In this elasticsearch tutorial, you will learn the basic concepts involved in elasticsearch, capacity planning and clustering.
The South China Morning Post print Arcade
Data Science is rapidly growing into an interesting and diverse field encompassing a mixture of deep specialization and broad applications. As we work to define this field it is important that we always have access to the growing landscape of data science concepts, and understand how they branch from high level approaches to specific implementations. In an effort to mix a birds-eye view of this growing landscape with the ability to drill down and learn the concepts, I decided to capture data science approaches and tools visually in an expandable ontology visualization.
Check out GridPlane.com. JD Hooge has posted images of some data visualization concepts he worked on with Google to look at aggregating social media topics.
KNALIJ: knalij.com/ Sci2 (Science of Science) Tool: sci2.cns.iu.edu/user/index.php These two scientific data mining and knowledge visualization tools have a certain amount in common. Both tools are free, and support research and data visualization of the science on a topic. Firstly and foremost for me, both of these are items I discovered through the kindness and sharing of my professional library colleagues. The first time I saw anything about KNALIJ was literally on our library information screen, where one of my peers in our library had selected it as the tool of the week. I have my suspicions as to who might have chosen it, but I'm not sure. However, for Sci2 I definitely know who it was. I had just started to write this post when Kristi Holmes pinged me on Twitter with the info about Sci2. KNALIJ allows you to do a Pubmed search and then displays an interactive bubble map of either the results, co-authorship, journals, or impact of funding. For the results map, it selects a set of the most current articles on your search topic. How many is not something I'm sure of, and I did not see any results earlier than 2009 in my searches. The display shows you the most commonly associated subject headings for your term, with bubbles. The more results for a concept, the larger the bubble; the more central a concept the hotter the colors. The maps are interactive, allowing you to mouse over and get more information about a bubble and its relationships. They also allow you to output a PDF of the generated map. I tried both simple and complex searches. I found some of the most intriguing maps from doing simple searches on large topic domains. More complex searches seemed to have peculiar results that made me question how the interface was mapping the search over to Pubmed. The unclear limit on what subset of the database is being searched also compromises the results for the purpose of doing actual research, however the results are quite fascinating for illustrating concepts for students or an audience. My test was a very quick and dirty one, so perhaps with further exploration of the advanced search features, prefs and search builder I might be able to clarify some of what I was trying to do. Now, for Sci2, it is a similar kind of tool, but more clearly and robustly serving the goals of scientists for doing actual research and creating publishable graphics. In their words: "The Science of Science (Sci2) Tool is a modular toolset specifically designed for the study of science. It supports the temporal, geospatial, topical, and network analysis and visualization of scholarly datasets at the micro (individual), meso (local), and macro (global) levels." Sci2 does not analyze PubMed data but instead the ISI Web of Knowledge. It gives more obvious control of the data set and outputs, but of course demands a greater investment of time in learning how to use it effectively and appropriately. Both of these are interesting tools, useful, but for rather different purposes, despite some of the superficial similarities.
Understanding basic concepts around plotting in R using the ggplot2 package. Based on material presented in ‘Building Data Visualization Tools’ course by JHU at Coursera #R #plotting #ggplot2
Wrapping your brain around data online can be challenging, especially when dealing with huge volumes of information.
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Data visualization designed by Vanilla Matcha. Connect with them on Dribbble; the global community for designers and creative professionals.
Data Visualization - Music Moods on Behance:
Took these photos from the RRR Forum. Thought they were interesting and I know you like pics. Honda MC engine ........... & check the steering rack...
Plotly's dash framework allows the user to build high quality interactive visualizations. This article provides a high level overview of the process.
Visualizing a year's worth of flights of Transavia driven by both art and data
Understanding basic concepts around plotting in R using the ggplot2 package. Based on material presented in ‘Building Data Visualization Tools’ course by JHU at Coursera #R #plotting #ggplot2
data visual design, data visualization and design, data visualization interaction design 🌟 Welcome to our channel! 🌟 Dive into the fascinating world of dat...
Whether you're a data enthusiast, a business professional, or simply someone curious about the world around you, this video is for you!Here's what you'll dis...
This LibGuide collects resources and tutorials related to data visualization. It is a companion to the visualization services provided by Data and Visualization Services at Duke University Libraries. This is a gallery of common data visualization types that are general enough for many data sources.
