Data and information visualization (data viz/vis or info viz/vis)[2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount[3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain domain of expertise, these visualizations are intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data (exploratory visualization).[4][5][6] When intended for the general public (mass communication) to convey a concise version of known, specific information in a clear and engaging manner (presentational or explanatory visualization),[4] it is typically called information graphics. Data visualization is concerned with visually presenting sets of primarily quantitative raw data in a schematic form. The visual formats used in data visualization include tables, charts and graphs (e.g. pie charts, bar charts, line charts, area charts, cone charts, pyramid charts, donut charts, histograms, spectrograms, cohort charts, waterfall charts, funnel charts, bullet graphs, etc.), diagrams, plots (e.g. scatter plots, distribution plots, box-and-whisker plots), geospatial maps (such as proportional symbol maps, choropleth maps, isopleth maps and heat maps), figures, correlation matrices, percentage gauges, etc., which sometimes can be combined in a dashboard.

1. DATA VISUALIZATION
NAME: SAMARPIT NANDANWAR
ROLL NO. : 48
DEPARTMENT : CSE
GUIDED BY : PROF. P.D. KAWARE
HVPM COET
A Presentation

2. CONTENT
• Introduction
• What is Data?
• What is Visualization?
• What is Data Visualization?
• Categories of Data Visualization
• Types of Data Visualization
• Visualization Tools
• Data Visualization Best Practices
• 7 Steps in Data Visualization
• Example of Data Visualization
• Benefits
• Drawbacks
• Conclusion

3. INTRODUCTION
• Data Visualization is a Graphical Representation of any data or information.
• Visual Elements such as Charts, Graphs, and Maps are few Data Visualization Tools that provide the
user to understand the data.
• In this world of Big Data, Data Visualization helps you to make decision, study the Data properly and
Understand the concepts properly.
• Why is Data Visualization Important :
• Easily Graspable
• Establish Relationships
• Interactive Visualization

4. WHAT IS DATA ?
• Data is a collection of information gathered by
observations, measurements, research or analysis.
• Data is organized in the form of graphs, charts or
tables.
• Data can be of many types :-
• Video
• Music
• Images, etc.

5. WHAT IS VISUALIZATION ?
• Visualization is any technique for creating images, diagrams,
or animations to communicate a message.
• Use bar charts to compare items between different groups,
measure changes over time and identify patterns or trends.

6. WHAT IS DATA VISUALIZATION ?
• Data visualization is the representation of data through use of
common graphics, such as charts, plots, infographics, and
even animations.
• Visual displays of information communicate complex data
relationships and data-driven insights in a way that is easy to
understand.
• Data Visualization is categorize into four key purposes :-
• Idea generation
• Idea illustration
• Visual discovery
• Data Visualization

7. IDEA GENERATION :
• Idea Generation is the act of forming ideas.
• Data visualization is commonly used to encourage idea
generation across teams.
• They are frequently manipulated during Design
Thinking sessions at the start of a project.

8. IDEA ILLUSTRATION :
• A drawing, diagram or picture in a book or magazine.
• Data Visualization for Idea illustration help to make ideas,
thoughts, feelings, etc. known to somebody.
• It is used to represent organization structures or processes,
facilitating communication between the right individuals for
specific tasks.
• Project managers frequently use Gantt charts and waterfall
charts to illustrate workflows.

9. VISUAL DISCOVERY :
• Visual discovery helps data analysts, data scientists,
and other data professionals identify patterns and
trends within a dataset.
• Visual discovery and every day data viz are more
closely aligned with data teams.

10. DATA VISUALIZATION :
• Data visualization is a critical step in the data science
process, helping teams and individuals convey data
more effectively to colleagues and decision makers.

11. TYPES OF DATA VISUALIZATION
Tables Pie Charts Line Charts Histograms Scatter Plots Tree Maps

12. TABLES
• This consists of rows and columns used to compare
variables.
• Tables can show information in a structured way.
• Tables can be a disadvantage for users which are
looking for high level trends.

13. PIE CHARTS
• These graphs are divided into sections that represent
parts of a whole.
• They provide a simple way to organize data and compare
the size of each component to one other.

14. LINE CHARTS
• These visuals show change in one or more quantities by
plotting a series of data points over time and are
frequently used within predictive analytics.
• Line graphs utilize lines to demonstrate these changes.

15. HISTOGRAMS
• This graph plots a distribution of numbers using a bar chart
(with no spaces between the bars), representing the
quantity of data that falls within a particular range.
• This visual makes it easy for an end user to identify outliers
within a given dataset.

16. SCATTER PLOTS
• These visuals are beneficial in revealing the
relationship between two variables, and they are
commonly used within regression data analysis.
• These can sometimes be confused with bubble
charts, which are used to visualize three variables via
the x-axis, the y-axis, and the size of the bubble.

17. TREE MAPS
• Display hierarchical data as a set of nested shapes,
typically rectangles.
• Tree maps are great for comparing the proportions
between categories via their area size.

18. VISUALIZATION TOOLS
D3.js
Vega
E-Charts

19. DATA VISUALIZATION BEST PRACTICES
Choose an
effective
visual
Keep it Simple
Know your
Audiences

20. 7 STEPS IN DATA VISUALIZATION
Develop
your
research
question
Create your
data
Clean your
data
Choose a
Chart Type
Choose
your tool
Prepare
Data
Create
Chart

21. EXAMPLE : WORLD POPULATION AT 8 BILLION

22. BENEFITS OF DATA VISUALIZATION
• Enhanced Understanding
• Improved Decision-Making
• Identifying Patterns and Trends
• Increased Efficiency:
• Effective Communication:

23. DRAWBACKS OF DATA VISUALIZATION
• Limited Context
• Data Quality and Integrity
• Time and Skill Requirements
• Data Security and Privacy Concerns

24. CONCLUSION
• Data Visualization is an important practice that allows for clear and effective communication of data
through use of graphics.
• Makes Data understandable and support decision making.
• Allows Decision makers to identify patterns and trends in data.

25. THANK
YOU !
HVPM COET