This document provides an introduction to data visualization and infographics. It discusses the differences between infographics, which focus more on design and communication, and data visualization, which focuses more on statistical analysis. Examples are given of effective infographics used in journalism, medicine, meteorology, history, and other fields. The document also outlines best practices for creating effective infographics and data visualizations, such as using color meaningfully and providing context so audiences understand the information presented. It cautions that not all visualizations present information accurately and provides some examples of misleading charts.
Session Slides for Presentation & Panel at Babson School of Business 4-17-18 ...Fred Isbell
This past weekend I had a chance to join an old colleague and friend, Clare Gillan, and be on a panel for her class at the Babson Graduate Business School MBA Program called “Competing on Analytics”. I kicked off the session with a mix of content I’ve used externally at outside speaking engagements and at the Duke/Fuqua School of Business talking to students about trends in technology and marketing. Topics included modern marketing, the art and science of marketing, Big Data and Analytics, Data Science and the role of Data Scientists, and two SAP case studies, one on Duke Men's basketball analytics and the second on SAP Statistics for the NHL fan experience and NHL.com.
Session Slides for Presentation & Panel at Babson School of Business 4-17-18 ...Fred Isbell
This past weekend I had a chance to join an old colleague and friend, Clare Gillan, and be on a panel for her class at the Babson Graduate Business School MBA Program called “Competing on Analytics”. I kicked off the session with a mix of content I’ve used externally at outside speaking engagements and at the Duke/Fuqua School of Business talking to students about trends in technology and marketing. Topics included modern marketing, the art and science of marketing, Big Data and Analytics, Data Science and the role of Data Scientists, and two SAP case studies, one on Duke Men's basketball analytics and the second on SAP Statistics for the NHL fan experience and NHL.com.
Information design is both a technical skill and an art form. To design great visualizations requires a diverse range of skill sets and a keen ability to understand the decisions to be made, the data available, the tools and platforms available for visualization design, and how to apply design best practices to create effective visualizations that communicate clearly. Even the most robust routine health information systems face challenges around how to visualize data in a way that facilitates decision-making by key stakeholders.
Data visualization trends in Business Intelligence: Allison Sapka at Analytic...Fitzgerald Analytics, Inc.
Allison Sapka's presentation at the Analytics and Data in Financial Services Meetup in Dec 2012. Alison discusses trends in Data Visualization, including why visualization is so powerful when implemented well, and confusing or misleading when done badly
I shared my thoughts on the opportunity of data visualization with a group of academic researchers and faculty members at the University of Washington, Bothell on May 28th, 2014. Here are the visuals that I used
Visualizing Healthcare Data: Information Design Best Practices (eHealth 2012 ...Stefan Popowycz
This is my eHealth 2012 presentation will focuse on the principles behind information design and how visualization best practices can be leveraged within context of healthcare data. It illustrates theory in action, by drawing specific attention to the successful public facing solution, the 2012 Canadian Hospital Reporting Project (CHRP). The CHRP tool is a pan-Canadian external facing solution with an audience of over 3000+ users; it received over 25,000 impressions in the first 24 hours, and was called by the Toronto Star as “an innovative online tool that is being heralded as the most advanced of its kind in the world.”
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Statistics: Visualizing Data
Introductory Essay from the Locks
The Reality Today
All of us now are being blasted by information design. It's being poured into our eyes
through the Web, and we're all visualizers now; we're all demanding a visual aspect to
our information… And if you're navigating a dense information jungle, coming across
a beautiful graphic or a lovely data visualization, it's a relief, it's like coming across a
clearing in the jungle. –David McCandless
In today’s complex ‘information jungle,’ David McCandless observes that “Data is the new soil.”
McCandless, a data journalist and information designer, celebrates data as a ubiquitous resource
providing a fertile and creative medium from which new ideas and understanding can grow.
McCandless’s inspiration, statistician Hans Rosling, builds on this idea in his own TEDTalk with his
compelling image of flowers growing out of data/soil. These ‘flowers’ represent the many insights that
can be gleaned from effective visualization of data.
We’re just learning how to till this soil and make sense of the mountains of data constantly being
generated. As Gary King, Director of Harvard’s Institute for Quantitative Social Science says in his New
York Times article “The Age of Big Data”:
“It’s a revolution. We’re really just getting under way. But the march of quantification,
made possible by enormous new sources of data, will sweep through academia,
business and government. There is no area that is going to be untouched.”
How do we deal with all this data without getting information overload? How do we use data
to gain real insight into the world? Finding ways to pull interesting information out of data can
be very rewarding, both personally and professionally. The managing editor of Financial Times
observed on CNN’s Your Money: “The people who are able to in a sophisticated and practical
way analyze that data are going to have terrific jobs." Those who learn how to present data in
effective ways will be valuable in every field.
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Many people, when they think of data, think of tables filled with numbers. But this long-held notion is
eroding. Today, we’re generating streams of data that are often too complex to be presented in a
simple “table.” In his TEDTalk, Blaise Aguera y Arcas explores images as data, while Deb Roy uses
audio, video, and the text messages in social media as data.
Some may also think that only a few specialized professionals can draw insights from data. When we
look at data in the right way, however, the results can be fun, insightful, even whimsical--and accessible
to everyone! Who knew, for example, that there are more relationship break-ups on Monday than on
any other day of the week, or that ...
Data Visualization Resource Guide (September 2014)Amanda Makulec
A summary guide to data visualization design, including key design principles, great resources, and tools (listed by category with short explanations) that you can use to help design elegant, effective data visualizations that help share your message & promote the use of your information.
Note that the tools & resources highlighted are suggested, and inclusion should not be considered as an endorsement from JSI.
Data Science in 2016: Moving up by Paco Nathan at Big Data Spain 2015Big Data Spain
The term 'Data Science' was first described in scientific literature about 15 years ago. It started to become a major trend in industry about 7 years ago.
O'Reilly Media surveys the industry extensively each year. In addition we get a good birds-eye view of industry trends through our conference programs and publications, working closely with some of the best practitioners in Data Science.
By now, the field has evolved far beyond its origins eclipsing an earlier generation of Business Intelligence and Data Warehousing approaches. Data Science is moving up, into the business verticals and government spheres of influence where it has true global impact.
This talk considers Data Science trends from the past three years in particular. What is emerging? Which parts are evolving? Which seem cluttered and poised for consolidation or other change?
Session presented at Big Data Spain 2015 Conference
15th Oct 2015
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by: http://www.paradigmatecnologico.com
Abstract: http://www.bigdataspain.org/program/thu/slot-2.html
Data science skills are increasingly important for research and industry projects. With complex data science projects, however, come complex needs for understanding and communicating analysis processes and results. The rise of data science has accompanied a comparable rise in business intelligence and the demand for visualizations and dashboards that can explain models, summarize results, assist with decision making, and even predict outcomes. Ultimately, an analyst’s data science toolbox is incomplete without visualization skills. This talk will explore the landscape of visualization for data science – using visualization for data exploration and communication, reproducible approaches to visualization, and how to develop better instincts for visualization choice and graphic design.
This is a practical presentation from NODA 2017, the Nordic Data conference, this year held in Odense. The presentation discusses tools (specifically Datawrapper) and general editorial approaches to data-driven journalism. The presentation advocates a pragmatic approach - based on searching for data, questioning, visualisation and written texts. This approach could provide opportunities specifically for regional/local media.
Be aware that as this is a presentation from the people behind Datawrapper the tool is discussed and presented on a number of slides in this presentation.
"Big Data" is term heard more and more in industry – but what does it really mean? There is a vagueness to the term reminiscent of that experienced in the early days of cloud computing. This has led to a number of implications for various industries and enterprises. These range from identifying the actual skills needed to recruit talent to articulating the requirements of a "big data" project. Secondary implications include difficulties in finding solutions that are appropriate to the problems at hand – versus solutions looking for problems. This presentation will take a look at Big Data and offer the audience with some considerations they may use immediately to assess the use of analytics in solving their problems.
The talk begins with an idea of how big "Big Data" can be. This leads to an appreciation of how important "Management Questions" are to assessing analytic needs. The fields of data and analysis have become extremely important and impact nearly all facets of life and business. During the talk we will look at the two pillars of Big Data – Data Warehousing and Predictive Analytics. Then we will explore the open source tools and datasets available to NATO action officers to work in this domain. Use cases relevant to NATO will be explored with the purpose of show where analytics lies hidden within many of the day-to-day problems of enterprises. The presentation will close with a look at the future. Advances in the area of semantic technologies continue. The much acclaimed consultants at Gartner listed Big Data and Semantic Technologies as the first- and third-ranked top technology trends to modernize information management in the coming decade. They note there is an incredible value "locked inside all this ungoverned and underused information." HQ SACT can leverage this powerful analytic approach to capture requirement trends when establishing acquisition strategies, monitor Priority Shortfall Areas, prepare solicitations, and retrieve meaningful data from archives.
Information design is both a technical skill and an art form. To design great visualizations requires a diverse range of skill sets and a keen ability to understand the decisions to be made, the data available, the tools and platforms available for visualization design, and how to apply design best practices to create effective visualizations that communicate clearly. Even the most robust routine health information systems face challenges around how to visualize data in a way that facilitates decision-making by key stakeholders.
Data visualization trends in Business Intelligence: Allison Sapka at Analytic...Fitzgerald Analytics, Inc.
Allison Sapka's presentation at the Analytics and Data in Financial Services Meetup in Dec 2012. Alison discusses trends in Data Visualization, including why visualization is so powerful when implemented well, and confusing or misleading when done badly
I shared my thoughts on the opportunity of data visualization with a group of academic researchers and faculty members at the University of Washington, Bothell on May 28th, 2014. Here are the visuals that I used
Visualizing Healthcare Data: Information Design Best Practices (eHealth 2012 ...Stefan Popowycz
This is my eHealth 2012 presentation will focuse on the principles behind information design and how visualization best practices can be leveraged within context of healthcare data. It illustrates theory in action, by drawing specific attention to the successful public facing solution, the 2012 Canadian Hospital Reporting Project (CHRP). The CHRP tool is a pan-Canadian external facing solution with an audience of over 3000+ users; it received over 25,000 impressions in the first 24 hours, and was called by the Toronto Star as “an innovative online tool that is being heralded as the most advanced of its kind in the world.”
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1
Statistics: Visualizing Data
Introductory Essay from the Locks
The Reality Today
All of us now are being blasted by information design. It's being poured into our eyes
through the Web, and we're all visualizers now; we're all demanding a visual aspect to
our information… And if you're navigating a dense information jungle, coming across
a beautiful graphic or a lovely data visualization, it's a relief, it's like coming across a
clearing in the jungle. –David McCandless
In today’s complex ‘information jungle,’ David McCandless observes that “Data is the new soil.”
McCandless, a data journalist and information designer, celebrates data as a ubiquitous resource
providing a fertile and creative medium from which new ideas and understanding can grow.
McCandless’s inspiration, statistician Hans Rosling, builds on this idea in his own TEDTalk with his
compelling image of flowers growing out of data/soil. These ‘flowers’ represent the many insights that
can be gleaned from effective visualization of data.
We’re just learning how to till this soil and make sense of the mountains of data constantly being
generated. As Gary King, Director of Harvard’s Institute for Quantitative Social Science says in his New
York Times article “The Age of Big Data”:
“It’s a revolution. We’re really just getting under way. But the march of quantification,
made possible by enormous new sources of data, will sweep through academia,
business and government. There is no area that is going to be untouched.”
How do we deal with all this data without getting information overload? How do we use data
to gain real insight into the world? Finding ways to pull interesting information out of data can
be very rewarding, both personally and professionally. The managing editor of Financial Times
observed on CNN’s Your Money: “The people who are able to in a sophisticated and practical
way analyze that data are going to have terrific jobs." Those who learn how to present data in
effective ways will be valuable in every field.
T
E
D
|
W
ile
y
V
is
ua
liz
in
g
D
at
a
In
st
ru
ct
or
M
at
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ia
ls
2
Many people, when they think of data, think of tables filled with numbers. But this long-held notion is
eroding. Today, we’re generating streams of data that are often too complex to be presented in a
simple “table.” In his TEDTalk, Blaise Aguera y Arcas explores images as data, while Deb Roy uses
audio, video, and the text messages in social media as data.
Some may also think that only a few specialized professionals can draw insights from data. When we
look at data in the right way, however, the results can be fun, insightful, even whimsical--and accessible
to everyone! Who knew, for example, that there are more relationship break-ups on Monday than on
any other day of the week, or that ...
Data Visualization Resource Guide (September 2014)Amanda Makulec
A summary guide to data visualization design, including key design principles, great resources, and tools (listed by category with short explanations) that you can use to help design elegant, effective data visualizations that help share your message & promote the use of your information.
Note that the tools & resources highlighted are suggested, and inclusion should not be considered as an endorsement from JSI.
Data Science in 2016: Moving up by Paco Nathan at Big Data Spain 2015Big Data Spain
The term 'Data Science' was first described in scientific literature about 15 years ago. It started to become a major trend in industry about 7 years ago.
O'Reilly Media surveys the industry extensively each year. In addition we get a good birds-eye view of industry trends through our conference programs and publications, working closely with some of the best practitioners in Data Science.
By now, the field has evolved far beyond its origins eclipsing an earlier generation of Business Intelligence and Data Warehousing approaches. Data Science is moving up, into the business verticals and government spheres of influence where it has true global impact.
This talk considers Data Science trends from the past three years in particular. What is emerging? Which parts are evolving? Which seem cluttered and poised for consolidation or other change?
Session presented at Big Data Spain 2015 Conference
15th Oct 2015
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by: http://www.paradigmatecnologico.com
Abstract: http://www.bigdataspain.org/program/thu/slot-2.html
Data science skills are increasingly important for research and industry projects. With complex data science projects, however, come complex needs for understanding and communicating analysis processes and results. The rise of data science has accompanied a comparable rise in business intelligence and the demand for visualizations and dashboards that can explain models, summarize results, assist with decision making, and even predict outcomes. Ultimately, an analyst’s data science toolbox is incomplete without visualization skills. This talk will explore the landscape of visualization for data science – using visualization for data exploration and communication, reproducible approaches to visualization, and how to develop better instincts for visualization choice and graphic design.
This is a practical presentation from NODA 2017, the Nordic Data conference, this year held in Odense. The presentation discusses tools (specifically Datawrapper) and general editorial approaches to data-driven journalism. The presentation advocates a pragmatic approach - based on searching for data, questioning, visualisation and written texts. This approach could provide opportunities specifically for regional/local media.
Be aware that as this is a presentation from the people behind Datawrapper the tool is discussed and presented on a number of slides in this presentation.
"Big Data" is term heard more and more in industry – but what does it really mean? There is a vagueness to the term reminiscent of that experienced in the early days of cloud computing. This has led to a number of implications for various industries and enterprises. These range from identifying the actual skills needed to recruit talent to articulating the requirements of a "big data" project. Secondary implications include difficulties in finding solutions that are appropriate to the problems at hand – versus solutions looking for problems. This presentation will take a look at Big Data and offer the audience with some considerations they may use immediately to assess the use of analytics in solving their problems.
The talk begins with an idea of how big "Big Data" can be. This leads to an appreciation of how important "Management Questions" are to assessing analytic needs. The fields of data and analysis have become extremely important and impact nearly all facets of life and business. During the talk we will look at the two pillars of Big Data – Data Warehousing and Predictive Analytics. Then we will explore the open source tools and datasets available to NATO action officers to work in this domain. Use cases relevant to NATO will be explored with the purpose of show where analytics lies hidden within many of the day-to-day problems of enterprises. The presentation will close with a look at the future. Advances in the area of semantic technologies continue. The much acclaimed consultants at Gartner listed Big Data and Semantic Technologies as the first- and third-ranked top technology trends to modernize information management in the coming decade. They note there is an incredible value "locked inside all this ungoverned and underused information." HQ SACT can leverage this powerful analytic approach to capture requirement trends when establishing acquisition strategies, monitor Priority Shortfall Areas, prepare solicitations, and retrieve meaningful data from archives.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
3. Infographics vs. Data Visualization
● INFOGRAPHIC: An Infographic is a graphical representation or
visualization of information or data, designed to present, analyse
or explore.
o More focus on design and communication.
● DATA VISUALIZATION: Tools and methods for visual
representation and description of data, statistics, and analysis.
o More focus on statistical analysis.
4. “THERE IS MAGIC IN GRAPHS.”
-Henry D. Hubbard
https://designinspirationhub.wordpress.com/2012/05/12/25-chart-graph-designs-for-inspiration/
5. Why are data visualizations and infographics important?
● Communicating
Results and Information
● Exploratory Data
Analysis
http://neomam.com/interactive/13reasons/
9. Creating Infographics:
A step by step guide
1. Understand Goal and
Audience
1. Obtain and Organize Data
1. Create Story/ Narrative
1. Visualize Data and make it
Eye Catching
10. Creating Infographics
● The “10-foot-test”
○ Put an infographic on a 11”x8.5” piece of paper and step 10 feet away. Do
you know what the infographic is saying?
● Proper Usage of Color
○ Use “only those components in a chart that actually represent values and
items.” (i.e. not the axes)
● Meaning to Audience
○ “If you can provide context and meaning to your data visualizations, your
audience will be more willing to listen”
○ Don’t simply show people with no math background an infographic – explain it
to them!
11. Data Visualization
Charting the Beatles: “What did the band’s calendar look like during Beatlemania?”
http://chartingthebeatles.com/#schedule
13. Applications of Data Science:
Journalism
The New York Times: “512 Paths to the White House”
http://www.nytimes.com/interactive/2012/11/02/us/politics/paths-to-the-white-house.ht`ml
14. Medicine
“Heart Disease in the United States - Map”
http://www.cardiologist.org/heart-disease-in-the-united-states-map/
15. Meteorology
“Surveying the Destruction caused by Hurricane Sandy”
http://www.nytimes.com/newsgraphics/2012/1120-sandy/survey-of-the-flooding-in-new-york-after-the-hurricane.html
16. “Mapping the Microbes of the New York City Subway”
www.wired.com/2015/02/mapping-microbes-new-york-city-subway/
Epidemiology
18. Creating Data Visualizations
● Maximize information shown (data-ink ratio)
● Always include axes and units when applicable
● Avoid unnecessary graph “decorations”
● Represent data in an accurate and truthful way
20. Not All Visualizations Are Created Equal
From “Media Matters”
http://mediamatters.org/research/2012/10/01/a-history-of-dishonest-fox-charts/190225
(Cont’d)
24. Links
A history of dishonest FOX charts: http://mediamatters.org/research/2012/10/01/a-history-of-dishonest-
fox-charts/190225
Element of Visualization: http://www.visual-literacy.org/periodic_table/periodic_table.html
Download R: http://www.r-project.org/
Canva https://www.canva.com/
Inforgr.am https://infogr.am/app/#/home
