- Tableau Introduction
- Data Visualization
- Tools and features of Data Visualization
- Advantages and Disadvantages of Tableau
- Tools of Tableau
- Download and Installation process of Tableau
- Tableau – Data Terminology
- Tableau Architecture
- Difference between Power BI and Tableau
- Data Types in Tableau
- Tableau Navigation
- Tableau Aggregation
- Tableau String Function
- Tableau Logical Function
- Tableau Expression Function
- Tableau Joins
- Data Extraction In Tableau
- Data Blending In Tableau
- Tableau Sorting
- Tableau Filters
- Tableau Bar Chart
- Tableau Histogram
- Interview Questions And Answers Of Tableau Part 1
- Interview Questions And Answers Of Tableau Part 2
What is Data Visualization?
Data visualization is the use of design content such as graphics, charts, and mapping to create a pictorial representation of statistical data and information.Data visualization transforms vast and micro data sets into graphics that user can easily understand and process.Variations, structures, and trends in data can be easily understood using data visualization tools.
In the Big Data environment, data visualization tools and techniques are critical for evaluating massive amounts of data.Data visualizations are all around you, yet they always come in the shape of graphical form. Graphical representations are still used to describe the combining of numerous representations and elements of data.
Nowadays data visualization tools expand further than the charts or graphs included in a Microsoft Excel spreadsheet to include scales and sensors, geographical maps, graphical analysis, a histogram, and a fever chart, among other features.
Data Visualization History
The theory of using visuals to understanding data from graphical information was first introduced in the 17th century, and it has been later redesigned as the pie graph in the early 1800s.When Charles Minard planned Napoleon’s invasion of Russia numerous years later, it was one of the most accomplished instances of statistical infographics. The map depicts the strength of Napoleon’s army as well as the route of his march from Moscow, as well as extraction temperature levels for a more proper analysis of the situation.
Computers allowed for the processing of massive amounts of data at high rates of speed. Data visualization has evolved into a rapidly combination of art and technology that will absolutely alter the corporate environment in the coming years.
Use of data Visualization
Data Visualization is useful because:
- To make learning and maintaining it easier.
- Unidentified facts, exceptions, and patterns must be discovered.
- To easily visualize patterns and correlations.
- It helps to ask better questions and provide more informed decisions.
- To do a market research
- To enhance perceptions.
How DataVisualization makes so effective?
Information, predictive analysis, and design mix to generate effective data visualization. Data visualizations transformed crucial insights from complex data sets into something understandable and attractive.
Accurate data visualizations, according to American statisticians and Yale professor Edward Tufte, include of “complicated ideas expressed with simplicity, reliability, and effectiveness.”
We must begin with clear, well-sourced, as well as full data to create an excellent data visualization. Only the data has been prepared for visualization, users must select the appropriate chart.
Once the chart has been finalized, we need to build and configure our visualization. Only the data has been prepared for visualization, users must select the appropriate chart.
There is no need to include anything that takes the focus away from the facts, so keep it as simple as possible.
Why data visualization is so important
It is important because analysis process in people’s minds. In contrast to examining spreadsheets and reports, using graphical representations to display a huge wide range of complex huge datasets seems to be more convenient. Data visualization is a simple and rapid technique to explain common thoughts. By making a small modification, we can try out a new pattern.
There are a few additional specializations in visualization techniques, such as:
- Data visualization can help discover areas that need to be improved or changed.
- The use of data visualization can help identify which factors have an impact on customer behavior.
- Data visualization assists in determining which products should be placed where.
- Sales figures could be predicted using data visualization.