The document discusses various data visualization techniques for encoding data using color and size. It provides examples of how to use color and size to represent categorical, sequential, and quantitative data. Specific chart types discussed include stacked bar charts, grouped bar charts, stacked area charts, streamgraph line charts, and grouped histograms. Guidelines are provided for effective use of color and size, such as using distinct colors to represent categories, employing color gradients to show progression, and varying size to indicate magnitude.
Data visualization of Big Data analytics nandini patil
Data Visualization is the art and science of making data easy to understand and consume for the end-user. It is the last part of the Data life cycle of data analytics. ppt is based on book Data analytics written by Anil Maheshwari
Data Visualization: A Powerful Tool for Insightful Analysis | CyberPro Magazinecyberprosocial
In today's world, where data is everything, data visualization is like a superpower for businesses, researchers, and analysts. It's all about taking boring raw data and turning it into cool pictures
DATA VISUALIZATION FOR MANAGERS MODULE 3| Building Visualization| BUSINESS ANALYTICS PAPER 1 |MBA SEM 3| RTMNU NAGPUR UNIVERSITY| BY JAYANTI R PANDE
MBA Notes by Jayanti Pande
#JayantiPande
#MBA
#MBAnotes
#BusinessAnalyticsNotes
The Gauge & Widget Advantage for your DashboardFusionCharts
Learn how widgets and gauges including the speedometer chart, bulb gauge, sparklines and bullet graphs help you monitor your key metrics in business dashboards — current sales vs target, average order value, current stock levels and more. Also learn usability tips right from color selection to how to add more context to the widgets in this presentation.
Data visualization of Big Data analytics nandini patil
Data Visualization is the art and science of making data easy to understand and consume for the end-user. It is the last part of the Data life cycle of data analytics. ppt is based on book Data analytics written by Anil Maheshwari
Data Visualization: A Powerful Tool for Insightful Analysis | CyberPro Magazinecyberprosocial
In today's world, where data is everything, data visualization is like a superpower for businesses, researchers, and analysts. It's all about taking boring raw data and turning it into cool pictures
DATA VISUALIZATION FOR MANAGERS MODULE 3| Building Visualization| BUSINESS ANALYTICS PAPER 1 |MBA SEM 3| RTMNU NAGPUR UNIVERSITY| BY JAYANTI R PANDE
MBA Notes by Jayanti Pande
#JayantiPande
#MBA
#MBAnotes
#BusinessAnalyticsNotes
The Gauge & Widget Advantage for your DashboardFusionCharts
Learn how widgets and gauges including the speedometer chart, bulb gauge, sparklines and bullet graphs help you monitor your key metrics in business dashboards — current sales vs target, average order value, current stock levels and more. Also learn usability tips right from color selection to how to add more context to the widgets in this presentation.
Developing Competitive Strategies in Higher Education through Visual Data MiningGurdal Ertek
Information visualization is the growing field of computer science that aims at visually mining data for knowledge discovery. In this paper, a data mining framework and a novel information visualization scheme is developed and applied
to the domain of higher education. The presented framework consists of three main types of visual data analysis: Discovering general insights, carrying out competitive
benchmarking, and planning for High School Relationship Management (HSRM). In this paper the framework and the square tiles visualization scheme are described and an application at a private university in Turkey with the goal of attracting brightest students is demonstrated.
http://research.sabanciuniv.edu.
The use of data visualization to tell effectivegentlemoro
Data usually represents unprocessed numbers, pictures or statements; information is typically the result of analyzing or processing the data. Data are usually collected in a raw format and thus the inherent information is difficult to understand. Therefore, raw data need to be summarized, processed and analyzed. These days, data are often summarized, organized, and analyzed with statistical packages or graphics software. Data must be prepared in such a way they are properly recognized by the program being used.No matter how well manipulated, the information derived from the raw data should be presented in an effective format, otherwise, it would be a great loss for both authors and readers.
EFFECTIVE SEARCH OF COLOR-SPATIAL IMAGE USING SEMANTIC INDEXINGIJCSEA Journal
Most of the data stored in libraries are in digital form will contain either pictures or video, which is tough to search or browse. Methods which are automatic for searching picture collections made large use of color histograms, because they are very strong to wide changes in viewpoint, and can be calculated trivially. However, color histograms unable to present spatial data, and therefore tend to give lesser results. By using combination of color information with spatial layout we have developed several methods, while retrieving the advantages of histograms. A method computes a given color as a function of the distance between two pixels, which we call a color correlogram. We propose a color-based image descriptor that can be used for image indexing based on high-level semantic concepts. The descriptor is
based on Kobayashi’s Color Image Scale, which is a system that includes 130 basic colors combined in 1180 three-color combinations. The words are represented in a two dimensional semantic space into groups based on perceived similarity. The modified approach for statistical analysis of pictures involves transformations of ordinary RGB histograms. Then a semantic image descriptor is derived, containing semantic data about both color combinations and single colors in the image.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A novel tool for stereo matching of imageseSAT Journals
Abstract Stereo matching techniques play an important role in many real world applications like robot stereo vision and image sequence analysis. From given pair of stereo pairs of images, it is possible to have matching techniques to obtain image descriptors or phenomena to compare the images. The goal of stereo matching can be achieved using either relational matching or feature or signal. However, the signal approach is most widely used. Recently Lemmens [10] provided a comprehensive review of many stereo matching techniques. In this paper we implement the techniques that can help in the real world. We build a prototype application that demonstrates the proof of concept. The empirical results revealed that the proposed application has good utility. Keywords – Stereo images, stereo matching,
RECOMMENDED ELEMENTS OF INFOGRAPHICS IN EDUCATION (PROGRAMMING FOCUSED)ijma
ABSTRACT
This study focused on investigating the elements of infographics in the field of education especially in Programming. It was done by reviewing related literature reviews, interviewing experts in design, content, and the current infographics in programming. The findings showed that based on literature review a good infographic should consist of a good title, suitable graphs/charts/pictures/images, readable text/font, a clear story, reliable data, have an excellent use of color and an appropriate design format. Based on six design experts stated that the position, location, and identification of each element in infographics design to make it clear to the audience. Furthermore, content expert explained some important points of data structure and algorithms. The last one is taken from 6 current infographics which contained 7 elements. This is important to enhance the reader’s understanding of the content of the infographic because it should present information in a clear, concise, and effective manner.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Multidimensional schema for agricultural data warehouseeSAT Journals
Abstract Agriculture is one of the important issues for a nation’s economy and it required technical breakthroughs in this century. With today’s computerized world, the agricultural data processing is an increasing need for formers and decision makers. Agricultural data is diversified, complex and non-standard. Developing a data warehouse for agricultural is a key challenge for researchers. The objective of this work is to design a data warehouse about crops and their requirements. The proposed data warehouse may be extended to Decision Support System (DSS) combine with data mining techniques. We proposed a multidimensional data warehouse for agriculture that provides solutions for farmers and gives response of their ad-hoc quires. This multidimensional schema further promotes star schema and snowflake schema that are commonly used to design data warehouses. In our manuscript normalization is applied to store the data in to star schema and duplicate values are removed, so that space and time complexities could be minimized. Index Terms: Agriculture, Data Warehouse, Multidimensional Schema, Dimensional Modeling, OLAP
RECOMMENDED ELEMENTS OF INFOGRAPHICS IN EDUCATION (PROGRAMMING FOCUSED) ijma
This study focused on investigating the elements of infographics in the field of education especially in
Programming. It was done by reviewing related literature reviews, interviewing experts in design, content,
and the current infographics in programming. The findings showed that based on literature review a good
infographic should consist of a good title, suitable graphs/charts/pictures/images, readable text/font, a
clear story, reliable data, have an excellent use of color and an appropriate design format. Based on six
design experts stated that the position, location, and identification of each element in infographics design
to make it clear to the audience. Furthermore, content expert explained some important points of data
structure and algorithms. The last one is taken from 6 current infographics which contained 7 elements.
This is important to enhance the reader’s understanding of the content of the infographic because it should
present information in a clear, concise, and effective manner.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Developing Competitive Strategies in Higher Education through Visual Data MiningGurdal Ertek
Information visualization is the growing field of computer science that aims at visually mining data for knowledge discovery. In this paper, a data mining framework and a novel information visualization scheme is developed and applied
to the domain of higher education. The presented framework consists of three main types of visual data analysis: Discovering general insights, carrying out competitive
benchmarking, and planning for High School Relationship Management (HSRM). In this paper the framework and the square tiles visualization scheme are described and an application at a private university in Turkey with the goal of attracting brightest students is demonstrated.
http://research.sabanciuniv.edu.
The use of data visualization to tell effectivegentlemoro
Data usually represents unprocessed numbers, pictures or statements; information is typically the result of analyzing or processing the data. Data are usually collected in a raw format and thus the inherent information is difficult to understand. Therefore, raw data need to be summarized, processed and analyzed. These days, data are often summarized, organized, and analyzed with statistical packages or graphics software. Data must be prepared in such a way they are properly recognized by the program being used.No matter how well manipulated, the information derived from the raw data should be presented in an effective format, otherwise, it would be a great loss for both authors and readers.
EFFECTIVE SEARCH OF COLOR-SPATIAL IMAGE USING SEMANTIC INDEXINGIJCSEA Journal
Most of the data stored in libraries are in digital form will contain either pictures or video, which is tough to search or browse. Methods which are automatic for searching picture collections made large use of color histograms, because they are very strong to wide changes in viewpoint, and can be calculated trivially. However, color histograms unable to present spatial data, and therefore tend to give lesser results. By using combination of color information with spatial layout we have developed several methods, while retrieving the advantages of histograms. A method computes a given color as a function of the distance between two pixels, which we call a color correlogram. We propose a color-based image descriptor that can be used for image indexing based on high-level semantic concepts. The descriptor is
based on Kobayashi’s Color Image Scale, which is a system that includes 130 basic colors combined in 1180 three-color combinations. The words are represented in a two dimensional semantic space into groups based on perceived similarity. The modified approach for statistical analysis of pictures involves transformations of ordinary RGB histograms. Then a semantic image descriptor is derived, containing semantic data about both color combinations and single colors in the image.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A novel tool for stereo matching of imageseSAT Journals
Abstract Stereo matching techniques play an important role in many real world applications like robot stereo vision and image sequence analysis. From given pair of stereo pairs of images, it is possible to have matching techniques to obtain image descriptors or phenomena to compare the images. The goal of stereo matching can be achieved using either relational matching or feature or signal. However, the signal approach is most widely used. Recently Lemmens [10] provided a comprehensive review of many stereo matching techniques. In this paper we implement the techniques that can help in the real world. We build a prototype application that demonstrates the proof of concept. The empirical results revealed that the proposed application has good utility. Keywords – Stereo images, stereo matching,
RECOMMENDED ELEMENTS OF INFOGRAPHICS IN EDUCATION (PROGRAMMING FOCUSED)ijma
ABSTRACT
This study focused on investigating the elements of infographics in the field of education especially in Programming. It was done by reviewing related literature reviews, interviewing experts in design, content, and the current infographics in programming. The findings showed that based on literature review a good infographic should consist of a good title, suitable graphs/charts/pictures/images, readable text/font, a clear story, reliable data, have an excellent use of color and an appropriate design format. Based on six design experts stated that the position, location, and identification of each element in infographics design to make it clear to the audience. Furthermore, content expert explained some important points of data structure and algorithms. The last one is taken from 6 current infographics which contained 7 elements. This is important to enhance the reader’s understanding of the content of the infographic because it should present information in a clear, concise, and effective manner.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Multidimensional schema for agricultural data warehouseeSAT Journals
Abstract Agriculture is one of the important issues for a nation’s economy and it required technical breakthroughs in this century. With today’s computerized world, the agricultural data processing is an increasing need for formers and decision makers. Agricultural data is diversified, complex and non-standard. Developing a data warehouse for agricultural is a key challenge for researchers. The objective of this work is to design a data warehouse about crops and their requirements. The proposed data warehouse may be extended to Decision Support System (DSS) combine with data mining techniques. We proposed a multidimensional data warehouse for agriculture that provides solutions for farmers and gives response of their ad-hoc quires. This multidimensional schema further promotes star schema and snowflake schema that are commonly used to design data warehouses. In our manuscript normalization is applied to store the data in to star schema and duplicate values are removed, so that space and time complexities could be minimized. Index Terms: Agriculture, Data Warehouse, Multidimensional Schema, Dimensional Modeling, OLAP
RECOMMENDED ELEMENTS OF INFOGRAPHICS IN EDUCATION (PROGRAMMING FOCUSED) ijma
This study focused on investigating the elements of infographics in the field of education especially in
Programming. It was done by reviewing related literature reviews, interviewing experts in design, content,
and the current infographics in programming. The findings showed that based on literature review a good
infographic should consist of a good title, suitable graphs/charts/pictures/images, readable text/font, a
clear story, reliable data, have an excellent use of color and an appropriate design format. Based on six
design experts stated that the position, location, and identification of each element in infographics design
to make it clear to the audience. Furthermore, content expert explained some important points of data
structure and algorithms. The last one is taken from 6 current infographics which contained 7 elements.
This is important to enhance the reader’s understanding of the content of the infographic because it should
present information in a clear, concise, and effective manner.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
2. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 2
UNIT IV : REPORTS FORMATTING AND DATA REDUCTION
Using Color and Size in Visualization – Encoding Data using Color Encoding Data using Size Stacked
and Grouped Bar Chart – Stacked Area Chart and Streamgraph Line Chart with Multiple Lines –
Histograms Aggregating Data with Group – By Hexbin Mapping Cross filtering – Building a Migrant
Deaths Dashboard – Reports Vs Dashboards
CO4: Experiment to build interactive / animated dashboards construct data stories and
communicate important trends/ patterns in the datasets
3. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 3
Use of Color in Data Visualization:
To enhance the understanding of data, highlight patterns, and convey insights to the audience.
Remember that while color is a powerful tool, it should be used thoughtfully and intentionally.
several ways color can be used effectively in data visualization:
1. Categorical Data:
Use distinct colors to represent different categories or groups within our data. This makes it easy to
differentiate between various elements.
For example, in a bar chart comparing sales across different regions, can assign a unique color to each
region.
4. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 4
2. Sequential Data:
Employ a gradient color palette to show the progression of data from low to high values.
This is suitable for representing ordered data like temperature or time.
A map illustrating population density using different shades of a color (light to dark) is an example of
using sequential color.
3. Diverging Data:
Utilize a divergent color scheme to represent data that has a central point of reference, such as positive
and negative values relative to a mean or midpoint.
This can be effective in visualizing changes in sentiment or comparing performance against a baseline.
5. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 5
4. Heatmaps:
Employ a color scale to represent data density in heatmaps.
Darker shades indicate higher density or intensity, while lighter shades indicate lower density.
Heatmaps are often used in biology, finance, and geospatial analysis to visualize patterns in large
datasets.
5. Highlighting Data:
Use contrasting or bold colors to highlight specific data points, trends, or outliers within visualization.
This technique draws the viewer's attention to crucial information.
6. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 6
6. Color Coding:
Color-code elements consistently throughout our visualization to maintain clarity and consistency.
For example, to represent different types of products, use the same color for each type across different
charts.
7. Creating Visual Hierarchies:
Vary color intensity or saturation to create visual hierarchies.
More intense colors can indicate primary elements, while less intense colors can represent secondary
or supporting elements.
7. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 7
8.Combining Color and Texture:
In cases where color alone might not be sufficient (e.g., colorblindness), use textures or patterns
in conjunction with color to convey information.
This enhances accessibility and ensures that more people can interpret visualization accurately.
9.Multi-Dimensional Data:
When dealing with data that has multiple dimensions, can use color to represent one dimension
while size or shape represents another.
This allows for complex data visualization without overwhelming the viewer.
8. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 8
10.Time Series Analysis:
Use a consistent color scheme for data points over time to make it easier to follow trends and
changes.
Also create animated visualizations where color changes as time progresses.
11.Storytelling and Narration:
Use color changes strategically to guide the viewer's attention through a narrative or sequence of
events in the data.
9. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 9
12.Brand Consistency:
If data visualization is part of a larger presentation or report, consider using colors consistent
with brand to maintain a unified look.
10. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 10
Rules for Optimal use of Color in Data Visualization:
1. Use color when you should, not when you can
2. Utilize color to group related data points
3. Use Categorical colors for unrelated data
4. Categorical colors have few easily discernible bins
5. Change in chart type can often reduce the need for colors
6. When not to use sequential color scheme
7. Choose appropriate background
8. Not everyone can see all colors
11. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 11
1.Use color when you should, not when you can:
Use of color should be carefully strategized to communicate key findings and this decision, therefore,
cannot be left for automated algorithms to make. Most data should be in neutral colors like grey with
bright colors reserved for directing attention to significant or atypical data points.
12. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 12
2.Utilize color to group related data points :
Color can be used to group data points of similar value and to render the extent of this similarity using the
following two color palettes :
a. A sequential color palette is composed of varying intensities of a single hue of color at uniform saturation.
Variability in luminance of adjacent colors corresponds to the variation in data values that they are used to
render.
b. A divergent color palette is made of two sequential color palettes (each of a different hue) stacked next to
each other with an inflection point in the middle. These become helpful when visualizing data with variations
in two different directions.
13. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 13
3.Use Categorical colors for unrelated data:
Categorical color palettes are derived from colors of different hues but uniform saturation and
intensity and can be used to visualize unrelated data points of completely dissimilar origin or
unrelated values.
4.Categorical colors have few easily discernible bins : While the use of different colors can help
distinguish between different data points, a chart should at most comprise of 6–8 distinct color
categories for each of those to be readily distinguishable.
14. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 14
5.Change in chart type can often reduce the need for colors :
A pie chart probably is not the best option in the previous example. The resulting loss of categories
may not always be acceptable.
6.When not to use sequential color scheme : For the subtle difference in color of a sequential
palette to be readily apparent, these colors must be places right next to each other like in the chart
on the left below.
15. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 15
7.Choose appropriate background : How our perception of color of the moving square changes
with changes in its background. The human perception of colors is not absolute. It is made
relative to the surroundings. Perceived colour of an object is dependent not only on the colour of
the object itself but also of its background.
8.Not everyone can see all colors : Roughly 10% of the world population is colour blind and to
make coloured infographics accessible to everyone, avoid use of combinations of red and green.
Shown below are how people with three different kinds of color blindness view the same map.
16. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 16
Use of size in Data Visualization:
Using size effectively in data visualization involves conveying information through variations in the size of visual
elements.
Emphasizing Importance
Quantitative Representation
Comparisons
Hierarchical Information
Grouping and Clustering
Visualizing Relationships
Magnitude and Proportions
Temporal Representation
Layering and Complexity
Avoid Misrepresentation
Legend and Annotations
Accessibility
Aesthetic Balance
Contextual Usage
Testing and Iteration
import matplotlib.pyplot as plt
import numpy as np
x = np.random.rand(50)
y = np.random.rand(50)
sizes = np.random.randint(10, 100, 50)
plt.scatter(x, y, s=sizes, alpha=0.5)
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Scatter Plot with Size Encoding')
plt.show()
17. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 17
ENCODING DATA USING COLOR ENCODING
Color encoding is a data visualization technique that involves using different colors to represent distinct
categories, groups, or values within a dataset. It is an effective way to convey information and highlight
patterns, relationships, or differences in data.
Categorical Data
Nominal and Ordinal Data
Highlighting Data
Legend
Contrast and Accessibility
Color Consistency
Color Scales
Common Mistakes
import matplotlib.pyplot as plt
import numpy as np
categories = ['Category A', 'Category B',
'Category C', 'Category D']
values = [20, 45, 30, 15]
colors = ['blue', 'green', 'orange', 'red']
plt.bar(categories, values, color=colors)
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Chart with Color Encoding')
plt.show()
18. Data types
Quantitative
• Anything that has exact numbers.
• For example, Effort in points: 0, 1, 2, 3, 5, 8, 13.
Duration in days: 1, 4, 666.
Ordered / Qualitative
• Anything that can be compared and ordered.
• User Story Priority: Must Have, Great, Good, Not Sure.
Bug Severity: Blocking, Average, Who Cares.
Categorical
• Everything else.
• Entity types: Bugs, Stories, Features, Test Cases.
Fruits: Apples, Oranges, Plums.
26. 2 January 2024 KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India 26
Size Encoding:
Size encoding is a data visualization technique that involves using the dimensions or proportions of
graphical elements, such as shapes or bars, to represent quantitative values in a dataset. This technique
can help convey information about the magnitude or scale of data points.
Quantitative Data
Magnitude Representation
Scatter Plots
Bubble Charts
Proportional Symbols
Hierarchical Data
Attention and Emphasis
Limitations
Legend and Context
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Nadu, India
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Stacked Bar Chart:
A stacked bar chart is a type of data visualization that represents data using a series of bars stacked
on top of each other. Each bar is divided into segments or sections, and the height of each segment
corresponds to a specific value or category. Stacked bar charts are useful for visualizing the
composition of a whole and comparing the contributions of different subcategories.
Composition Representation
Categorical Data
Percentage or Proportion
Multiple Variables
Insight into Trends
Legend
Labeling
Limitations
Comparisons
import matplotlib.pyplot as plt
x = ['A', 'B', 'C', 'D']
y1 = [10, 20, 10, 30]
y2 = [20, 25, 15, 25]
plt.bar(x, y1, color='r')
plt.bar(x, y2, bottom=y1,
color='b')
plt.show()
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import matplotlib.pyplot as plt
import numpy as np
x = ['A', 'B', 'C', 'D']
y1 = np.array([10, 20, 10, 30])
y2 = np.array([20, 25, 15, 25])
y3 = np.array([12, 15, 19, 6])
y4 = np.array([10, 29, 13, 19])
plt.bar(x, y1, color='r')
plt.bar(x, y2, bottom=y1, color='b')
plt.bar(x, y3, bottom=y1+y2, color='y')
plt.bar(x, y4, bottom=y1+y2+y3, color='g')
plt.xlabel("Teams")
plt.ylabel("Score")
plt.legend(["Round 1", "Round 2", "Round 3", "Round 4"])
plt.title("Scores by Teams in 4 Rounds")
plt.show()
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Grouped Bar Chart:
A grouped bar chart is a type of data visualization that displays multiple bars side by side within a category.
Each group of bars represents a distinct category, and the individual bars within each group represent different
subcategories or variables.
Grouped bar charts are particularly useful for comparing values between different categories and subcategories.
Comparative Analysis
Categorical Data
Multiple Variables
Color Coding
Legend
Labeling
Spacing:
Limitations
Orientation
Trends and Patterns
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(5)
y1 = [34, 56, 12, 89, 67]
y2 = [12, 56, 78, 45, 90]
width = 0.40
plt.bar(x-0.2, y1, width)
plt.bar(x+0.2, y2, width)
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Stacked Area Chart:
A stacked area chart is a type of data visualization that displays quantitative data as a series of stacked areas,
with each area representing a different category or subcategory.
This chart is particularly useful for showing how different components contribute to a whole over time or
across other dimensions
Composition Representation
Time-Series Data
Categorical Data:
Multiple Variables
Color Coding
Legend
Labeling
Trends and Patterns
Limitations:
import numpy as np
import matplotlib.pyplot as plt
x=range(1,6)
y1=[1,4,6,8,9]
y2=[2,2,7,10,12]
y3=[2,8,5,10,6]
# Basic stacked area chart.
plt.stackplot(x,y1, y2, y3,
labels=['A','B','C'])
plt.legend(loc='upper left')
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Streamgraph Line Chart:
A streamgraph is a type of data visualization that is used to display how the composition of different categories
changes over time. It is similar to a stacked area chart but with a more fluid and wavy appearance. In a
streamgraph, each category is represented by a flowing stream of color, and the height of the stream at any point
in time indicates the relative proportion of that category.
Streamgraphs are useful for showing patterns and shifts in the distribution of data over time.
Designed for showing temporal changes in the composition of categories.
Fluid, wavy appearance with colors representing different categories.
Useful for highlighting trends and shifts over time.
Can become visually complex with too many categories or data points.
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import matplotlib.pyplot as plt
import numpy as np
time = np.arange(0, 10, 0.1)
layer1 = np.sin(time) + 2
layer2 = np.sin(time) + 1
layer3 = np.sin(time)
layer4 = np.sin(time) - 1
plt.stackplot(time, layer1, layer2, layer3, layer4,
labels=['Layer 1', 'Layer 2', 'Layer 3', 'Layer 4'],
alpha=0.5)
plt.xlabel('Time')
plt.ylabel('Value')
plt.title('Streamgraph Line Chart')
plt.legend(loc='upper right')
plt.show()
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Grouped Histograms:
Grouped histograms show multiple histograms side by side, each representing a distinct category or group. It is
suitable for comparing distributions across groups or categories.
Data Preparation: Organize data and define meaningful groups.
Select Bins: Determine bin ranges for each group. Bin size affects granularity.
Frequency Calculation: Count data points falling within each bin for each group.
Plotting: Create bars for each bin in each group on the same chart.
Color Coding: Use colors to differentiate bars from different groups.
Axes and Labels: Label bins on x-axis and show frequency/count on y-axis.
Legend: Include a legend to identify each group's bars.
Title and Context: Add a title and context to explain the visualization's purpose
Visualization Tools:
Software like Python (Matplotlib, Seaborn), R (ggplot2), and data visualization platforms offer tools for creating
grouped histograms.
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import matplotlib.pyplot as plt
import numpy as np
# Sample data for two groups
group1_data = np.random.normal(0, 1, 1000)
group2_data = np.random.normal(2, 1, 1000)
# Creating histograms for both groups
plt.hist(group1_data, bins=20, alpha=0.5, label='Group 1')
plt.hist(group2_data, bins=20, alpha=0.5, label='Group 2')
# Adding labels and title
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Grouped Histograms')
plt.legend()
plt.show()
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HEXBIN MAPPING:
Hexbin mapping is a data visualization technique used to handle dense data points in a scatter plot.
Instead of individual points, data is grouped into hexagonal bins, creating a heatmap-like representation.
Hexagons allow better visualization of density variations in a 2D space.
import matplotlib.pyplot as plt
import numpy as np
x = np.random.normal(0, 1, 1000)
y = np.random.normal(0, 1, 1000)
plt.hexbin(x, y, gridsize=20, cmap='viridis')
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Hexbin Mapping Example')
plt.colorbar(label='Density')
plt.show()
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Cross Filtering:
Cross filtering is an interactive data exploration technique.
It involves selecting data points in one visualization and seeing the corresponding changes in other linked
visualizations.
Enables users to explore data relationships and correlations.
Hexbin Mapping with Cross Filtering:
Hexbin mapping can be combined with cross filtering to enhance data exploration.
Users can select a hexagonal bin in one visualization and see the effects on other linked visualizations.
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import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
x = np.random.normal(0, 1, 1000)
y = np.random.normal(0, 1, 1000)
# Creating a hexbin plot with Seaborn
sns.set(style="whitegrid")
sns.jointplot(x=x, y=y, kind="hex", color="blue")
plt.show()
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Steps for Implementation:
Data Preparation: Organize and preprocess data for visualization.
Hexbin Mapping: Plot the hexbin map, aggregating data points into hexagonal bins.
Cross Filtering Setup: Link other visualizations (e.g., line chart, bar chart) to the hexbin map.
Interactive Selection: Enable user interaction to select hexagons.
Cross Filtering Effect: Reflect selected hexagons' data in linked visualizations.
Visualization Framework: Tools like D3.js, Plotly, or custom web applications can facilitate hexbin
mapping with cross filtering.
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BUILDING A MIGRANT DEATHS DASHBOARD IN DATA VISUALIZATION
Data Collection and Preparation
Choose a Visualization Tool
Design the Dashboard
Interactive Features
Ethical Considerations
Accessibility and Usability
Deployment
Promotion and Outreach
Updates and Maintenance
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REPORTS DASHBOARDS
A Report is any informational work. This information can be at
any format. Table, Chart, text, number or anything else.
These are documents that show a snapshot of findings pertaining
to a specific topic.
A dashboard is a visual display of the most important information
needed to achieve one or more objectives; consolidated and arranged
on a single screen so the information can be monitored at a glance.
A dashboard is a great way to customize and tailor the display of
chosen data, such as specific metrics or Key Performance
Indicator(KPIs).
Power BI Report
Power BI Report is combination of multiple visual elements
(charts, texts, values…) on a page that can be inter-related with
each other.
Data visualized in the report can be sliced and diced with slicers.
Power BI report is fully interactive from user and it can be
filtered based on some criteria.
Power BI Dashboard
Power BI Dashboard is a high-level view of some of key KPIs of one
or more reports.
Dashboard is a day-to-day view of KPIs, and provide the navigation
point to the detailed reports.
Power BI Dashboard isn’t built for slicing and dicing.
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Items Dashboard Report
Data Source Dashboard are built based on multiple tables that are
connected to each other in one or more ways
Reports are generally created from single table of data set
with no relationship from other tables
No. of pages Dashboards are not allowed to cross more than one
page, it always shows the important reports in the
single page itself.
Reports are generally built-in multiple pages.
Visualizations Dashboards always concentrate on building insights
into the data by using attractive visuals, graphs,
charts, etc.
Reports are not concentrated on the visualization part of
the data rather it looks to create summary pages.
Template Dashboards don’t have any set template, it’s up to
the creator to visualize the data to fit the needs of
the business.
Reports generally have a set template and according to
the addition, deletion of the data, the template will
create reports if the formulas are applied from the data
table.
Slicers and filters Since dashboards are limited to a single page, not
possible to use filters and slicers.
In reports, we can filter and slice the data by using slicers
and many filtering options like cross-filtering, visual
level filtering, and page-level filtering.
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Kind of
Information
Dashboards may include only limited
information which is only important to the
end users
Reports not limited to a single page, so it can have a
detailed break up of each category of the report in
detail in multiple pages.
Reader
Interactivity
Dashboard are pinned to the page so the reader
can just read through the data.
Reports are created with any kind of filters and slicers
so the user can interact with the data set.
Changes to Visuals Dashboards are pinned to the page even the
report owner changes it will not reflect on the
page.
Reports usually come along with the data set, so if
the reader wishes to change the visual type, they
can change at any point in time.
Alerts Dashboards can create alerts to email when
specific condition or criteria is met or limit
crossed.
Reports cannot create alerts to email when specific
condition or criteria is met or limit crossed.
Data set View With Dashboards, we cannot see the source data
because the reader only gets the single page
information.
Reports can see tables, data sets, and fields of the
data in detail i.e. Raw Data.