• d3.sf("Data Dive")

    App Academy

    2
  • d3.sf("Data Dive")

    App Academy

    1
  • d3.sf("Data Dive")

    App Academy

    We're back with a data dive! Join us at App Academy (https://www.appacademy.io/) to dive into some open datasets. We'll create visualizations, get un-stuck, and have a data-driven good time. To make the most of the night, plan to come out with a hack-size vis project in mind. Remember, these jam sessions are not exclusive to those steeped in D3 experience. These are meant to be smaller sessions than our norm where data divers from all areas come together to show interest, offer encouragement, struggle with friends, put their fingers to the keys and learn. To improve your data visualization skills you have to practice. • Quick ways to build d3 examples: interactive editor (http://blockbuilder.org/) code notebook (https://beta.observablehq.com/) • Search the d3 examples to fill your mind with ideas (http://blockbuilder.org/search) • A gallery for even more ideas (https://github.com/mbostock/d3/wiki/Gallery) • Tutorials to exercise your mind (https://github.com/mbostock/d3/wiki/Tutorials)

    2
  • d3.sf("Data Dive")

    App Academy

    We're back with a data dive! Join us at App Academy (https://www.appacademy.io/) to dive into some open datasets. We'll create visualizations, get un-stuck, and have a data-driven good time. To make the most of the night, plan to come out with a hack-size vis project in mind. Remember, these jam sessions are not exclusive to those steeped in D3 experience. These are meant to be smaller sessions than our norm where data divers from all areas come together to show interest, offer encouragement, struggle with friends, put their fingers to the keys and learn. To improve your data visualization skills you have to practice. • Quick ways to build d3 examples: interactive editor (http://blockbuilder.org/) code notebook (https://beta.observablehq.com/) • Search the d3 examples to fill your mind with ideas (http://blockbuilder.org/search) • A gallery for even more ideas (https://github.com/mbostock/d3/wiki/Gallery) • Tutorials to exercise your mind (https://github.com/mbostock/d3/wiki/Tutorials)

    4
  • Panel Discussion with Zan Armstrong, Nadieh Bremer, and Tamara Munzner

    Join us at App Academy (https://www.appacademy.io/) for a conversation about data visualization in the worlds of academia, industry, and freelance work. We have an amazing panel of guests in town for the Exploratorium's Visualise conference (https://www.exploratorium.edu/visualise): • Nadieh Bremer, independent (https://twitter.com/nadiehbremer) • Zan Armstrong, Google (https://twitter.com/zanstrong) • Tamara Munzner, University of British Columbia (https://twitter.com/tamaramunzner) -- **Schedule** 6pm: doors open for food & socializing 7pm: panel discussion begins 8pm: show & tell We'd love to hear your questions for our panel! Please comment them on Meetup or tweet at us: Shirley Wu (@sxywu) and Mason Chinkin (@MChinkin). Finally, get involved in our show & tell - share your recent projects and work-in-progress with us! -- **Full Bios** Nadieh Bremer is a graduated Astronomer, turned Data Scientist, turned Data Visualization Designer, based near Amsterdam. After working for a consultancy & fintech company where she discovered her passion for the visualization of data, she's now working as a freelancing data visualization designer under the name "Visual Cinnamon". As 2017's "Best Individual" in the Information is Beautiful Awards, she focuses on uniquely crafted (interactive) data visualizations that both engage and enlighten its audience. Ranging from companies as extensive as Google News Lab and UNESCO to small start-ups. From printed magazines such as Scientific American to an interactive experience for the Guardian to more promotionally focused artful visualizations for press releases, data-driven reports, and data art for in the office. As long as there's data that has a story to reveal. Zan Armstrong is a data visualization engineer and designer. As part of Google’s Accelerated Science team, Zan creates custom visualizations for ml experts, data analysts, and scientists to help them to make scientific discoveries. With a background in mathematics and data analysis, she is especially fascinated by identifying what characteristics of the data might be most important and creating visual forms that best reveal those characteristics. She also has explained why Everything is Seasonal at OpenVis Conf, gave recommendations for Data Visualization for Analysis and Discovery at the SciPy conference, been exhibited at SF MoMa for work done with Stamen Design, won an Information is Beautiful award for an article published in Scientific American, and published research in IEEE InfoVis on Visualizing Statistical Mix Effects and Simpson's Paradox. Tamara Munzner is a computer science professor at the University of British Columbia and holds a PhD from Stanford. She has been active in visualization research since 1991, has published over sixty-five papers, and co-chaired InfoVis and EuroVis. Her book Visualization Analysis and Design appeared in 2014, and she received the IEEE VGTC Visualization Technical Achievement Award in 2015. She is the editor of the AK Peters Visualization series with CRC Press. She has worked on problem-driven visualization in many domains including genomics, computational linguistics, web log analysis, and journalism. Her technique-driven visualization interests include graph drawing and dimensionality reduction. Her evaluation interests include controlled experiments in a laboratory setting and qualitative studies in the field.

    5
  • d3.southBay('Meetup')

    LinkedIn Building R (LSNR)

    Join us at LinkedIn (https://www.linkedin.com/) for a d3 meetup celebrating interactive data visualization in the browser. Join us for a behind-the-scenes conversation with some of the authors from the Machine Learning Research Journal distill.pub (https://distill.pub/about/). We'll hear from Shan Carter (https://twitter.com/shancarter) on just how the interactive diagrams in the recently published Activation Atlas (https://distill.pub/2019/activation-atlas/) are built (with d3js!). Come learn about creating building blocks (https://distill.pub/2018/building-blocks/) for visually exploring the data-spaces inside of machine learning models. After the talks, we'll have time for show & tell, to see recent projects from our d3 community. We'd love to see what you are building :-) --- • Quick ways to build d3 examples: interactive editor (http://blockbuilder.org/) code notebook (https://beta.observablehq.com/) • Search the d3 examples to fill your mind with ideas (http://blockbuilder.org/search) • A gallery for even more ideas (https://github.com/mbostock/d3/wiki/Gallery) • Tutorials to exercise your mind (https://github.com/mbostock/d3/wiki/Tutorials)

    19
  • d3.sf("Dashboards")

    App Academy

    Join us at App Academy (https://www.appacademy.io/) for a night of conversations about Dashboards, that famous form of linked visualizations for filtering and data analysis. @teachrdan (https://twitter.com/teachrdan) will take us through a tour of the design space for these data dashboards. on dashboards: http://datastori.es/135-the-dashboard-conspiracy-with-lyn-bartram-and-alper-sarikaya/ --- after Dan's talk, we'll have d3 show and tell, as is our custom. We'd love to see what you are building :-) --- some resources for d3 generally: • Quick ways to build d3 examples: interactive editor (http://blockbuilder.org/) code notebook (https://beta.observablehq.com/) • Search the d3 examples to fill your mind with ideas (http://blockbuilder.org/search) • A gallery for even more ideas (https://github.com/mbostock/d3/wiki/Gallery) • Tutorials to exercise your mind (https://github.com/mbostock/d3/wiki/Tutorials)

    1
  • d3.sf("Data Dive")

    App Academy

    We're back with a data dive! Join us at App Academy (https://www.appacademy.io/) to dive into some open datasets. We'll create visualizations, get un-stuck, and have a data-driven good time. To make the most of the night, plan to come out with a hack-size vis project in mind. Remember, these jam sessions are not exclusive to those steeped in D3 experience. These are meant to be smaller sessions than our norm where data divers from all areas come together to show interest, offer encouragement, struggle with friends, put their fingers to the keys and learn. To improve your data visualization skills you have to practice. • Quick ways to build d3 examples: interactive editor (http://blockbuilder.org/) code notebook (https://beta.observablehq.com/) • Search the d3 examples to fill your mind with ideas (http://blockbuilder.org/search) • A gallery for even more ideas (https://github.com/mbostock/d3/wiki/Gallery) • Tutorials to exercise your mind (https://github.com/mbostock/d3/wiki/Tutorials) there will be pizza 🍕 Thanks to Primer for sponsoring the food! https://primer.ai/ https://primer.ai/careers/

    5
  • d3.sf("Data Dive")

    App Academy

  • A Pseudorandom Walk Through d3-random with Curtis Mitchell

    # Description Data visualization holds a deep interest for many people because it occupies the intersection of so many disciplines such as graphic design, software development, and psychology. Discussions and demonstrations regarding working with d3.js often include aspects of software and design but sometimes overlook some of the more mathematical and statistical parts of its API. This talk will be a deep dive of one of these more quantitative modules in d3: d3-random, a module designed for generating random numbers from various statistical distributions. We will walk through what each of these distributions entails and give some examples of their application. We will also touch on related concepts from math and statistics such as the central limit theorem and define what randomness means in the contexts of statistics and software engineering. Hopefully by the end of this talk you’ll have a deeper understanding of the methods in the d3-random module as well as a better appreciation for the intersection of mathematics and data visualization. d3-random: https://github.com/d3/d3-random -- # Speaker bio Curtis Mitchell studied math and physics at the University of North Texas and caught the data visualization bug while working as an analyst in the energy industry. Since making a career shift to web development he has worked at several data analysis and machine learning startups. He is currently at Mode Analytics where he is helping to build a web application focused on serving the needs of analysts and data scientists. Twitter: https://twitter.com/Curt_Mitch -- # Schedule: 6:30PM Doors open 7PM Talk starts 7:30PM Show & Tell

    8