The document provides an overview of data visualization, emphasizing its role in presenting data in a clear and understandable manner for end users. It discusses the requirements for effective visualization, types of charts, and best practices, highlighting the importance of accuracy and appropriate representation of data. Various chart types and the objectives of effective data visualization are outlined to help enhance data comprehension.

1. Assistant Professor Nandini Patil
Godutai Engg. College,kalaburagi
Data Visualization

2. Introduction
 Data Visualization is theart and science of making
data easy to understand and consume for the end
user.
 Shows right amount of the data, in the right order,
in the right visual form, to convey the high priority
information .
 Requirement- understanding
 consumer need
 nature of data
 Avaiable tools & technique to represent data
 Last step in the data life cycle

3. Excellence in Visualization
 Data can be presented in the form of rectangular tables or
in the colourfull graphs of various types.
 Tables – Small, non-comparative, highly-labeled data sets.
 Graphs – Amount of graph grows, big data, which will
helps to give shape to data.
 Objectives:
 Shows, and Even Reveal, the data
 Induce the viewer to think of the substances of the data.
 Avoid Distoring what the data have to say
 Make Large Datasets Coherent
 Encourage the eyes to compare different pieces of data
 Reveal the data at several levels of details
 Serve a reasonably clear purpose
 Closely integrate with the statistical and verbal descriptions of
the datasets

4. Types of Charts
Many kinds of data so there are many types of graphs,
popular chart are as follows
1. Line Graph
2. Scatter plot
3. Bar Graph
4. Stacked Bar graphs
5. Histograms
6. Pie charts
7. Box charts
8. Bubble Graph
9. Dial
10. Geographical data maps
11. Pictographs

5. Line Graph

6. Scatter plot

7. Bar Graph

8. Stacked Bar graphs

9. Histograms

10. Pie charts

11. Box charts

12. Bubble Graph

13. Dial

14. Geographical data maps

15. Pictographs

16. Visualization Example

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21. Tips for data visualization
 Fetch appropriate and correct data for analysis
 Sort the data in the most appropriate manner
 Choose appropriate method to present the data
 The dataset could be pruned
 The visualization could show additional
dimension for reference
 The numericaldata may need to be binned into
few categories
 High-level visualization could be backed by more
detailed analysis
 Need to present additional textual information