In the era of big data, we are facing a large amount of data on the daily basis and these data are very important for future business decision. For example, every smartphone comes with numerous sensor and different internet activities which continuously generate data and company continuously save the data for future use.
What is Data Visualization?
Data visualization is the presentations of pieces of information in a graphical form.It turns large and small datasets into visuals that are easier for human brains to understand and process.
"complex ideas communicated with clarity, precision, and efficiency" American statistician and Yale professor Edward TufteWhy is data visualization very important?
Data visualization is an important phase in data analysis because we can’t rely on a single frequency table and other statistics concept to understand the data. Good visualization can give a better understanding and gain insights.
Data visualization can also:
- Better Decision Making: Today almost all organizations from different sectors are using data visualization techniques to ask a better question and make a better decision.There are lots of more technology and tools made it easy to learn about your organization and make a data-driven decision.
- Story Telling: Data visualization and information graphics have become an essentials tools for media.
- Establish clear correlation between business operations and activity: Since the data is visualized in a highly comprehensive and interactive manner, establishing existing or possible correlations between various business operations comes in easy here. This gives business leaders a clear insight into business performance and needs for further strategies.
- Identify upcoming trends: Knowing about an upcoming trend in your business and planning for it in advance.
Some Use cases of data visualization:
- Country budget on defence:
Types of visualization:
These are the step basically used in data exploration process we introduced with two major things like types of variable and variable analysis.
Basically, there are two types of variables(I)Qualitative or Categorical: Qualitative data, that can’t be measured or belongs from some category or ranges for example married, urban etc (II) Quantitative or Continuous: Quantitative data are numeric in nature like your salary. And three different types of data analysis are (I) Univariate analysis(single variable). (II) Bivariate Analysis(Correlations). (III) Graphical analysis(scatter plots).
In above pic there are four different types of chart suggestions:
- Comparison chart: In comparison chart basically we compare different categorical data and continuous data. Basically, we use bar chart and line chart for this purpose.
- Relationship chart: It is widely used to understand the correlations between two or more continuous variable. Basically, we use Scatter chart for this purpose actually we can add a more continuous variable in the chart in which Bubble chart we use for visualization.
- Composition chart: Composition chart is basically used for distribution upon category and another variable. Basically, we use pie chart and bar chart.
- Distribution chart: It is basic steps for data visualization for categorical and continuous variables. Basically, we use Histogram, Boxplot, Multiple box plot, scatter plot.
Apart from these, there are also different types of visualization techniques like Heatmap, Area chart, Geospatial etc
Tools and Libraries for data visualization
- D3 js
This article is about basic concept towards data visualization. We will implement these concepts with different libraries and BI tools in upcoming articles.
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