose. For instance, if you need a comparison between categories, a line chart would not be suitable for that purpose. Instead, a pie chart makes it easy to see the difference between those categories. Below list contains the well-known data visualization types:
- Charts (Pie Chart, Line Chart, Bar Chart)
- Tables
- Graphs
- Maps
- Infographics (Group of Charts)
- Dashboards (Group of Infographics, Charts, tables, etc.)
Books about Data Visualization
In this section, we are listing some valuable books from Amazon’s best sellers.
- Storytelling with Data: Let’s Practice!
- The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios
- Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures
What else can Data Visualization Do?
Data visualization not only helps in converting bulky facts and figures into catchy diagrams and graphs but does much more than just that.
Coding with Python promotes the in-depth analysis of data which helps in comparison and contrast of the tables and charts. It helps in interpreting the calculated results and shows positive and negative trends. This allows the user to establish timelines and predict future values and data mapping.
Data dashboards help in keeping the plans on track and clouds allow brainstorming in a comfortable manner. All of this and more comes together seamlessly to create a solid medium of effective data communication.