Presentation from the September 2010 Columbus Web Analytics Wednesday. The presenter was Tim Wilson of Resource Interactive. Download the presentation (PPT 2007) for notes embedded in the slides and some useful animations.
The art technique of data visualizationUday Kothari
Decision making based on information has been the single most important objective of a data warehousing or big data pursuit. No matter how big, fast and varied data are generated and processed; decision makers are only concerned with the consumption of its end result – data visualization.
Data visualization simply means representing data in a visually appealing manner to enable understanding of the context in which we operate. Data visualization is a “moment of truth” that stems from a data management initiative. It is a very linear process of decision making; and hence, critical to its success. However, data visualizations also possess the potential to put an end to such initiatives; especially, when they are either heavily biased on just the design or contain information overload.
This webinar on the art and technique of data visualization focuses sharply on the one thing that matters most to qualify for effective data visualization: the truth that comes out from data. We have facilitated the discussion with the help of our 3D framework: Design, Discovery & Data.
After registering, you will receive a confirmation email containing information about joining the webinar.
An overview of some methods and principles for big data visualization. The presentation quickly hits on the topic of dashboards and some cyber security uses. The topic of a big data lake is also briefly discussed in the context of a cyber security big data setup.
This slide deck gives a general overview of Data Visualization, with inspiring examples, the strength and weaknesses of the human visual system, a few technical frameworks that may be used for creating your own visualizations and some design concepts from the data visualization field.
Certain modalities (such as text, graphs, tables, and images) can better present recommendations and explanations to users. The focus of this study is the visualization of explanations in recommender systems. The study falls in the area of controlling the recommendation process which gained little attention so far.
The art technique of data visualizationUday Kothari
Decision making based on information has been the single most important objective of a data warehousing or big data pursuit. No matter how big, fast and varied data are generated and processed; decision makers are only concerned with the consumption of its end result – data visualization.
Data visualization simply means representing data in a visually appealing manner to enable understanding of the context in which we operate. Data visualization is a “moment of truth” that stems from a data management initiative. It is a very linear process of decision making; and hence, critical to its success. However, data visualizations also possess the potential to put an end to such initiatives; especially, when they are either heavily biased on just the design or contain information overload.
This webinar on the art and technique of data visualization focuses sharply on the one thing that matters most to qualify for effective data visualization: the truth that comes out from data. We have facilitated the discussion with the help of our 3D framework: Design, Discovery & Data.
After registering, you will receive a confirmation email containing information about joining the webinar.
An overview of some methods and principles for big data visualization. The presentation quickly hits on the topic of dashboards and some cyber security uses. The topic of a big data lake is also briefly discussed in the context of a cyber security big data setup.
This slide deck gives a general overview of Data Visualization, with inspiring examples, the strength and weaknesses of the human visual system, a few technical frameworks that may be used for creating your own visualizations and some design concepts from the data visualization field.
Certain modalities (such as text, graphs, tables, and images) can better present recommendations and explanations to users. The focus of this study is the visualization of explanations in recommender systems. The study falls in the area of controlling the recommendation process which gained little attention so far.
Data visualization is often used as the first step while performing a variety of analytical tasks. With the advent of large, high-dimensional datasets and strong interest in data science, there is a need for tools that can support rapid visual analysis. In this paper we describe our vision for a new class of visualization recommendation systems that can automatically identify and interactively recommend visualizations relevant to an analytical task.
This presentation have the concept of Big data.
Why Big data is important to the present world.
How to visualize big data.
Steps for perfect visualization.
Visualization and design principle.
Also It had a number of visualization method for big data and traditional data.
Advantage of Visualization in Big Data
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this session I will share insights and knowledge that I have gained from building up a Data Science department from scratch. The talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organization.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
The Other 99% of a Data Science ProjectEugene Mandel
Slides from my talk at Open Data Science Conference 2016.
Algorithms and models are an important (and cool) part of data science. This talk is about all the other steps that it takes to deploy a data science project that makes a product slightly smarter. Stuff that you hear from practitioners, but is not covered well enough in books.
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Data Driven Practice in e-MDs. This covers custom crystal reports from scratch, slicing and dicing data in Excel, Visualizing Data, and understanding that change isn't really a technical problem.
Data visualization is often used as the first step while performing a variety of analytical tasks. With the advent of large, high-dimensional datasets and strong interest in data science, there is a need for tools that can support rapid visual analysis. In this paper we describe our vision for a new class of visualization recommendation systems that can automatically identify and interactively recommend visualizations relevant to an analytical task.
This presentation have the concept of Big data.
Why Big data is important to the present world.
How to visualize big data.
Steps for perfect visualization.
Visualization and design principle.
Also It had a number of visualization method for big data and traditional data.
Advantage of Visualization in Big Data
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this session I will share insights and knowledge that I have gained from building up a Data Science department from scratch. The talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organization.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
The Other 99% of a Data Science ProjectEugene Mandel
Slides from my talk at Open Data Science Conference 2016.
Algorithms and models are an important (and cool) part of data science. This talk is about all the other steps that it takes to deploy a data science project that makes a product slightly smarter. Stuff that you hear from practitioners, but is not covered well enough in books.
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Data Driven Practice in e-MDs. This covers custom crystal reports from scratch, slicing and dicing data in Excel, Visualizing Data, and understanding that change isn't really a technical problem.
Creating a marketing dashboard for the purpose of presenting data in a manner that is easy to understand and act upon. Presented at an agencyside workshop in March 2010.
ABSTRACT: The ongoing big data revolution has revolutionized the way in which technology is used to empower new business segments like social networking and transform old business segments like traditional retail. However, the DNA that is used to build data processing platform is evolving quite rapidly. There is a plethora of competing tools, technologies, and “religion” for how to build state-of-the-art data analysis frameworks. In this talk, I will go over five ways to build scalable high-performance long-lasting data analysis frameworks in the wrong way. Surprisingly, the industry is full of examples of organization building frameworks in this “wrong” way. Since the “right” way to build a technology framework is dependent on the key business drivers, it is my hope that this talk will spur a discussion on what is the “right” way for Pinterest. The talk will focus on technologies including “data plumbing” (e.g. tools in the Hadoop ecosystem), and statistical modeling methods (e.g. R and Python). In this talk, I’ll try to connect to platform builders, data scientists, and business decision makers.
BIO: Jignesh Patel is a Professor in Computer Sciences at the University of Wisconsin-Madison, where he also earned his Ph.D. He has worked in the area of databases (now fashionably called “big data”) for over two decades. He has won several best paper awards, and industry research awards. He is the recipient of the Wisconsin COW teaching award, and the U. Michigan College of Engineering Education Excellence Award. He has a strong interest in seeing research ideas transition to actual products. His Ph.D. thesis work was acquired by NCR/Teradata in 1997, and he also co-founded Locomatix -- a startup that built a platform to power real-time data-driven mobile services. Locomatix became part of Twitter in 2013. He is an ACM Distinguished Scientist and an IEEE Senior Member. He also serves on the board of Lands’ End, and advises a number of startups.
Data Science - An emerging Stream of Science with its Spreading Reach & ImpactDr. Sunil Kr. Pandey
This is my presentation on the Topic "Data Science - An emerging Stream of Science with its Spreading Reach & Impact". I have compiled and collected different statistics and data from different sources. This may be useful for students and those who might be interested in this field of Study.
Information Visualization is becoming an increasingly important strategy to provide dashboard consumers insights into their information deluge. It is important for practitioners to learn how to design dashboards in the most effective ways.
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
- Data Quality
- Data as a Service
- Structured Data Meets Text
- No Black-box
- Towards Self-optimizing Machines
A talk on EDHREC, a service for magic the gathering deck recommendations. I discuss the algorithms used, my infrastructure, and some lessons learned about building data science applications.
Just because you can doesn't mean that you should - thingmonk 2016Boris Adryan
Big data! Fast data! Real-time analytics! These are buzzwords commonly associated with platform offerings around IoT.
Although the Law of large numbers always applies, just because you can deploy more sensors doesn't automatically mean that you should. After all, they cost money, bandwidth, and can be a pain to maintain. On the example of the Westminster Parking Trial, I'd like to show how analytics on preliminary survey data could have reduced the number of deployed sensors significantly.
A similar logic goes for fast and real-time analytics. While being advertised as killer features, many people new to IoT and analytics are not even aware that they might get away with batch processing. On the example of flying a drone, I'd like to discuss for which use cases I'd apply edge processing (on the drone), stream or micro-batch analytics (when data arrives at the platform) or work on batched data (stored in a database).
Shared at "Data-Driven Design for User Experience" with Le Wagon Tokyo, 25 Aug
https://www.meetup.com/ja-JP/Le-Wagon-Tokyo-Coding-Station/events/280067831/
In UX design, data means the voice of users (customers) and actionable insights that are beyond just numbers. Hearing these voices through user research and usage analytics is a critical process of building a human-centric design. Based on data-driven design, UX designers, product managers, and even senior management can listen to the inner voice of users and extrapolate those to discover a user journey for clear call-to-action and unwavering customer loyalty.
At this webinar, our guest speaker Emi Kwon, UX Design Director at Metlife, will walk you through the basics of data-driven design as well as share some tips and tricks for making data-driven design your value proposition as a product manager/ UX specialist.
Agenda:
✔️ Data ecosystem — Data lake, data warehouse…what does it mean for UX?
✔️ Small data and big data — the opportunities and pitfalls
✔️ Research method basics — qualitative, quantitative or triangulated
✔️ Usage analytics and A/B testing
✔️ What about COVID-19 and remote usability testing?
This TDWI EU 2012 presentation looks at the various options for implementing a data store for analytical purposes and shows that there's no 'one size fits all' solution available
From Lab to Factory: Or how to turn data into valuePeadar Coyle
We've all heard of 'big data' or data science, but how do we convert these trends into actual business value. I share case studies, and technology tips and talk about the challenges of the data science process. This is all based on two years of in-the-field research of deploying models, and going from prototypes to production.
These are slides from my talk at PyCon Ireland 2015
Industry of Things World - Berlin 19-09-16Boris Adryan
This talk makes the case for a measured use of big data pipelines and analytics methods based on the specific business case: one size doesn't fit all. Rather than buying the fastest stack and the most hyped methods, practitioners interested in analytics for Internet-of-Things deployments can save a lot of money by asking themselves a few questions that I lay out in the talk.
I present the results of an interview study on what type of problems real-life business analysts face and show how CEU's new MSc program helps overcome these problems.
Getting Real with AI - Columbus DAW - May 2024 - Nick Woo from AlignAITim Wilson
Nick Woo from AlignAI presented at the May 2024 Columbus Data and Analytics Wednesday meetup. The topic was a practical, business-oriented approach to identifying use cases for AI.
Power Digital - GA4 & BigQuery - CBUS WAW - Scott Zakrajsek.pdfTim Wilson
Scott Zakrajsek's presentation at Columbus Web Analytics Wednesday in November 2023—a brief overview of Google Analytics 4, how and why it integrates with Google BigQuery, and some of the basics of getting that data back out of BigQuery using SQL.
Columbus WAW 2022 - How GA4 can Help You Navigate a Cookie-Restricted World.pdfTim Wilson
Presentation by Ken Williams and Cory Watson in Columbus on 09-Jun-2022. The session was in-person seminar that went into some of the key aspects of Google Analytics 4 and the main steps organizations should go through in order to adopt the platform, with Google's Universal Analytics slated to go away in July 2023.
Details at https://www.cbuswaw.com/june-2022-google-analytics-4-meetup-and-bonus-seminar
Columbus Web Analytics Wednesday - Google Analytics 4Tim Wilson
Presentation by Ken Williams and Cory Watson at Columbus Web Analytics Wednesday on 08-Jun-2022. The session was an overview of Google Analytics 4 and the main steps organizations should go through in order to adopt the platform, with Google's Universal Analytics slated to go away in July 2023.
Details at https://www.cbuswaw.com/june-2022-google-analytics-4-meetup-and-bonus-seminar
Superweek 2019 - Digital Analytics Meets Data ScienceTim Wilson
Past attendees of Superweek have ridden along with Tim as he explored R, and then as he dove deeper into some of the fundamental concepts of statistics. In this presentation, he provides the latest update on that journey: how he is putting his exploration into the various dimensions of data science to use with real data and real clients. The statistical methods are real, the code is R (and available on GitHub -- see http://bit.ly/ga-and-r), and the data is only lightly obfuscated.
Ruth Milligan - Columbus Web Analytics Wednesday - August 2018Tim Wilson
They want the Teddy Bear, not the stuffing.
Your audiences want to touch, feel and see your message, they don’t want a cascade of stuffing, thread and other parts that may show your expertise but confuse their understanding. Ruth Milligan‘s presentation unpacks specific storytelling strategies every data scientist and analyst needs to embrace as it relates to distilling data into information that resonates. She also discusses the critical tenets of content framing for building short talks to lay audiences.
Ruth Milligan is an executive communication coach and trainer. She works primarily with leaders delivering consequential communications and trains in content framing, storytelling, public speaking and executive presence. She is also one of the longest running TEDx organizers in the world with Nancy Kramer, having organized and curated TEDxColumbus since 2009. Along with her team at Articulation, her current practice, they have coached over 400 TEDx and TED-style talks. They are known for their work in science, research and data fields, helping researchers, scientists and analysts making their insights accessible to lay audiences. She provided coaching to our speakers for Women in Analytics 2018 and has become a trusted partner to data insight teams at MetLife, Nationwide and Ford as well as coaching a variety of speakers at UnitedHealthCare, EcoLab, Express, Nationwide Children’s Hospital and many groups inside The Ohio State University and Wexner Medical Center.
Creative that Counts - Beth Sibbring from Tangible Impact at Columbus WAWTim Wilson
Beth Sibbring from Tangible Impact presented on "(digital) creative that counts" at the September 2017 Columbus Web Analytics Wednesday. The slides focus on digital display creative and outlines -- with examples -- what works and what doesn't (and why so many companies' processes lead to the latter).
Should Digital Analysts Become More Data Science-y?Tim Wilson
Presentation by Tim Wilson at Superweek 2017 in Budapest, Hungary. The presentation explores why digital analysts should consider adding some data science skills to their toolset, what types of tools that entails, and what sort of additional value that will help them deliver to their organizations
This presentation describes a process that marketers and marketing analysts can implement to enable their organizations to become more data-driven. It describes the fundamental differences between performance measurement and hypothesis validation, and then describes a framework/process to "A.D.A.P.T. to Act and Learn" (Align, Discover hypotheses, Assess hypotheses, Prioritize hypotheses, Test hypotheses, Act on the results and Learn for the future).
Tim Wilson's presentation at eMetrics San Francisco in April 2013 covering an approach for analysts to establish and manage a well-defined process for hypothesis discovery, prioritization, and testing to help pivot companies to be data-driven -- to orient towards a culture of validated learning.
Gilligan's Guide to Analysts as Community Managers' Best FriendsTim Wilson
Tim Wilson's Boston eMetrics 2012 presentation of tips and approaches that enable analysts to be highly effective and highly valued through the multi-faceted ways they support their community managers. This presentation is also available on YouTube (with voiceover) at: http://youtu.be/4kW5J8dj46k
A 5-minute blitz presentation from the February 2011 Columbus Web Analytics Wednesday. This was a comparison of the major web analytics platforms -- Google Analytics, Adobe Omniture Sitecatalyst, Webtrends, and Coremetrics. The comparison was limited to the base tools -- not the various add-ons available.
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What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Implicitly or explicitly all competing businesses employ a strategy to select a mix
of marketing resources. Formulating such competitive strategies fundamentally
involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
3.0 Project 2_ Developing My Brand Identity Kit.pptxtanyjahb
A personal brand exploration presentation summarizes an individual's unique qualities and goals, covering strengths, values, passions, and target audience. It helps individuals understand what makes them stand out, their desired image, and how they aim to achieve it.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
At Techbox Square, in Singapore, we're not just creative web designers and developers, we're the driving force behind your brand identity. Contact us today.
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
Sports events - Golf competitions/billiards competitions/company sports events: dynamic and challenging
⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
➢ 2024 BAEKHYUN [Lonsdaleite] IN HO CHI MINH
➢ SUPER JUNIOR-L.S.S. THE SHOW : Th3ee Guys in HO CHI MINH
➢FreenBecky 1st Fan Meeting in Vietnam
➢CHILDREN ART EXHIBITION 2024: BEYOND BARRIERS
➢ WOW K-Music Festival 2023
➢ Winner [CROSS] Tour in HCM
➢ Super Show 9 in HCM with Super Junior
➢ HCMC - Gyeongsangbuk-do Culture and Tourism Festival
➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
Company Valuation webinar series - Tuesday, 4 June 2024FelixPerez547899
This session provided an update as to the latest valuation data in the UK and then delved into a discussion on the upcoming election and the impacts on valuation. We finished, as always with a Q&A
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3. The Single Most UsefulData Visualization Concept Data-Pixel Ratio* = No. of non-white pixels devoted to data Total non-white pixels Stephen Few Edward Tufte * Term coined by Stephen Few as a derivation of Edward Tufte’s “data-ink ratio”
4. Data-Pixel Ratio* = No. of non-white pixels devoted to data Total non-white pixels * Term coined by Stephen Few as a derivation of Edward Tufte’s “data-ink ratio”
5.
6. Redundant info(in this case) Overkill on the size Heavier than needed Adds no value Tough to read at an angle Redundant Calibri is a hideous font (IMHO)
10. Slightly over $150,000… but you have to work to figure that out …and needlessly decreases the data-pixel ratio
11. I defy the laws of light! …and needlessly decreases the data-pixel ratio
12. Leave 3D to the Movies Source: Image captured from recovery.gov (long since removed, thankfully)
13. ~10% of men in the U.S. have some form of color blindness (and a form of red-green color blindness accounts for over half of these)
14. On Screen and Color Printout Red = Green Black-and-White Printout
15.
16.
17. Pie Charts = EVIL! Rainbows Are Good in Princess Tales — Not in Data Visualization Labels, Labels, Labels Those Pesky Near-Zero Values Seeing Small Differences… and 1D vs. 2D Perception Economy (of Space) Is a Virtue
18. If you must use a pie chart… …the sum of the parts must be 100% Source: http://www.math.yorku.ca/SCS/Gallery/
23. Iconic Memory (The “visual sensory register”) Preattentive processing of information Short-term Memory (a.k.a. working memory) Limited to 3 to 9 chunks of visual information Long-term Memory Source: Information Dashboard Design by Stephen Few and Brain Rules by John J. Medina
24. Dashboards: Less is More Aim for a single page/screen: The human struggles to process/compare data that is not in its field of vision (short-term memory limitations) Should provide “at a glance” information – it’s got to be efficient to consume if you expect people to use it Measures included must be tied to objectives and should be organized as such All web analytics vendors have pretty crappy built-in dashboards
26. Sparklines “Historical precision” often not necessary Still effectively enables spotting trends, spikes, and dips Economical use of space…which means more metrics can be digested/compared at once
27. Impressions are down 85% from a year ago Email opens and clickthroughs are dropping Database growth is declining
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30. Further Reading Information Dashboard Design by Stephen Few Brain Rules by John J. Medina Blogs: Peltier Tech Blog (http://peltiertech.com/WordPress/) Presentation Zen by Garr Reynolds (http://www.presentationzen.com/) Flowing Data by Nathan Yau (http://flowingdata.com/)
Editor's Notes
The challenge with data visualization is that it is the combination of two distinct skillsets, and those skillsets aren’t typically ones that both come naturally to a person. The goal – for the purposes of this presentation and for analysts in business – is not to simply make the data “pretty,” but, rather, to make it as easily understood as possible. You want your audience to spend as few brain cycles as possible understanding the data that is being presented to them so that, rather, they can spend those cycles on interpreting the data and making decisions that drive action.This presentation lays out a number of guidelines and examples for doing this.
No single principle drives effective data visualization more than the data-pixel ratio. The goal for any visualization of data should be to minimize the number of pixels that don’t directly communicate information.As it happens, this slide is a complete violation of this concept! The multiple borders, the titles, the pictures of Few and Tufte, and certainly the green gradient background all take away from the core piece of information – what the ratio is.
No single principle drives effective data visualization more than the data-pixel ratio. The goal for any visualization of data should be to minimize the number of pixels that don’t directly communicate information.As it happens, this slide is a complete violation of this concept! The multiple borders, the titles, the pictures of Few and Tufte, and certainly the green gradient background all take away from the core piece of information – what the ratio is.
Top left: a well-meaning analyst had some data and didn’t want to just present in a totally white background, so he highlighted all of it and did “all borders.” That looked too plain, so he made a critical mistake and decided to add to it – it’s much easier for us to make lines heavier and invert cells…when that is often a poor choice. The vertical lines really don’t add value, and the column headings, while needed, are simply reference material. Top right: the vertical lines removed and the column headings made simply bold black on white – the data itself is now equally as weighty as the “decoration.”Bottom left: The horizontal lines separating the rows have been backed off a bit. The human eye can still easily detect them, but they don’t compete with the information itself.Bottom right: Removed the horizontal row lines altogether AND removed the word “Region” from each individual entry. It’s not always the right thing to do to remove horizontal lines, but in many cases they don’t add real value.
This is a default chart generated by Excel 2007 from the data on the previous slide. Applying the data-pixel ratio shows a whole host of opportunities to reduce the non-information pixels.The angled text issue and my personal feelings about Calibri are not data-pixel issues…but we can fix those, too.
This horizonal bar chart addresses all of the issues noted on the previous slide. Notice how the “lit” pixels are almost entirely dedicated to communicating the data itself. And, as a horizontal bar chart, the labels are much easier to read. The horizontal bar chart is woefully underused, and it often enables the tightest, easiest to consume visualization of data.
Microsoft has fallen into the same trap of “improving” Excel through addition. If I look at JUST the first three types of charts available in Excel 2007, only one third of them are ones that I could ever envision myself using. This doesn’t mean the other chart types shouldn’t be available. There may be some oddball scenario where a 3D stacked cone would make sense. But, ALL of these should be buried in the “Other Crazy Options” section of the Chart dialog box.The biggest issue is all of the “3D” options (which, to be clear, are not really going to render anything in true 3D – they’re just going to provide a 2D representation of a 3D visualization, which, we find, leads to more unnecessary effort from your audience to understand the data).
This is the same default chart. We’ve already addressed the issues with this representation, but, to look at the downsides of 3D-ifying examples, it’s a useful place to start. Notice how the Northeast Region is clearly showing Sales that were slightly over $150,000. Now…to the next slide.
When the chart gets made into a 3D chart, the bars are placed in the middle of the “base.” At first blush, it looks like the Northeast Region is right at $150,000. Some people will falsely think that is the actual value. Others will realize (subconsciously) that they need to project the top of the bar back a bit and then follow the gridlines over and around the corner to understand the value actually being represented. This is needlessly making your audience do mental work to understand what is being presented.
At least for 3D charts, Excel applies the “right” math to make the transformation. In the case of shadows, the shadows are actually projected behind the background that has the gridlines. The human brain (again, subconsciously) will be a bit confused by this – is the actual value for each bar, when compared to the gridlines and the values, based on the top of the main bar or on the top of the shadow? Clearly, it’s the top of the main bars…but there is a quick hesitation in trying to make sense of the odd shadow behavior required.
An example of an “artist” getting given some data and told to present it in a way that was engaging and creative. It just isn’t as easily interpreted as it should be.
While not quite “very common,” roughly 5% of the population suffers from some form of color blindness (color blindness is extremely rare in women, but it affects roughly 10% of men). This can make some data visualizations almost impossible to interpret for some people if you are not careful.
In addition to color-blindness,everyone sees information in grayscale that gets printed on a black-and-white printer. These printers are often faster and cheaper than color printers, so expect your data to, at some point, be rendered in grayscale.Notice how, in grayscale, the common red/yellow/green paradigm totally breaks down – only the yellow is distinguishable. The red and green look almost identical!This isn’t to say don’t use color. But, if you do, be sure it merely supplements/reinforces what is already shown. In the example above, for instance, you could only display a circle when the information is bad…and that circle would be red.
This is another example of unnecessarily incorporating color in a visualization. This is the same data set as before, but with each region plotted as its own series. This can happen either inadvertently, or it could be a misguided attempt to solve the “angled text” along the x-axis. The problem is that it requires the viewer to jump back and forth between the legend and the chart to match colors and determine which bar goes with which region.
Viewed in grayscale, the only option for the user is to count – identify, for instance, that the South Region is the third region listed in the legend and that it appears to be a light gray like the third bar in the chart. This is unduly mentally taxing the viewer just to understand the information – mental effort that would be better applied to interpreting the information.
For an exhaustive write-up on these points at http://bit.ly/evilpie .For one of the funniest write-ups that have pie charts as a central figure, see http://bit.ly/piecharts
One example of a pie chart being appropriately used!
The same base chart. A mis-application of the data-pixel ratio would be to see the range from $0 to $150,000 as being redundant because all of the regions exceeded $150,000 in sales. It can be tempting – especially when there is little variation across the values in a series – to shift the y-axis to start at something other than 0. The next slide shows this.
While shifting the bottom of the y-axis to $150,000, you get more granularity in the values, you also get a highly distorted view of the differences between the different regions. At first glance, it looks like the Northwest Region’s sales were many times over the sales of the Northeast and Southeast Regions. This isn’t the case, but the column height is what the viewer will assess initially and most easily, and that presents a misleading picture.
It’s important to understand that the human brain has a very limited ability to hold multiple data points (or data series) in short-term memory at once. Therefore, if there is a relationship between how two metrics are moving over time, that relationship is much more likely to be noticed if both data series are shown on the same screen/page, than if they are on separate screens.
There are LOTS of things wrong with this dashboard:Many violations of the data-pixel ratio – gradient backgrounds, container frames within container frames, an overly heavy logo/navigation areaThe “gauge” is inefficient and ineffective – it doesn’t show how the metric is trending, it is so imprecise that the value itself has to be placed on the gauge, and green/red are vagueThe Regional Performance chart has drop shadows on the lines, and the point markers are unnecessarily large; they also rely heavily on color to identify which region is represented by which lineThe bubble chart needlessly uses a 3D effect; the values themselves wind up overlapping each other, which makes them cluttered and difficult to read; and…it’s not clear what the bubble sizes represent. If they represent the “%” values, they actually aren’t accurately doing so (but, humans are notoriously bad at interpreting the difference in size between two different two-dimensional areas – that’s something not covered in this presentation)
Sparklines, in combination with appropriate labels and some additional data points, provide an effective way to convey how a metric has been trending over time without taking up very much room
I use this as a theoretical “ideal” dashboard. It would only show, in big, red, unequivocal terms, what’s going on that is not expected and that is undesirable. That is really the only information that is going to drive analysis and action.Clearly, in reality, this would not fly. But, the actual dashboard actually adheres to this principle. It doesn’t jump out quite as much as the theoretical one, but the same three trouble spots are clearly evident.Other point: this dashboard is actually another example of how the data-pixel ratio is something that can continue to guide/drive improvement over time. This dashboard was created by someone who knew and followed the data-pixel ratio concept. So, there are a lot of potentially extraneous things not included. But, after this dashboard design was reviewed by a team of people who were well-versed in the concept, a number of opportunities to increase the data-pixel ratio were identified. The next slide shows how the dashboard style has evolved based on that feedback.
I use this as a theoretical “ideal” dashboard. It would only show, in big, red, unequivocal terms, what’s going on that is not expected and that is undesirable. That is really the only information that is going to drive analysis and action.Clearly, in reality, this would not fly. But, the actual dashboard actually adheres to this principle. It doesn’t jump out quite as much as the theoretical one, but the same three trouble spots are clearly evident.Other point: this dashboard is actually another example of how the data-pixel ratio is something that can continue to guide/drive improvement over time. This dashboard was created by someone who knew and followed the data-pixel ratio concept. So, there are a lot of potentially extraneous things not included. But, after this dashboard design was reviewed by a team of people who were well-versed in the concept, a number of opportunities to increase the data-pixel ratio were identified. The next slide shows how the dashboard style has evolved based on that feedback.
Overall, the dashboard has been lightened up. The headings for each of the groups of metrics have gone from being inverted white-on-dark-gray to being simple headings with a light line underneath for delineation. The trend arrows have evolved from being a 5-option (3 of them yellow) set of red/yellow/green to simple grayscale.
There are a lot of resources out there, but I’ve only included the ones that I actually refer to on a regular basis. Some additional notes on the blogs:Peltier Tech Blog – this is a fantastic resource both for how to push Excel pretty hard to achieve the results you want, as well as for best practices (with a lot of “here’s a better way to” posts with data visualizations he has found/seen and how the information could have been more effectively presented)Presentation Zen – Garr Reynolds wrote a book with the same name, and his focus is on presentations, which, really, are a super-set of data visualization; still, his blog is useful, as the principles he espouses for “clearly presenting information” apply to both presentations in general and data visualization in particularFlowing Data – some of Yau’s posts fall into the “clever ways to present data” rather than “the clearest way to present data.” Still, he ferrets out a lot of useful content and provides insightful commentary on it