Data and Information Visualization Part 1part 1.pptx

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Lamees Mahmoud El-Ghazoly

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Dear fellow learners and researchers,
I want to express my deep passion for sharing knowledge and helping people find the information
they need. I believe that knowledge should be accessible to everyone, and by sharing it, we can
contribute to the advancement of our society.
However, I also want to emphasize the importance of creating your own research. While my work
may serve as a source of inspiration or a starting point for your own research, I urge you to
conduct your own exploration and draw your own conclusions. This not only ensures the
originality and authenticity of your work but also allows for the discovery of new ideas and
perspectives.
Let us all strive to create and share knowledge in an ethical and responsible manner.
By doing so, we can make a positive impact on our communities and the world.
Thank you for your attention, and I wish you all the best in your research and learning endeavors.
And remember Share is Care.
Sincerely,
Lamees El-Ghazoly.

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In our increasingly Data-driven world, it's more important than
ever to have accessible ways to view and understand data.
After all, employees' demand for data skills steadily increases
each year.
Employees and Business owners at every level need to
understand data and its impact.
That's where Data Visualization comes in handy.
To make Data more accessible and understandable, Data
Visualization in Dashboards is the go-to tool for many businesses
to Analyze and share Information.
Introduction

• Let’s briefly discuss the term “Data Science” because these two terms are interrelated. But
how? Let’s understand.
• So, in simple terms, 'Data Science' is the science of analyzing raw data using statistics and
machine learning techniques to conclude that information.
• Data Science is an umbrella that contain many other fields like Machine learning, Data
Mining, big Data, statistics, Data visualization, data analytics,… But do you know what Data
Science Pipeline is?
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Before jumping into the term “Data Visualization“
In simple words, a pipeline in data science is “a set of actions which changes the raw (and
confusing) data from various sources (surveys, feedback, list of purchases, votes, etc.) to an
understandable format so that we can store it and use it for Analysis.”

Fetching/Obtaining the Data
Scrubbing/Cleaning the Data
Data Visualization
Modeling the Data
Interpreting the Data
Revision
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The Raw Data undergoes different stages within a pipeline,
which are:

There are multiple types of data. Some more common types of data include the
following:
Single character
Boolean (true or false)
Text (string)
Number (integer or floating-point)
Picture
Sound
Video
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Let's start with: what is Data?
In general, Data is any set of characters that is gathered and translated for some purpose,
usually Analysis.
If Data is not put into context, it doesn't do anything to a human or computer.

Bar Charts
Pie Charts
Stacked Bar Charts
Grouped Bar Charts
Heatmap
Treemaps
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Visualizing Categorical Data
When working with Categorical Data, it is important to visualize your data in a way that
makes the patterns and relationships easy to understand. Here are some common ways to
visualize categorical data:

Bar charts
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Bar charts are a simple and effective way to visualize
categorical data.
They show the frequency or proportion of each category
by representing it as a bar.
The height of each bar represents the frequency or
proportion of that category.
 Pareto Chart
 Grouped Bar Chart
 Simple Bar Chart
 Stacked Bar Chart
 Histogram

Bar charts- Stacked Bar Chart
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In this Example the Return on Equity (ROE) for Oriental Weavers Carpet (ORWE), Each Million
of the company's total Equity generated 0.32, 0.30, 0.21, and 0.35 Million of EBT in 2018,
2019, 2020, and 2021 as the highest ROI ratios, respectively. Reflected an increase in
EBT.https://www.slideshare.net/lameesmahmoud1/orwe-financial-forecasting-and-
analysispdf

Bar charts- Pareto Chart
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A Pareto Chart is a Bar Chart that displays the relative frequency or proportion of different
categories in descending order of importance.
The categories are ordered from left to right according to their frequency or proportion, with the
most important category on the left and the least important category on the right.
The cumulative frequency or proportion is also displayed, making it easy to see which categories
contribute the most to the total.
Pareto Charts are often used to identify the most important causes of a problem or to prioritize
actions based on their impact.

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Example: We have taken the major defects related to the Metal fabrication and casting Process. And we
will analyze the contribution of defects among all by help of the Pareto principle (80/20 Rule). So, let’s
get started with two important examples, details are given below.
Defects Quantity Cumulative Total Cumulative in %
Fatigue 41 41 18%
High temp. defect 37 78 35%
Mechanical Property degradation 32 110 50%
Creep 28 138 62%
Env. Interaction 23 161 73%
Microstructural changes 19 180 81%
Wear 16 196 88%
Abrasive Wear 12 208 94%
fretting 9 217 98%
Erosion 5 222 100%
Now, we are supposed to calculate the Cumulative total and Cumulative percentage

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Now, we will plot the Pareto Chart to apply the 80/20 rule to know the 80% contribution among
the all defects.

Bar charts- Annotated Bar
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For Example: A Retail Company can use its sales
record to create visualizations that depict the
revenue earned in each region during a specific
period.
This comparison can help the company evaluate
performance and make informed decisions.
Annotated Bar
Annotated Bar by Queryon combines bar
chart functionality with customizable labels.
Help users pay attention to key data in a bar
chart.
It includes Stacked, Side-By-Side (Clustered) ,
and overlapping (a.k.a bar in bar.)
Overlapping bars help visualize the
comparison between two things when one is
inherently a part of the other.

Bar charts- Horizontal Bar Chart
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For Example: A Retail Company can use its sales record to create visualizations that depict Total
Revenue & The No. of Units Sold Over Sales Channel- All Region
This comparison can help the company evaluate performance and make informed decisions.

Pie Charts
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A Pie Chart is a circular chart that is used to display the relative proportions or percentages
of different categories or groups. The entire pie represents the total of the data, and each
slice of the pie represents a category or group.
The size of each slice is proportional to the percentage or fraction that it represents. Pie
charts are useful for displaying data that can be divided into a few distinct categories and
where the emphasis is on the relative proportions of each category.
They are often used in business, marketing, and finance to show market shares, sales figures,
or budget allocations.
To create a pie chart, you need to follow these steps:
1.Choose the data that you want to display in the pie chart and
calculate the percentage or fraction for each category.
2.Draw a circle and divide it into slices that correspond to the
percentages or fractions of each category.
3.The slices should be proportional to the percentage or fraction
that they represent.
4.Label each slice with the name of the category and its
percentage or fraction.
5.Add a title to the chart that describes the data that is being
displayed.

Pie Charts
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For Example: The following is a survey we conducted inside Cairo University to collect data for
a client who wants to open a restaurant on the university campus.https

Visualizing
Numeric Data
1. Discrete data: Discrete data
consists of values that can be
counted and are usually whole
numbers. Examples of discrete
data include the number of
children in a family, the number
of cars in a parking lot, or the
number of students in a
classroom. Discrete data is often
displayed using bar charts,
histograms, or frequency tables.
2. Continuous data: Continuous
data consists of values that can
be measured and can take on
any value within a range.
Examples of continuous data
include height, weight,
temperature, or time.
Continuous data is often
displayed using line
charts, scatter plots, or box plots.
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Numeric Data refers to data that consists of numerical values,
which can be measured or counted. Examples of numeric data
include age, height, weight, temperature, income, and stock
prices. To visualize numeric data, There are two types
of Numeric Data:
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Visualizing
Numeric Data
 Histogram
 Box plot
 Scatter plot
 Line chart
 Heatmap
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To visualize Numeric Data, there are several common
techniques:
Box plot
Heatmap
Scatter plot
Histogram

Box plot
The Box represents the
interquartile range (IQR), which
is the range of values between
the first quartile (Q1) and the
third quartile (Q3).
The whiskers represent the
range of values within 1.5
times the IQR from the box.
Outliers are plotted as
individual points outside the
whiskers.
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A Box Plot shows the distribution of Numeric Data by displaying the
median, quartiles, and outliers.
For Example: These data (in attached link) represent the
conditions in which these insects might be found during the
course of routine death investigations, which may be valuable to
understanding the conditions under which insects colonize
human remains; a goal of applied forensic entomology as it is
used in casework.
Boxplot of median ambient scene temperature (°C) by scene
location.
Boxplot illustrating the median ambient air temperature (°C) and
associated variation recorded near the decedent at scenes located
indoors and outdoors for forensic entomology cases over the
study period (January 2013–April 2016). (N = 192).
Insects and associated arthropods analyzed during medicolegal
death investigations in Harris County, Texas, USA: January 2013-
April 2016 | PLOS ONE

Box plot
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Boxplot of median ambient scene temperature (°C) by scene
location.
Boxplot illustrating the
median ambient air
temperature (°C) and
associated variation
recorded near the decedent
at scenes located indoors
and outdoors for forensic
entomology cases over the
study period (January
2013–April 2016). (N = 192).

Heatmap
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A Heatmap (aka heat map) depicts values for a main variable of
interest across two axis variables as a grid of colored squares.
The axis variables are divided into ranges like a bar
chart or histogram, and each cell’s color indicates the value of
the main variable in the corresponding cell range.
This example overlays a
heatmap on top of the map.
It includes buttons that allow
users to change the
appearance of the heatmap.
Source:
Heatmaps | MapsJavaS
cript API | Google for
Developers

Heatmap
The superheat function
requires that the data be in
particular format
Specifically
The Data most be all
numeric
The Row names are used
to label the left axis. If the
desired labels are in a
column variable, the
variable must be converted
to row names (more on this
below)
Missing values are
allowed.
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For Example: let’s create a heatmap for the mtcars dataset that come
with base R.
The mtcars (Motor Trend Car Road Tests) Dataset contains information
on 32 cars measured on 11 variables. sorted the rows so that cars that
are similar appear near each other. We will also adjust the text and label
sizes.
Here we can see that the Toyota Corolla and Fiat 128 have
similar characteristics. The Lincoln Continental and Cadillac
Fleetwood also have similar characteristics.

Scatter Plot
Scatter Plots give a visual
portrayal of the correlation,
or connection between the
two factors.
Types of Correlation
All correlations have two
properties: Direction and
Strength.
The Direction of the
correlation is controlled by
whether the correlation is
positive or negative.
The Strength of a correlation
is determined by its numerical
value.
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A scatter Plot is used to show the relationship between two
Numeric Variables.
Positive Correlation
Both variables move in the same direction. In other words, as
one variable increases, the other variable also increases. As one
variable decreases, the other variable also decreases.
Example: years of education and yearly salary
are positively correlated.
Negative correlation
The variables move in opposite directions. As one
variable increases, the other variable decreases. As one
variable decreases, the other variable increases.
Example: hours spent sleeping and hours spent awake
are negatively correlated.
Each point on the plot represents the value of one variable on the
x-axis and the value of the other variable on the y-axis.
The pattern of points on the plot can reveal the Strength and
Direction of the Relationship between the two variables.

Scatter Plot
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Examples of scatter plots
The below charts show different relationships between variables,
Strong Positive, Strong Negative

Line chart
Line charts can be
customized in various ways,
such as by adding labels,
colors, and markers to the
data points, or by adjusting
the scale of the axes to
highlight specific trends or
patterns.
They are a popular and
effective way to visualize
Data and communicate
insights to others.
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A line chart, also known as a Line Graph, is a type of chart
that displays data as a series of points connected by a line. It is
commonly used to show trends over time or to compare data sets.
To create a line chart, the data is plotted on a two-
dimensional coordinate system, with the x-axis representing
the independent variable (usually time) and the y-axis
representing the dependent variable.
Each data point is then plotted as a point on the graph, and a
line is drawn between the points to show the trend over time.
Line charts are often used in Business and Finance to show
trends in stock prices, sales figures, or other metrics over
time.
They can also be used in scientific research to display
experimental data or to compare the results of different
experiments.

Line chart
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https://www.slideshare.net/lameesmahmoud1/orwe-financial-
forecasting-and-analysispdf
In this Example the Liquidity
Ratios for Oriental Weavers
Carpet (ORWE), we have
compared the result of the
averages for four years in a
row as a tool for comparison,
as it is most appropriate
because there is no accurate,
updated, or weight data for
the industry averages. Also,
we cannot compare the
organizations with each
other. For example, the
different accounting
standards applied make the
comparison incorrect.

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Arguably the first data
visualizations were in the
field of cartography.
In 1569, the Flemish
cartographer Gerardus
Mercator’s map of the
world marked a major
development in how we
depict the surface of the
spherical Earth on a flat
piece of paper.
Many of the visualization techniques of today were invented during the industrial revolution, with the
field making large strides in the mid-19th century. What may seem simple and obvious today, such as a
bar chart or line graph, would have been strange and unfamiliar to someone 200 years ago.
Map-makers
in 1765, along came the
above timeline chart from
Joseph Priestley, showing
the overlapping lifetimes
of various classical
statesmen. The chart
shows the balance of
trade for Scotland with
various territories in
Europe and the New
World.
Fifteen years later, he
was at it again, this time
with the sometimes
controversial pie chart
and various creative
combinations.
Building on the ideas of
Playfair, she incorporated
charts into many of her
publications and is
credited with the
invention of the Polar
Area Chart, or “Coxcomb”.
Florence nightingale
A Short History of Data Visualization

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The representation and presentation of data that exploits our visual perception abilities in
order to amplify cognition is called visual data analysis. This approach uses visualizations to
communicate complex information and patterns in a way that is easily understandable and
memorable.
Our visual perception abilities are well-suited for identifying patterns, trends, and outliers in
large data sets. For example, our ability to quickly recognize shapes, colors, and spatial
relationships can be leveraged to create visualizations that highlight important features of
the data.
What is Data Visualisation?

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Google Insights: Keyword Infographic
Our visual perception abilities are well-suited for identifying patterns, trends, and outliers in
large data sets. For example, our ability to quickly recognize shapes, colors, and spatial
relationships can be leveraged to create visualizations that highlight important features of the
data.
Popularity

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8 hats of data visualisation Design
The final scoping issue to consider at this stage of your visualization design project is an
assessment of your personal capabilities and those of any collaborators that you involve in
the work.
Data visualization can be approached from different perspectives or "hats," each
emphasizing a different aspect of the visualization process. Here are some of the most
common hats of data visualization:
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Initiator
Data
Scientist
Journalist
Computer
Scientist
Designer
Cognitive
Scientist
Project
Manager
User
experience
(UX)
designer

Initiator
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The initiator is an important aspect of the visualization process as it refers to the
person or group responsible for initiating the visualization project and defining its
goals and objectives.
They must identify the target audience, the data to be visualized, the desired
outcomes, and the constraints of the project, such as budget, timeline, and
resources.
 Brief: Open, strict, helpful, unhelpful
 Format: Static, interactive, video
 Audience size: One, group, www
 Audience type: Domain experts, general
 Resolution: High level, detail, exploratory
Questions/Inform
Learn/Increase knowledge.
Change behavior
Answer
Analyze data
Persuade
Enlighten
Tell a story
Trigger questions
Fun/Play
Shape opinion
Find patterns/Find no Patterns
Familiarize with data
Interact
Assist decisions
Shock/Make an impact
Art/Aesthetic
Emphasize issues
Serendipitous discoveries
Contextualize data
pleasure
Inspire

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This hat emphasizes the statistical and Machine Learning aspects of visualization. The
data scientist uses visualization to explore and analyse complex data sets, and to
communicate the results of statistical models and algorithms.
The „Data Miner‟ – acquires the data Addresses the data for quality Prepares the
data for its purpose Enhances and consolidate the data Strong statistical knowledge
Undertakes initial descriptive analysis Undertakes exploratory visual analysis
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Data Scientist

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Journalists are an important aspect of the visualization process, as they often use
data visualizations to tell stories and communicate complex information to their
audience.
Journalists may work with Data Analysts, Designers, and Developers to create
visualizations that support their reporting and help their audience understand the
context and implications of the stories they are telling.
Journalists
Able to work effectively with other stakeholders
Communicate
Familiarize with data
Easy to understand.
Identify relevant data sources
Tell a story

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Journalists
What stories should users/readers be able to derive
from this visualisation?

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The „Executor‟ – brings the project alive. Has the Critical Technical Capability Acquires, handles
and Analyses Data, Technical illustration skills, and Technical programming skills.
Computer Scientist
Able to adapt to changing
Expertise in
Software
Development
Processes &
Tools
DevOps
Git
Agile
Familiar
with
Visualization
Libraries
DevOps
Git
Agile
Programmin
g Languages
Python
Java
R
Technologies & Methodologies

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Computer scientists may be responsible for the development of custom software tools
and libraries that enable the creation and manipulation of Data Visualizations.
They may also be responsible for the development of Algorithms that enable the
analysis and processing of large and complex data sets, and for the implementation of
data structures that optimize the performance of the visualization.
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Computer Scientist

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That emphasizes the visual design aspects of visualization. The designer works on the
layout, color, typography, and other design elements of the visualization, with the goal
of making it aesthetically appealing and easy to understand.
Designer
Saturation
Rationalizes and reasons design options
Understands the message
Balances form and function
Understands the possibilities
Communicate
Length
Radius/Diameter
Position
Slope
Height
Blur/Focus
Orientation Speed
Shape
Color
Luminescence

Designer The data visualization anatomy;
 Data representation layer
 Colour and background layer
 Animation and interaction layer
 Layout, placement and apparatus layer
 The annotation layer
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Cognitive Scientist
Cognitive scientists have played a key role in developing effective data visualization techniques
that enable individuals to quickly and accurately interpret data, leading to more informed
decision-making.
Involves Visualization in the creation and manipulation of mental images, which can be used
to enhance learning, problem-solving, and Decision-making.
 Acts at the client-designer gateway
 Manage expectations
 Present possibilities
 Launch and publicize
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Cognitive Scientist
The „Thinker‟ – visual perception knowledge
Knows how the eye and brain work
Understands principles like „Gestalt Laws‟
Colour theories, HCI
Memory, attention, decision making
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Cognitive Scientist
Visible pixels on left graph: blue = 82% orange =18%
Visible pixels on right graph: blue = 91% orange= 9%
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User experience (UX) designer
This hat emphasizes the user experience and usability aspects of visualization.
The UX designer works on making the visualization easy to use, engaging, and interactive, with
the goal of maximizing user engagement and understanding.
Deep knowledge of common web-based technologies
like HTML, XML, JavaScript, and others
Very well aware of modern designing trends and tools
wireframe tasks and tools
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The website wireframe connects the underlying conceptual structure, or
information architecture, to the surface, or visual design of the website
Wireframes help establish functionality and the relationships between
different screen templates of a website.
wireframe tool
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