10 Types of Data Visualization Explained

So let’s check them out!

1. Tableau. Tableau is a data visualization tool that can be used by data analysts, scientists, statisticians, etc. to visualize the data and get a clear opinion based on the data analysis.
2. Looker.
3. Zoho Analytics.
4. Sisense.
5. IBM Cognos Analytics.
6. Qlik Sense.
7. Domo.
8. Microsoft Power BI.

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

1. Column Chart. This is one of the most common types of data visualization tools.
2. Bar Graph.
3. Stacked Bar Graph.
4. Line Graph.
5. Dual-Axis Chart.
6. Mekko Chart.
7. Pie Chart.
8. Scatter Plot.

Best Data Visualization Techniques for small and large data

• Bar Chart. Bar charts are used for comparing the quantities of different categories or groups.
• Pie and Donut Charts.
• Histogram Plot.
• Scatter Plot.
• Visualizing Big Data.
• Box and Whisker Plot for Large Data.
• Word Clouds and Network Diagrams for Unstructured Data.
• Correlation Matrices.

1. 5 tips for visualizing concepts. Francis Gagnon.
2. Use simple shapes. The fundamental principle of design “keep it simple” applies here.
3. Leave out entire ideas. It’s common to have to illustrate a complex idea with several propositions.
4. Find the essence of an idea.
5. Use abstraction.
6. Data illustration is ok.

VizSweet

r/dataisbeautiful, also known as Data Is Beautiful, is a subreddit dedicated to aesthetically pleasing works of data visualization. It was created in 2012; as of January 2020, it had over 14 million members.

Just as Excel can perform basic data analysis functions, it has a surprising number of data visualization tools under the hood. “Excel isn’t explicitly a data visualization tool, it’s a spreadsheet,” says Excel developer and consultant Jon Peltier. However, one of Excel’s strengths is its flexibility, he adds.

A 5-step guide to data visualization

1. Step 1 — Be clear on the question.
2. Step 2 — Know your data and start with basic visualizations.
3. Step 3 — Identify messages of the visualization, and generate the most informative indicator.
4. Step 4 — Choose the right chart type.
5. Step 5 — Use color, size, scale, shapes and labels to direct attention to the key messages.