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Interactive Data Visualization for the Web

Interactive Data Visualization for the Web
Copyright © 2013 Scott Murray Printed in the United States of America. O’Reilly books may be purchased for educational, business, or sales promotional use. Nutshell Handbook, the Nutshell Handbook logo, the cover image, and the O’Reilly logo are registered trademarks of O’Reilly Media, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. While every precaution has been taken in the preparation of this book, the publisher and author assume no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. Related:  Visualization

VISUALIZING MATHS & PHYSICS D3 Tips and Tricks by Malcolm Maclean D3.js can help you make data beautiful. D3 Tips and Tricks is a book written to help those who may be unfamiliar with JavaScript or web page creation get started turning information into visualization. Data is the new medium of choice for telling a story or presenting compelling information on the Internet and d3.js is an extraordinary framework for presentation of data on a web page. Is this book for you? It's not written for experts. It's put together as a guide to get you started if you're unsure what d3.js can do. Why was D3 Tips and Tricks written? Because in the process of learning things, it's a great way to remember them if you write them down :-). As a result, learning how to do cool stuff with D3 meant that I accumulated a sizeable number ways to help me out when the going got tricky. So here we are! What's in the book? But wait! There are over 50 code examples that are used in the book (with their data files) available to download (still free!) The awesome that is Open Source.

Max Roser – Economist Text Mining Tool | Theory and Applications Edited by Shigeaki Sakurai, ISBN 978-953-51-0852-8, 226 pages, Publisher: InTech, Chapters published November 21, 2012 under CC BY 3.0 licenseDOI: 10.5772/3115 Edited Volume Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Northwestern University Center for Interdisciplinary Exploration and Research in Astrophysics - Stellar Evolution The Formation of Nuclear Star Clusters by Fabio Antonini The three simulations correspond to different initial distributions for the cluster orbits. Most galaxies, including the Milky Way, contain massive (10^7 Solar masses) star clusters at their center. Understanding the formation of such nuclear star clusters is important as it could shed light on the processes that have shaped the central regions of galaxies and led to the formation of their central black holes. This visualization shows the (simulated) formation of a compact nuclear star cluster at the center of the dwarf starburst galaxy Henize 2-10. These clusters, the galaxy (Henize 2-10), and the central BH were realized adopting a particle by particle representation and then evolved forward in time with a GPU-based N-body code. Credit: simulations by Arca-Sedda, M., Capuzzo-Dolcetta, Antonini, F. and Seth., A. Download movies: S1, S2, S3 The Late Evolution of Our Solar System by Aaron Geller Download movie Download movie

UNSW Learning Analytics & Data Science in Education Research Group December 8, 2015 - 'Research Forward': Exploring practical uses of analytics @ UNSW - L Vigentini (UNSW Australia, Learning & Teaching Unit) November 24, 2015 - Evaluating the student experience in Massive Open Online Courses (MOOCs): methods, problems and insights - C. Zhao, L Vigentini (UNSW Australia, Learning & Teaching Unit) November 10, 2015 - Show me my data! October 27, 2015 - MTFeedback: providing notifications to enhance teacher awareness of small group work in the classroom - Dr Roberto Martinez Maldonado (UTS) & Andrew Clayphan (UNSW) October 13, 2015 - Two short talks: 1) Discrimination-Aware Classifiers for Student Performance Prediction - Ling Luo (University of Sydney) 2) Detecting Students at Risk of Failing - A/Prof Irena Koprinska (University of Sydney) September 29, 2015 - Business Intelligence and Analytics v Learning Analytics – Opportunities for cross-pollination of ideas and practices - A/Prof. August 4, 2015- Learning in the MOOCs and learning from the MOOCs - Dr.

VisIt About VisIt VisIt is an Open Source, interactive, scalable, visualization, animation and analysis tool. From Unix, Windows or Mac workstations, users can interactively visualize and analyze data ranging in scale from small (<101 core) desktop-sized projects to large (>105 core) leadership-class computing facility simulation campaigns. Users can quickly generate visualizations, animate them through time, manipulate them with a variety of operators and mathematical expressions, and save the resulting images and animations for presentations. What's New VisIt is a distributed, parallel visualization and graphical analysis tool for data defined on two- and three-dimensional (2D and 3D) meshes. History VisIt was originally developed by the Department of Energy (DOE) Advanced Simulation and Computing Initiative (ASCI) to visualize and analyze the results of terascale simulations. For any additional questions, send e-mail to VisIt Users.

DATORN i UTBILDNINGEN Text:Jan Hylén E-Post: jan@janhylen.se Ny trend: Skolutveckling med egna frågor och dataanalys Hur kan skolans personal själv använda data på ett strukturerat sätt för att åstadkomma skolutveckling? Det är en trend som är i stark tillväxt. Men kan denna trend inrymmas inom modetermen Learning Analytics, eller är den lärardrivna analysen något annat? Nacka och Stockholm har utforskat en metod för datastödd skolutveckling. Modellen har två grundpelare. Exemplet från Stockholm och Nacka kan ses som en jordnära och konkret tillämpning av begreppet Learning Analytics, vilket förekommer allt oftare. Fenomen på frammarsch Oavsett benämning så räknar många experter med att fenomenet snart kommer att prägla utbildningsväsendet. Engelsmännen är inte lika övertygade om att det kommer att få en så stor påverkan på utbildningssektorn som den amerikanska Horizon-rapporten antar. Individnivån Många kommuner och regioner har system för central antagning till gymnasiet. Datorn i Utbildningen nr 3-2014.

Why do we visualize data? Why do we visualize data? Do data visualizations aim to inform audiences effectively? Or do they simply aim to catch people’s eye, providing the just gist of the data? This is a question which has been hotly debated by some of the leading authors in the field of data visualization recently. Data visualization is a spectrum, determined by your data, your objectives and your audience. However, to limit data visualization to just being used for this purpose is stifling and ignores a vast array of other perfectly valid reasons we might visualize data. Consider Stefanie Posavec, one of many incredibly creative people who use data visualization to make art. Is it data, visualized? I asked Stefanie about the purpose of her work. What about the importance of informing people? “I’m always trying to inform my audience, but the level of information can vary from the gist to something more detailed and in-depth,” she said. Maybe you’re going to be doing a data-driven presentation for your managers.

Signals: Applying Academic Analytics Key Takeaways Applying the principles of business intelligence analytics to academia promises to improve student success, retention, and graduation rates and demonstrate institutional accountability. The Signals project at Purdue University has delivered early successes in academic analytics, prompting additional projects and new strategies. Significant challenges remain before the predictive nature of academic analytics meets its full potential. Academic analytics helps address the public’s desire for institutional accountability with regard to student success, given the widespread concern over the cost of higher education and the difficult economic and budgetary conditions prevailing worldwide. Student success algorithms customized by course Intervention messages sent to students New strategies for identifying students at risk Today, more than 11,000 students have been impacted by the Signals project, and more than 50 instructors have used Signals in at least one of their courses.

Subtleties of Color (Part 1 of 6) : Elegant Figures : Blogs Introduction The use of color to display data is a solved problem, right? Just pick a palette from a drop-down menu (probably either a grayscale ramp or a rainbow), set start and end points, press “apply,” and you’re done. Although we all know it’s not that simple, that’s often how colors are chosen in the real world. As a result, many visualizations fail to represent the underlying data as well as they could. The purpose of data visualization—any data visualization—is to illuminate data. Encoding quantitative data with color is (sometimes literally) a simple matter of paint-by-numbers. In spatial datasets [datasets with at least two dimensions specifying position, and at least one additional dimension of quantity (a category that includes not only maps, but everything else ranging from individual atoms to cosmic background radiation)] color is probably the most effective means of accurately conveying quantity, and certainly the most widespread. In short, people aren’t computers.

Learning Analytics: Leveraging Education Data [Infographic] : Causation vs Correlation: Visualization, Statistics, and Intuition Visualizations of correlation vs. causation and some common pitfalls and insights involving the statistics are explored in this case study involving stock price time series. By Alex Jones, Jan 2015. As someone who has a tendency to think in numbers, I love when success is quantifiable! So, I looked into how my working at Cameron relates to the company's stock price. Alongside this analysis, I'll demo scaling and data manipulation to illustrate basic lessons of visualization, statistics, and analysis. First, I pulled Stock Price over my first ~90 Days, which aligns perfectly with Days Worked. Then simply added a rolling count of days, how convenient! Example: Neat! Super! Not so fast… let’s Regress Days Worked across Stock Price. It's important to realize that while visualization is a powerful tool and incredibly insightful way to ingest data, it's not the whole story. Blasphemy! So what do all those numbers really say? StockPrice= $75.99 -$.29672(NumberDaysAlexHasWorked) WRONG. Let’s graph!

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