Making Abstract Data Visible

1. DATA
VISUALIZATION
(Making Abstract Data
Visible)
Submitted to: Submitted by:
Ms. Annu dhankar Priyanshi Jain (CSE/15/111)

2. Contents
• Introduction
• History
• Terminology used in Data Visualization
• Examples of Diagrams used for Data
Visualization
• Advantages
• Disadvantages
• Applications

3. Introduction
• Data visualization is a general term that describes any effort to
help people understand the significance of data by placing it in a
visual context.
• Patterns, trends and correlations that might go undetected in text-
based data can be exposed and recognized easier with data
visualization software.
• Today's data visualization tools go beyond the standard charts
and graphs used in Microsoft Excel spreadsheets, displaying data
in more sophisticated ways such as infographics, dials and
gauges, geographic maps, spark lines, heat maps, and
detailed bar, pie and fever charts.

4. History
• Michael Friendly and Daniel J Denis of York University are
engaged in a project that attempts to provide a comprehensive
history of visualization. Contrary to general belief, data
visualization is not a modern development. Stellar data, or
information such as location of stars were visualized on the
walls of caves.
• First documented data visualization can be tracked back to
1160 B.C. with Turin Papyrus Map which accurately illustrates
the distribution of geological resources and provides
information about quarrying of those resources.

5. Terminologies used
Author Stephen Few defines two types of data, which are used in
combination to support a meaningful analysis or visualization:
• Categorical:- Text labels describing the nature of the data, such as
"Name" or "Age". This term also covers qualitative (non-numerical) data.
• Quantitative:- Numerical measures, such as "25" to represent the age in
years.
Two primary types of information displays are tables and graphs :
• A table contains quantitative data organized into rows and columns with
categorical labels. It is primarily used to look up specific values.
• A graph is primarily used to show relationships among data and portrays values
encoded as visual objects (e.g., lines, bars, or points). Numerical values are
displayed within an area delineated by one or more axes.

6. Examples of Data Visualization
• Bar Chart
• Histogram
• Scatter Plot
• Scatter Plot (3D)
• Stream Graph
• Tree Map
• Gantt Chart
• Heat Map

7. • Bar Chart
A bar graph (also known as a bar chart or bar diagram) is a visual tool that
uses bars to compare data among categories. A bar graph may run horizontally or
vertically. The important thing to know is that the longer the bar, the greater its value.
Examples Usage:-
Comparison of values, such as sales performance for several persons or businesses in a
single time period.

8. • Histogram
A diagram consisting of rectangles whose area is proportional to the frequency of
a variable and whose width is equal to the class interval.
Examples Usage:-
Determining frequency of annual stock market percentage returns within particular
ranges (bins) such as 0-10%, 11-20%, etc.

9. • Scatter Plot
A graph in which the values of two variables are plotted along two axes, the
pattern of the resulting points revealing any correlation present.
Examples Usage:-
Determining the relationship (e.g., correlation).

10. • Scatter Plot (3D)
A 3D Scatter Plot (or a Cloud Plot) allows you to visualize the relationship between
three variables. The default view for a Multi-Variate result is a 2D Scatter Plot. A 3D
Scatter Plot looks like this: For the purpose of creating scatter plots, the variables
must be assigned to specific axes.

11. • Stream Graph
A stream graph is a type of stacked area graph which is displaced
around a central axis, resulting in a flowing, organic shape.

12. • Tree Map
A diagram representing hierarchical data in the form of nested rectangles, the area
of each corresponding to its numerical value.

13. • Gantt chart
A Gantt chart is a horizontal bar chart. Frequently used in project
management, a Gantt chart provides a graphical illustration of a schedule that
helps to plan, coordinate and track specific tasks in a project.
Examples :
Schedule / Progress in Project Planning

14. • Heat Map
A representation of data in the form of a map or diagram in which data values are
represented as colours.
Examples :
Analyzing risk, with green, yellow and red representing low, medium, and high risk,
respectively.

15. Advantages
1. Faster Action
• The human brain tends to process visual information far more easily than
written information.
• Use of a chart or graph to summarize complex data ensures faster
comprehension of relationships than cluttered reports or spreadsheets.
2. Communicate Findings in Constructive Ways
• Many business reports submitted to senior management are formalized
documents that are often inflated with static tables and a variety of chart
types.
• They become so elaborate that they fail to make information vibrant and
memorable for those whose opinions matter most.

16. 3. Understand Connections Between Operations and Results
• One benefit of data visualization is that it allows users to track connections
between operations and overall business performance.
• Finding a correlation between business functions and market performance is
essential in a competitive environment.
4. Interact With Data
• A chief benefit of data visualization is that it brings exposes changes in a timely
manner.
• But unlike static charts, interactive data visualizations encourage users to
explore and even manipulate the data to uncover other factors.

17. Disadvantages
1. Data visualization tools show but they don’t explain:
• While data visualizations can be generated in real-time, they do not provide
any explanations.
• In fact, the process through which companies draw insight has not changed
in the last 30 years. Analysts look at data and then write reports.
• This process is too slow for the market and too costly for the company.
2. Different users draw different insights:
• Two different users confronted with the same data visualization may not
necessarily draw the same conclusion, depending on their previous
experiences and particular level of expertise.
• This presents several problems for companies. On the one hand, certain
users could be erroneously drawing conclusions which cost the company
money and on the other, in highly regulated industries, users’ incorrect
conclusions could actually put the company at risk.

18. 3. No guidance:
• We don’t speak data, we speak English, so software that
explains the data to us in plain English.
• This is the value of Natural Language Generation software,
the last mile in the data analytics workflow.
4. Data visualization provides a false sense of security
• Graphics are great for conveying simple ideas fast – but
sometimes, they are just not enough
• Graphics can make users think they are making data driven
decisions or think they fully understand the data when in
reality they are only seeing a picture but they don’t know the
full story.

19. Applications
• Poly maps
• Flot
• Transforming
• SAS Visual Analytics
• Microsoft Excel
• R Project
• Networkx

20. THANK
YOU…