Analyzing and Visualizing Data Chapter 6 Data Representation Introducing Visual Encoding Data representation is the act of giving visual form to your data. Viewers: When perceiving a visual display of data, it is decoded using the shapes, sizes, positions and colors to form an understanding Visualizers: Doing the reverse through visual encoding, assigning visual properties to data values Comprised of a combination of two properties Marks: Visible features like dots, lines and areas Attributes: Variations applied to the appearance of marks, such as size, position, or color. Introducing Visual Encoding cont. TBA Introducing Visual Encoding cont. TBA Introducing Visual Encoding cont. TBA Introducing Visual Encoding cont. TBA Introducing Visual Encoding cont. Marks and Attributes are the ingredients, a chart type is the recipe offering a predefined template for displaying data. Different chart types offer different ways of representing data. Introducing Visual Encoding cont. TBA Introducing Visual Encoding cont. TBA Introducing Visual Encoding cont. TBA Introducing Visual Encoding cont. Chart Types TBA Chart Types Exclusions Inclusions Categorical comparisons Dual families Text visualization Dashboard Small multiples A note about ‘storytelling’ Influencing Factors and Considerations TBA Influencing Factors and Considerations cont. TBA Influencing Factors and Considerations cont. TBA Influencing Factors and Considerations cont. TBA Influencing Factors and Considerations cont. TBA Influencing Factors and Considerations cont. TBA Influencing Factors and Considerations cont. TBA Influencing Factors and Considerations cont. TBA Influencing Factors and Considerations cont. TBA Influencing Factors and Considerations cont. TBA Influencing Factors and Considerations cont. TBA Influencing Factors and Considerations cont. TBA Analyzing and Visualizing Data Selecting a Graph Selecting a Graph Pie Charts Compare a certain sector to the total. Useful when there are only two sectors, for example yes/no or queued/finished. Instant understanding of proportions when few sectors are used as dimensions. When you use 10 sectors, or less, the pie chart keeps its visual efficiency. Selecting a Graph cont. Bar Charts/Plots Ordinal and nominal data sets Compare things between different groups or to track changes over time Measure change over time, bar graphs are best when the changes are larger Display and compare the number, frequency or other measure (e.g. mean) for different discrete categories of data Flexible chart type and there are several variations of the standard bar chart including horizontal bar charts, grouped or component charts, and stacked bar charts. Frequency for each category of a categorical variable Relative frequency (%) for each category Select.

1. Analyzing and Visualizing Data
Chapter 6
Data Representation
Introducing Visual Encoding
Data representation is the act of giving visual form to your data.
Viewers: When perceiving a visual display of data, it is decoded
using the shapes, sizes, positions and colors to form an
understanding
Visualizers: Doing the reverse through visual encoding,
assigning visual properties to data values
Comprised of a combination of two properties
Marks: Visible features like dots, lines and areas
Attributes: Variations applied to the appearance of marks, such
as size, position, or color.
Introducing Visual Encoding cont.
TBA

2. Introducing Visual Encoding cont.
TBA
Introducing Visual Encoding cont.
TBA
Introducing Visual Encoding cont.
TBA
Introducing Visual Encoding cont.
Marks and Attributes are the ingredients, a chart type is the
recipe offering a predefined template for displaying data.
Different chart types offer different ways of representing data.

3. Introducing Visual Encoding cont.
TBA
Introducing Visual Encoding cont.
TBA
Introducing Visual Encoding cont.
TBA
Introducing Visual Encoding cont.

4. Chart Types
TBA
Chart Types
Exclusions
Inclusions
Categorical comparisons
Dual families
Text visualization
Dashboard
Small multiples
A note about ‘storytelling’
Influencing Factors and Considerations
TBA

5. Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA

6. Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA

7. Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA

8. Analyzing and Visualizing Data
Selecting a Graph
Selecting a Graph
Pie Charts
Compare a certain sector to the total.
Useful when there are only two sectors, for example yes/no or
queued/finished.
Instant understanding of proportions when few sectors are used
as dimensions.
When you use 10 sectors, or less, the pie chart keeps its visual
efficiency.
Selecting a Graph cont.
Bar Charts/Plots
Ordinal and nominal data sets
Compare things between different groups or to track changes
over time

9. Measure change over time, bar graphs are best when the
changes are larger
Display and compare the number, frequency or other measure
(e.g. mean) for different discrete categories of data
Flexible chart type and there are several variations of the
standard bar chart including horizontal bar charts, grouped or
component charts, and stacked bar charts.
Frequency for each category of a categorical variable
Relative frequency (%) for each category
Selecting a Graph cont.
Histograms
It plots the frequencies that data appears within certain ranges.
Underlying frequency distribution (shape) of a set of continuous
data.
Underlying distribution (e.g., normal distribution), outliers,
skewness, etc
Plot the frequency of score occurrences in a continuous data set
that has been divided into classes
Selecting a Graph cont.
Box plots
Distribution of a continuous measure by some grouping variable
They measure the spread of the data, sort of like standard
deviation.
The line in the middle of the box is the median.

10. The box itself represents the middle 50% of the data.
The box edges are the 25th and 75th percentiles.
The vertical size of the boxes are the interquartile range, or
IQR.
The tops and bottoms of the boxes are referred to as “hinges”.
Whiskers: They represent the reasonable extremes of the data.
That is, these are the minimum and maximum values that do not
exceed a certain distance from the middle 50% of the data.
If no points exceed that distance, then the whiskers are simply
the minimum and maximum values.
Outliers, data points that are too big or too small compared to
the rest of the data.
Selecting a Graph cont.
Scatter plots
View the potential relationship of two continuous variables
Graphical view of the relationship between two sets of numbers.
Scatter plots show how much one variable is affected by
another.
The relationship between two variables is called their
correlation.
Find potential relationships between values, and to find outliers
in data sets.

11. Instructor: Dr. < Your Name >
Class: ISOL536-Security Architecture and Design
Assignment: Week 6 Individual Assignment 5
Length: Minimum of 600 words
Total points: 40 points
Due date: Sunday, February 16, 2020
Briefly respond to all the following questions. Make sure to
explain and backup your responses with facts and examples.
This assignment should be in APA format and have to include at
least two references.
Since it is so dangerous, why would designers install software
into the kernel at all (or make use of kernel software)? If you
were an antivirus designer or maker, what other methods do you
utilize to prevent virus?
Discussion 1
Data representation is the act displaying the visual form of your
data. The process of identifying the most effective and
appropriate solution for representing our data is unquestionably
the most important feature of our visualization design. Working
on this layer involves making decisions that cut across the
artistic and scientific foundations of the field.

12. Here we find ourselves face-to-face with the demands of
achieving that ideal harmony of form and function that was
outlined in Chapter 6, Data Representation. We need to achieve
the elegance of a design that aesthetically suits our intent and
the functional behavior required to fulfill the effective
imparting of information.
According to Kirk 2016, in order to dissect the importance
of data representation, we are going to "look at it from both
theoretical and pragmatic perspectives." Choose three of the
storytelling techniques (Pages 161 - 209) in which data is
presented and stories are being interpreted. Discuss the
importance and the advantages of using these techniques.
Provide an example of each technique.
Reference
Kirk, A. (2016). Data Visualisation: A Handbook for Data
Driven Design. Thousand Oaks, CA:Sage Publications, Ltd.
Requirements:
APA Format
400 Words
Subject: Analyzing and Visualizing Data
Discussion 2
Students will be required to create 1 new thread, and provide
substantive comments on at least 3 threads created by other
students. Make sure to explain and backup your responses with
facts and examples. This assignment should be in APA format
and have to include at least two references.
Your initial posting should be a minimum of 400 words
If the maker of antivirus software wants to be successful, the
software has to be as close to bulletproof as the maker can
possibly make it. Nothing is perfect; we certainly should
understand at this point that no software can be proven bug free

13. and that no security posture is 100% risk-free. Based on this
statement, what do you think it could be better to improve the
antivirus software? How safe do you feel to use antivirus
software in your organization, and what other precautions do
you use to prevent virus, malware, etc.?
TextBook:
Schoenfield, Brook S.E. – Securing Systems: Applied Security
Architecture and Threat Models, CRC Press, 2014 ISBN: 978-
1-4822-3397-1
Requirements:
APA Format
400 Words
Subject: Security Architecture and Design