Whichgraph? A research portfolio about visual explanations, learning and interactivity. What data are you using to tell your visual story? So You Think You Can Scroll. Jim Vallandingham @vlandham Abusing The Force So You Think You Can Scroll Scrolling A Short History.
GitHub - cookiengineer/machine-learning-for-dummies: Machine Learning for Dummies (aka Artificial Intelligence aka Deep Learning) Machine Learning for Dummies: Part 2 – Chatbot’s Life. The last article covered an introduction to Neural Networks and Evolutionary AI Concepts, in particular Genetic Programming and NEAT.
In case you’ve missed it, it is required to understand this article. Machine Learning for Dummies: Part 1 – Chatbot’s Life. I often get asked on how to get started with Machine Learning.
Most of the time, people have troubles understanding the maths behind all things. And I have to admit, I don’t like the maths either. Math is an abstract way of describing things. And I think the way machine learning is described is too abstract to understand it easily. A Complete Tutorial to learn Data Science in R from Scratch.
Introduction R is a powerful language used widely for data analysis and statistical computing.
It was developed in early 90s. Since then, endless efforts have been made to improve R’s user interface. The journey of R language from a rudimentary text editor to interactive R Studio and more recently Jupyter Notebooks has engaged many data science communities across the world. This was possible only because of generous contributions by R users globally. But, what about Machine Learning ? My first impression of R was that it’s just a software for statistical computing. Data visualization: A view of every Points of View column : Methagora. We’ve organized all the Points of View columns on data visualization published in Nature Methods and provide this as a guide to accessing this trove of practical advice on visualizing scientific data.
As of July 30, 2013 Nature Methods has published 35 Points of View columns written by Bang Wong, Martin Krzywinski and their co-authors: Nils Gehlenborg, Cydney Nielsen, Noam Shoresh, Rikke Schmidt Kjærgaard, Erica Savig and Alberto Cairo. A Snapshot of Current Trends in Visualization. Guest Editors’ Introduction • Theresa-Marie Rhyne and Min Chen • February 2017 Read the Guest Editors’ Introduction inSpanish | Chinese Translations by Osvaldo Perez and Tiejun Huang Listen to the Guest Editors' Introduction.
One Dataset, Visualized 25 Ways. “Let the data speak.”
It’s a common saying for chart design. The premise — strip out the bits that don’t help patterns in your data emerge — is fine, but people often misinterpret the mantra to mean that they should make a stripped down chart and let the data take it from there. You have to guide the conversation though. You must help the data focus and get to the point. Otherwise, it just ends up rambling about what it had for breakfast this morning and how the coffee wasn’t hot enough. What Makes A Good Data Visualization? — Information is Beautiful. Hi there.
IBM Design Language. Visualizing data is central to this key moment in time, when the borders between big and impersonal, and small and intimate data will blur as we’ve never seen before.
The greater the quantity and kinds of data collected, the more we need to experiment with how to make it unique. Instead of starting from standards, begin from a blank page and experiment with a custom visualization. Even if you come back to the basics, small details from your process and play can enhance basic charts to reveal more about topics users are interested in. Business intelligence tools lead people to believe that the ideal process to create visualizations is to load data in a tool, pick from among a list of out-of-the-box charts, and get the job done in a couple of clicks.
Yet, simplified solutions are rarely able to frame hard-to-define problems, let alone solve them. Data Journeyman. TimeViz Browser. Tutorials and Resources Archives. The Data Visualisation Catalogue. Motion Periodic Table. D3 in Depth. Interactive Data Analysis - Jeffrey Heer - May 23, 2013. 39 studies about human perception in 30 minutes. Bars and pies for proportions Much is said about the relative merits of bars and circles for showing proportions.
All five of these studies legitimize the use of pie charts when conveying proportions and some even show their superiority over bar charts. I did not encounter any studies that said we should not use pie charts for showing proportions in all cases. Eells (7) was among the first to publish a paper on this topic in 1926. In his time, pie charts were ridiculed much as they are today for their assumed perceptual inadequacies. He also wanted to know more about how circles were processed. Not only did he find that pie charts were read as easily, quickly and accurately as bar charts, but that as the number of components in the chart increased, bars become less efficient encoding the data. He found that 50 percent of people use the outer arc to make proportional judgments, while 25 percent use area, and the other 25 percent use the inner arc or angle. Archived Webinar: Learn the Essentials of Data Visualization.
Categories: Opportunities, Webinar Tags: Data Visualization, Webinar Earlier this month, we held the webinar, Learn the essentials of data visualization, with Stephanie Evergreen, a US-based expert on using research to present data effectively, and Andy Kirk, a UK-based apostle for better designed data visualization. In this one-hour webinar, Evergreen and Kirk addressed the basics of data design and demonstrated various ways for showing different types of data. The archive of their webinar appears above.
The webinar included a lively question and answer session with the audience, but there was not enough time to get to all of the questions so the speakers kindly provided follow-up answers here. 7 Data Visualization Types You Should be Using More (and How to Start) 7 Data Visualization Types You Should be Using More (and How to Start) Unique data visualizations are more memorable, and add variety for the audience — even the most clear and straightforward visualization types lose their appeal when repeated over and over again. As visual literacy increases in the general population, data visualization designers will need to continually extend their knowledge of and proficiency across a widening range of visualization approaches to grow their skills alongside audience familiarity and expectations.
Even more importantly, broad visualization know-how is essential for matching the data visualization type to the data available, the story to be told, and the question being answered. In this article, I review 7 less-common (though certainly not unheard-of) yet very useful data visualization approaches: SlopegraphsParallel CoordinatesAlluvial DiagramsSunburstsCircle PackingHorizon ChartsStreamgraphs Two “In the Wild” Examples. Declutter your data visualizations — storytelling with data. Dear Data Two. How Information Graphics Reveal Your Brain’s Blind Spots. This story was co-published with Source. Visual Evidence Data and design in everyday life Lena Groeger Welcome to Visual Evidence, a new regular series about visualization in the real world!
We’ll take a look at unexpected datasets, cool design solutions or insightful graphics. Chances are, you probably think your mind works pretty well. But you’d be wrong. Let’s look at some of the wacky things our minds make us think and do. Our Mind’s Everyday Quirks We’ll start with a study of Israeli judges done by researchers at Ben-Gurion University of the Negev in Israel and Columbia University. Yup. As you can see in the chart, the rate of favorable rulings starts at around 65% early in the day, then drops to almost zero, and then spikes back up again after the judges come back from a meal break. The paper ominously concludes: “Indeed, the caricature that justice is what the judge ate for breakfast might be an appropriate caricature for human decision-making in general.” Visuals Fool Our Minds, Too. 7 Data Visualization Types You Should be Using More (and How to Start)
7 Data Visualization Types You Should be Using More (and How to Start) Unique data visualizations are more memorable, and add variety for the audience — even the most clear and straightforward visualization types lose their appeal when repeated over and over again. As visual literacy increases in the general population, data visualization designers will need to continually extend their knowledge of and proficiency across a widening range of visualization approaches to grow their skills alongside audience familiarity and expectations. Even more importantly, broad visualization know-how is essential for matching the data visualization type to the data available, the story to be told, and the question being answered. In this article, I review 7 less-common (though certainly not unheard-of) yet very useful data visualization approaches: SlopegraphsParallel CoordinatesAlluvial DiagramsSunburstsCircle PackingHorizon ChartsStreamgraphs.
Junk Charts Trifecta Checkup: The Definitive Guide. The Junk Charts Trifecta Checkup is a general framework for data visualization criticism. It captures how I like to organize the thinking behind my critique pieces. The need for such a framework is clear. Tell a Meaningful Story With Data. Rudyard Kipling once wrote, “If history were taught in the form of stories, it would never be forgotten.” The same applies to data. Companies must understand that data will be remembered only if presented in the right way. All the 'little of visualisation of design'
This is a collection of the entire, growing series of posts about the 'little of visualisation design', respecting the small decisions that make a big difference towards the good and bad of this discipline. In each post I'm going to focus on just one small matter - a singular good or bad design choice - as demonstrated by a sample project.
Each project may have many effective and ineffective aspects, but I'm just commenting on one. Datavisualization.ch. DataIsBeautiful. Which chart or graph is right for you? D3 Tutorials, D3 Screencasts, and a D3 Newsletter. Happy (Belated) PiDay 2016! References for visualising uncertainty. Data Visualization and Analytics to Grow your Business. Home - Seeing Data.