What is data visualization?
Data visualization is transferring and changing information into some chart and visual stuff that we use it to make recognizing patterns in big datasets easier.
Visualizing data is one of the important part in data science which shows that data should be visualized after processing and modeling to help us make clear results. Also, data visualization is a part of the data architecture system, which maps the flow of data and provides a plan for data management, while documenting the assets of an organization.
Data Visualization has a very important role at analyzing data. For example, when a data scientist is writing advanced predictive analytics algorithms, visualizing the outputs is important to monitor the results and ensure that the models are working correctly. Because It is more simple to get result with visualization of the complicated algorithms than numerical outputs.
Totally, Data Visualization is a form of relation which show density and complexity of data in plots. The Pictures give us the ability to compare data and use them for analyzing the process in a more simple way.
We know that data visualization is important but why?
Data visualization, using visual data, provides a quick and effective way to communicate information. It helps businesses to recognize which factors affect on the customer’s behavior and which region needs to be proved. Therefor, not only you can make the data more useful for beneficiaries, but also predict the sales rate with data comprehension and visualization.
What are the features of data visualization?
It is accurate, useful, efficient and scalable.
What are the types of analysis for data visualization ?
There are 3 types of analysis for data visualization:
- Univariate analysis: Here we use a special feature for analyze all the dimension and features of data. One of the best and most efficient single feature plots for input the information about data distribution is distribution piece. When we want to analyze the effect on the output variable according to the input, we should use the distribution chart.
- Bivariate analysis: comparing data between two features means we have a bivariate analysis.
- Multivariate analysis: here we compare more than two variables.
What are the different data visualization models?
At first the only way to visualize data was to use a Microsoft Excel to turn data into a table, bar chart or any other chart. we can use this method for out visualization but there are better ways for this work:
- Infographics
- Bullet charts
- Thermal maps
- Time series graphs
- Line charts
- Tree diagrams
- Area charts
- Bubbly clouds
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