Data visualization happens to be an interdisciplinary field that deals with the graphic representation of the data. It’s a style of communicating a large set of data like in churn analysis. You need to understand the graphics elements, which are the points or lines on the chart. Various points together can form the line of the curve. And each type of “line” and “curve” has a specific meaning. Through the mapping, we come to know how the element’s attributes change with the data. You can consider the bar chart as the mapping in which the bar length defines the “magnitude of the variables.” And various graphic designs come with a different readability score of the chart, and the mapping is the core competency of the data visualization. It has developed out of the Statistics, and we study it inside the descriptive statistics. You can consider it as a form of art as well as Science. You hence require both the design skills as well as the computing stills apart from the statistics skills. And if you want to learn all these, you can contact Naresh I Technologies. We provide complete Data Science online training for all Data Science certifications. Naresh I Technologies also is the number one computer training institute in Hyderabad and among the top five computer training institutes in India.
What is Data Visualization?
Data visualization is the technique for communicating the data or information via the visualization through objects like points, lines, bars, slices, curves, which are the example of graphics. The main aim is to ensure information clarity and efficiency of information for the user. You can consider it for data analysis and a part of Data Science. As per Vitaly Friedman, the main goal of data visualization is to communicate information clearly and effectively. And it’s done through the graphical objects, which we have already discussed. It does not mean that visualization should be boring for conveying the information intent or sophisticated to prove its functionality. And in reality, the aesthetic values and functionalities require the boosts equally, and only then we come up with the correct visual storytelling and let the stakes know the exact picture of the data. It’s not easy to ensure the balance, and only the best data science and analysis knowledge can make you successful in doing such. You might have noticed that in the majority of instances, we are not able to do this. However, when we do that and keep up with such storytelling, we are thrilled, as we get the real picture of sparse or even the most complex data.
Both Fernanda Viegas or Martin M. Wattenberg quoted once the above fact. And both the functionality and user attention are the goals of the data visualization.
Need for Data Visualization?
We make use of plots, statistical graphics, and information graphics to visualize the data more profoundly. We have other toolsets also. We can figure out the numerical data with dots, bars, and lines. And you can cater to the users the quantitative message or the dataset intent. However, keep in mind that these intents are not even known to the getter like the surveyors. And they come into the limelight only after Deep analysis. And this is an even more intuitive use case in the case of Big Data. And that is why Data Science is essential. That makes “Data Visualization” necessary in “Data Science.” And it’s a must during data analysis. It is “important” in feature engineering. You cannot compare or apply causalities with the data visualization. And we have a visualization tool for each kind of data. Like tables helps the users to look out for the specific measurements. And the various charts help find the relationships and the patterns within the data for a feature set. Remember, we also know the variables as features in Data Science.
How does Data Visualization Work?
Through Data visualization, we represent the information or the data graphically. And, we make use of the elements like graphs, charts as well as maps. And all these tools cater to the users or stakes the trends, patterns, and outliers in the data.
Types of Data Visualizations
Various commonly used data visualization charts, as well as the graph formats, are as below:
- Column Chart
- Stacked Bar Graph
- Stacked Column Chart
- Dual Axis Chart
- Line Graph
- Area Chart
- Pie Chart
- Waterfall Chart
- Bubble Chart
- Scatter Plot Chart
- Mekko Chart
- Bullet Graph
- Funnel Chart
- Heat Map
- Bar Graph
All of the above advance and make better the data interpretation, though each of them is a good fit for a specific data set. As a Data Scientist, you need to learn how to pick the right visual aid for a given data set. And if you do that properly, you and the stakes will be least confused and be up with more appropriate analysis.
Charts
In the charts, we have the data as symbols. Like, we have bars, lines, and slices in a bar chart, line chart, and pie charts.
Tables
The table is for studying specific measurements as we discussed above.
Infographics
The infographics provide the timeline of some historical data or the complete story of technology or any event.
Latest Data visualization trends
Data visualization is now up with cognitive frameworks and multidimensional imaging as well as intelligence. And they hence are more apt for data storytelling. According to Gartner, data stories via visualization will be the top method of analytic report presentation by 2025. And, around 75% of the stories will be through Augmented Analytic techniques. And you by now know the meaning of the augmented analytics, as we discussed in the previous blog.
However, a new trend is coming, and that is the data visualizing through the AR/VR, and we know this now as immersive analytics. And that is the three-dimensional data visualization that can help you develop models, games, realize models of virtual events, analyze supplement maps, and much more.
By 2022, the augmented VR is going to worth around $209bn globally. And that is good enough to provide you an insight into how much potential the analytics and data visualization has.
The VR caters to us a more intuitive data interaction. And via VR, we get the “real interpretation” of the data. And it’s more accurate too. And that makes the decision making easier.
Smart devices and real-time visualization are the other fields to have a look at in the field of data visualization.
Remember, Data visualization can make decision making much simple.
Data beyond the visuals
The data visualization is now seeing a new dimension via immersive analytics, and that is what we mean when we talk of the data beyond the visuals. And it sits at the confluence of Big Data, Machine learning, VR, and AR. It helps the businesses to understand each bit of the data. And it makes the use optimized as well.
And through it we can ensure:
- Seamless data interpretation
- Better decision making
- Better human involvement
Data democratization
Data democratization ensures that data is available to all and even the average end-users.
Data cleansing
It’s the process of making the data set more analytic friendly. You can compare it with data purging, where we delete the useless data.
Data governance
Through data governance, we ensure the integrity, security, and availability of the data. And it makes data management more intuitive and ensures its best usage.
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