Before the boost of artificial intelligence, big data analysis, and machine learning, there was statistics which was very popular. Statistics is nothing but the study of patterns using mathematics and it is a way to understand the problems in the real world. Data visualization is the same concept. It has become popular these days because of its ability and power to display all the results at the end of a machine learning process. But, it is also being used increasingly these days for explanatory data analysis before applying the machine learning models.
We all know that nowadays there is a huge buzz going over the word data, such as data mining, big data, data warehouse, data science, data analysts, etc. It highlights that data plays a major role in the current era as it is influencing the everyday activities of humans. Let us understand data visualization to a greater extent.
Understanding data visualization
Every day, humans generate more than 2.5 quintillion bytes of data that range from text messages, emails, IoT devices, images, autonomous cars, etc. All this huge amount of data is readily available through which we can leverage useful information thus helping various organizations to get a clear insight into different areas. For example, with the help of this information, organizations can know about how to bring a boost for the revenue, which fields need more focus, how to seek customer’s attention, etc.
But do you think it is easy to interpret all this collected data? The answer is no! This data is present in a raw format and several steps are needed to be performed so that this raw data can be converted into useful information. This is where the job of a data scientist starts. We provide them with the raw data; they start working on the stages which include data acquisition, cleaning, visualization, building a model for predicting future information, etc. Among them, data visualization is a key step.
Data visualization is a graphical representation of raw data and information. It is the process of producing images that shows the relationship between represented data to the viewers. We can represent this data in the form of charts, graphs, or any other visualization format. We will discuss this later.
Why data visualization?
Data does not make sense sometimes until we look at it in a visual form. Its visual format allows the patterns and trends to be seen more easily rather than looking through hundreds and thousands of rows on a spreadsheet. There is a great need of interpreting large batches of data. It is important not only for data analysts and data scientists, it is necessary for almost every career. Whether you are working in tech, marketing, design, finance, or any other career, data visualization is a must.
The sole purpose of data analysis is to gain useful insights and data is much more worthy when it is visualized. Even if meaningful insights are pulled from all the data without visualization, even then it will be quite difficult to understand its meaning. Charts and graphs make it easier to understand and identify all the trends and patterns.
Types of data visualization charts
Now that we have understood what data visualization is and why is it important, let us see the different types of charts and graphs used for the same.
Line chart
A line chart is the simplest chart that illustrates the changes that happen over time. The x-axis is the period and the y-axis is the quantity. This can be helpful to illustrate the sales of a company for a month or how many units factory produce every day.
Area chart
An area chart is just the adaptation of a line chart. The area under the line is filled to show its significance. The colored area should be transparent so that the overlapping areas can be seen clearly.
Bar chart
Just like the line chart, the bar chart also illustrates the changes over time. But if there is more than one variable present, then this chart can make it easier to compare all the data for every variable at every second. For example, it can compare the company’s sales from the present year to last year.
Histogram
A histogram looks like a bar chart. But, it represents frequency rather than trends. The x-axis represents the intervals and the y-axis represents frequency. So, every bar represents the frequency for that time interval.
Scatter plot
We use scatter plot to find the correlations. Every point of scatter plot means that when x = this then y = this. In this way, if the points show trends in a particular way then there is a relation between them otherwise not.
Bubble chart
It is an adaptation of a scatter plot. Each point is a bubble. Their area has a meaning along with its placement on the axes. But, not every data will fit perfectly on this as it has limitations on the size of the bubble due to the limited amount of space on axes.
Pie chart
It is best for showing percentages. It precisely presents the pieces in proper proportions.
Gauge
It illustrates the distance between the intervals. We can use multiple gauges at a time for multiple intervals. It can be represented as a clock-like or tube-type gauge.
Heat map
A heat map is a color-coded matrix. We use a formula to color each cell of the matrix. It represents the relative value or risk of that particular cell. Colors green and red are widely used as green represents better results whereas red is the worst result.
Frame diagram
These diagrams are treemaps that consist of branches that have even more branches connecting to them. It shows a hierarchical relationship structure.
Conclusion
In data analysis, data visualization is a crucial step. Without it, important messages and insights can get lost. Once your extract data from the web, it goes through the data analysis process that gives the organization easily consumable graphs and charts to gain meaningful insights. If your organization is ready to get the most from the data, data visualization is the key.