Trying to tell a compelling story with numbers? Data visualization is an important way analysts and number-crunchers can convey results to many different audiences. In this presentation, David Kretch, a Consultant at Summit, shares some best practices for Data Visualization.
Graphs generated by the default settings of graphing software programs often require the user to perform time consuming or difficult visual tasks. Or, the graphs present visual messages that don't support the author's intended message.
The most effective visual displays are created with an understanding of how the brain processes visual information and how people interpret visual patterns.
Process of converting data set having vast dimensions into data set with lesser dimensions ensuring that it conveys similar information concisely.
Concept
R code
Graphs generated by the default settings of graphing software programs often require the user to perform time consuming or difficult visual tasks. Or, the graphs present visual messages that don't support the author's intended message.
The most effective visual displays are created with an understanding of how the brain processes visual information and how people interpret visual patterns.
Process of converting data set having vast dimensions into data set with lesser dimensions ensuring that it conveys similar information concisely.
Concept
R code
Top 8 Different Types Of Charts In Statistics And Their UsesStat Analytica
Are you confused about various Types Of Charts In Statistics? In this blog, you will get to learn about the various Types Of Charts In Statistics in detail.
Effective Business Presentations with Storyboarding and Data VisualizationCarmen Proctor
Create Effective Presentations by learning a few basic steps and best practices. Deliver concise, to the point, and visually appealing presentations to both internal and external clients. Use storyboarding and correct data visualization are the key to getting your message across.
This will help you:
- Shorten extremely long presentations
- Deliver content in a very clear and easy to understand manner
- Simplify very data heavy presentations
- Keep focus on the project objectives, not filling the white space
Data visualization & Story Telling with DataDr Nisha Arora
Storytelling with data using the appropriate visualization is a skill that is well sought-after for data-driven decision making and it spans many industries and roles (technical/non-technical).
In this presentation, we will briefly discuss the importance of understanding the context, selecting the right visuals, key points for effectively using those for storytelling, design dos, and don’ts, etc.
Data visualization is an interdisciplinary field that deals with the graphic representation of data. It is a particularly efficient way of communicating when the data is numerous as for example a time series.
Top 8 Different Types Of Charts In Statistics And Their UsesStat Analytica
Are you confused about various Types Of Charts In Statistics? In this blog, you will get to learn about the various Types Of Charts In Statistics in detail.
Effective Business Presentations with Storyboarding and Data VisualizationCarmen Proctor
Create Effective Presentations by learning a few basic steps and best practices. Deliver concise, to the point, and visually appealing presentations to both internal and external clients. Use storyboarding and correct data visualization are the key to getting your message across.
This will help you:
- Shorten extremely long presentations
- Deliver content in a very clear and easy to understand manner
- Simplify very data heavy presentations
- Keep focus on the project objectives, not filling the white space
Data visualization & Story Telling with DataDr Nisha Arora
Storytelling with data using the appropriate visualization is a skill that is well sought-after for data-driven decision making and it spans many industries and roles (technical/non-technical).
In this presentation, we will briefly discuss the importance of understanding the context, selecting the right visuals, key points for effectively using those for storytelling, design dos, and don’ts, etc.
Data visualization is an interdisciplinary field that deals with the graphic representation of data. It is a particularly efficient way of communicating when the data is numerous as for example a time series.
This presentation contains an introduction of tableau software and in a particular way in Connecting to data, Visual Analytics, Dashboard and stories, Calculations, Mapping and Tableau Online & Competitors.
To correctly portray complex data a developer must utilize modern data visualization techniques. This session describes how to create data graphics (charts) and dashboards that are concise, attractive and usable. Learn the practical design principles that apply to every data graphic you produce. Without this firsthand knowledge one can innocently construct visuals that erroneously represent data and mislead viewers. I cover Important Visual Perception Patterns to Know and the Top Common Chart Design Errors. I will also share the knowledge framework for creating effective graphical data dashboards. Apply the best design pattern every time using the "3 threes" — a convenient memory hook representing the distinctions between systems that “monitor, measure, and manage” performance metrics for “operations, tactical or strategic” purposes. Become a hero of interactive data visualization. Copious examples included.
A quick reference on designing data visualizations that delight and leverage best practices from the design world to ensure your data is presented in meaningful, usable, fun ways.
A walk through the maze of understanding Data Visualization using several tools such as Python, R, Knime and Google Data Studio.
This workshop is hands-on and this set of presentations is designed to be an agenda to the workshop
Analyzing and Visualizing Data Chapter 6Data Represent.docxdurantheseldine
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.
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Introducing Visual Encoding cont.
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Introducing Visual Encoding cont.
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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.
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Introducing Visual Encoding cont.
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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.
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Influencing Factors and Considerations cont.
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Influencing Factors and Considerations cont.
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Influencing Factors and Considerations cont.
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Influencing Factors and Considerations cont.
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Influencing Factors and Considerations cont.
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Influencing Factors and Considerations cont.
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Influencing Factors and Considerations cont.
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Influencing Factors and Considerations cont.
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Influencing Factors and Considerations cont.
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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.
Dianne Finch, visiting assistant professor of communications at Elon University, provided this data visualization handout from an issue of the Communications of the ACM during the SABEW 2014 session, "Data Visualization: A Hands-On Primer for Business Journalists," March 28, 2014.
For more information about training for journalists, please visit http://businessjournalism.org.
Design Patterns
Christian Behrens
https://www.behance.net/gallery/29576487/The-Form-of-Facts-and-Figures
Christopher Alexander
The term design patterns was originally coined about three decades ago by Christopher Alexander, an architect and critic who envisioned a generic and modular “language” of methods to describe the process of construction and urban planning by means of recurring problems that are well-known in a specific context, and respective solutions that have been proved and tested in the past and can therefore be seen as a safe choice to tackle a certain design challenge. Although it never made its breakthrough in the field of architecture, the basic idea of design patterns was adopted by other engineering disciplines, most notably software development in the early 1990s. A second wave of success seems to have appeared recently, when several projects were launched to build up pattern libraries for digital user interfaces. https://en.wikipedia.org/wiki/Christopher_Alexander
2
Design Patterns
Rejected by Architects, Adopted by Software Engineers,
…and the field of user interface design.
Although Alexander’s book became a bestseller and is a de-facto standard read for architecture students until today, it received much criticism and invoked sceptical reactions among the architecture community. Looking back at it some thirty years later, Alexander’s pattern language can be described as a success story on a detour. While widely rejected by architects and urban planners, the concept was picked up by computer scientist in the late 1980s and became a huge success in the wake of the rise of object-oriented programming languages such as Java
3
Design Patterns
Rejected by Architects, Adopted by Software Engineers,
…and the field of user interface design.
http://zurb.com/patterntap
http://patternry.com/
useful for the general description of common design problems, and provide solutions based on the relationships and behaviors of objects Companies and institutions that deal with interface design problems, have also launched own projects that aim at streamlining the development of new products and services by means of a comprehensive design pattern collection.
Design Patterns can help to tackle commonly known recurring design problems with well-proven solutions. A single pattern provides a brief description of one particular design problem. This problem can be a physical attribute of an application (for instance a dropdown menu), or a functional behavior (e.g. the login dialog of a website). A pattern typically consists of a description of the problem, and a solution that has been proven before and is generally recognized. Usually, a pattern provides additional information like an example of a real-world scenario in which it has been successfully applied as well as a rationale to briefly describe the benefit the usage this patterns bears.
4
Discrete Quantities:
Simple Bar Chart
Snapshot:
they do not display con.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Data Visualization by David Kretch
1. Data Visualization
April 3, 2015
• When you should graph
• What you should graph
• Given some data, how would you graph it
2. When should you graph your data?
2Data Visualization
Always
Don’t just make graphs for client reports -- graph your data for
yourself, so you understand it.
If you use a table in a report, see if you can make it into a graph.
3. Why graphs?
Because of the environment that humans evolved in, we are much
better at getting info from color, size, shape, and position than from
reading text.
3Data Visualization
Find the dangerous creatures!
5. Why else do people like graphs?
People like cool-looking stuff.
5Data Visualization
Not cool Cool
6. What are we currently doing?
• Making lots of tables
6Data Visualization
Group Mean 25% 50% 75%
Bananas 11.3 2.7 4.6 23.1
Kittens 4.0 0.9 3.6 7.5
Phones -3.1 -11.0 -2.9 2.2
Variable Parameter
Estimate
Cuteness 0.6***
Ability to Fly 1.4***
Deadliness 11.2***
Telepathy -9.8***
Big Ears -17.3***
7. What is wrong with tables?
Tables give only a partial picture – means only tell us so much.
Figuring out what’s bigger, and by how much, requires more work.
The information is not necessarily in any order, so we need to read
all the numbers.
7Data Visualization
8. What kinds of graphs should you make?
• The distribution, instead of
giving just mean, median, etc.
• The relationship between two
variables – the conditional
distribution
• Graph estimation results’ point
estimates and confidence
intervals
8Data Visualization
9. What to expect out of this presentation
1. Discussion of the type of graph (e.g. distributions)
2. How the type of graph applies to continuous vs. categorical data
3. Extensions (e.g. graphing more than one at a time)
What not to expect: how to do these in any particular software.
9Data Visualization
11. Distributions – Continuous variables
Make density plots/histograms for continuous variables. These give
much more information than means, medians, etc.
Two distributions with the same mean, but which are dramatically different.
11Data Visualization
12. Density vs. histogram
A density plot is basically a smoothed histogram.
12Data Visualization
13. Distributions – Categorical variables
Make bar charts for categorical variables.
Tip: if your categories don’t have any inherent order, order them
from largest to smallest.
13Data Visualization
14. Compare distributions using color
Suppose we want to compare the distribution of income among
different occupations. Plot all the distributions, distinguished by
color, and use transparency to make them all visible simultaneously.
14Data Visualization
17. Relationships between variables
If we’re asking, for example, what GDP growth looks like at different
levels of government spending, we can show this using a
scatterplot.
17Data Visualization
18. How to show trends
We can highlight the trend using scatterplot smoothing, which
adapts the shape of the trend line to the data.
18Data Visualization
19. How to show multiple groups
We can see if the relationship differs among groups by giving each
group a color.
19Data Visualization
20. Another use for colors
Suppose we want to come up with rules to identify people’s favorite
food based on population density and elevation (bear with me)
Can we see this on a graph?
20Data Visualization
21. Graphing relationships with categorical data
With categorical data, you typically can’t use scatterplots because
points fall right on top of each other (‘overplotting’).
However! We can use jittering to move the plotted points slightly.
21Data Visualization
Without jittering With jittering
22. Graphing relationships with categorical data
The next step beyond jittering is to use a boxplot, which shows
– The mean,
– 25th and 75th percentiles,
– 1.5 times the inter-quartile range (IQR)
– outliers (plotted as points)
22Data Visualization
mean
75th pctile
mean + 1.5 *IQR
outlier
23. Looping back
A boxplot isn’t, after all, all that different from the multi-colored
density plot we showed earlier. Which is better depends on what
you’re trying to show.
23Data Visualization
24. Use log scale if your data spans a wide range
Let’s say you have a large
range of values, but most of
your data is concentrated to
one part of the range.
It’s easier to see what’s
going when we use log
scale.
24Data Visualization
26. Graphing estimation results
We make a lot of regression tables, but we can make them easier to
understand by putting them into graphs.
26Data Visualization
27. ggplot(df, aes(population_density, elevation, color = favorite_food)) +
geom_point()
27Data Visualization
dataset x variable y variable
make scatterplot
color variable
All graphs made in R and ggplot2
28. Data Visualization Checklist
• Always graph
• Use color, size, shape, and position
• Three important types of graph:
– Distribution
– Relationship
– Estimation results
• Highlight important facts
• Make it cool-looking
28Data Visualization