Elizabeth Renter

🌟 Exciting New Episode of the PolicyViz Podcast! 🌟 This week, I chat with author and teacher Nick Desbarats about his new book, “Practical Charts.” Dive deep into the art and science of creating effective charts for impactful data visualization. 📊✨ 🔍 What to Expect: ➡️Learn how to choose the right chart types for your data ➡️Challenges conventional wisdom on charts, especially the pie chart ➡️Gain insights into the transition to online teaching ➡️Discover the difference between creating clear, effective graphs and overwhelming dashboards. 📈 Whether you’re looking to grab attention or ensure your data is clearly understood, this episode will equip you with the knowledge to create visuals that truly convey your message. #DataVisualization #PolicyVizPodcast #PracticalCharts #NickDebra #ChartDesign #DataScience #Analytics #PodcastRecommendations Listen now: https://lnkd.in/ecuZ8J3Y

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New set of #dataphys projects uploaded to my ongoing blog post of projects I've been doing. You too can engage and entertain your friends, family, and students by providing the opportunities to create data visualizations with stuff lying around the house. https://lnkd.in/gXMgqh3x

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🎇🎇🎇🎇🎇🎇🎇🎇 I'm really excited to launch the Back to Viz Basics: Flourish Edition project! Following #B2VB, we'll make graphs in Flourish and make better, interactive, and responsive graphs, charts, cards, and much more! Every other week, Eric Balash & team post a dataset for us to explore and work with. To date, it's mostly been Tableau folks participating, but I think it's valuable to try other tools. Follow along as we create graphs in Flourish and, together, we learn how to make great graphs and charts. All the details you need are right here: https://lnkd.in/efHKMiJq

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Synthetic Data –an Overview, a Taxonomy & some FAQs - the title of my latest blog https://lnkd.in/exWfryJS One of my key points, based on recent research by #NewMR is that whatever your views on Synthetic Data, you are in a minority. The current range of views encompasses lots of different views and none of them represents a majority view. I think this means we all need to be a little but cautious in our pronouncements and recommendations. I hope you find my post useful and I'd love to hear your take on it? Judith Passingham, Dharmendra Jain, Agus Elissondo, Colin Strong, Christian Walsh, Barry Jennings, Sue York, Karine Pepin, Alexandra Kuzmina, Ben Leet, StJohn "Singe" Deakins, Yogesh Chavda, Hans Zijlstra, Sidi Lemine, Jonathan (JD) Deitch, Adriana R., Frank Pica, Febronia Ruocco, Scott Swigart, Jerome Sopocko, Paige Schoenfeld, Jean-Marc Leger, Leonardo Valente, Gabriela Kusters, Germaine Reyes, Ravinder Roopra, Melanie Courtright, Renee Smith, Kathy Joe, Elise Misse, Lisa Salas

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🎇🎇🎇🎇🎇🎇🎇 Season 10 finale of the PolicyViz Podcast with Nancy Organ and her new book, Data Visualization for People of All Ages. We talk about a range of issues relating to data visualization education and literacy. ➡️Importance of Data Visualization. Nancy underscores the necessity of data visualization skills in the modern world and how these skills can be nurtured from a young age. Nancy’s book aims to demystify complex data concepts, starting with basic data units and building up to more sophisticated visual representations. ➡️Educational Approach. Her book includes self-assessment tools and classroom exercises to facilitate learning with an emphasis on making the content relatable and straightforward for a broad audience. ➡️Ethics in Data Visualization. There is a strong focus in Nancy’s book and in our conversation on maintaining data integrity and making ethical choices in visual storytelling. ➡️Techniques and Tools. We also discuss various data visualization formats such as timelines and flowcharts and how to understand different data encodings to enhance perceptibility and engagement. ➡️Incorporating Visualization into Education. Finally, Nancy and I talk about integrating data visualization and data science into the K-12 curriculum and how to promote visual thinking across multiple disciplines and learning environments. Thanks for tuning in over this season! I hope you learned a lot about creating and communicating your data and analysis. Be on the lookout for great new episodes coming this fall. In the meantime, enjoy your summer!!!! 🎙Listen: https://lnkd.in/edYPM8kF 📺Watch: https://lnkd.in/ezvb65mZ

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🔢 Currently reading and thinking about how much data's consumed in tabular form -- easy to confuse & difficult to tell a story 🔢 Bureau of the Census Manual of Tabular Presentation (1949) An outline of theory and practice in the presentation of statistical data in tables for publication https://lnkd.in/eD3wt7U9

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Some things do run in cycles. In the mid-1990s I wrote a sometimes-funny column for Datamation magazine. This concocted item (from the column) foreshadowed the arrival three decades later of ChatGPT: --- Termpaper mills, a funky, if supposedly sleazy, innovation of the 1960's and 70's, are rushing to the aid of computer makers and users. The same people who once cranked out college termpapers on Beowulf, Jean- Paul Sartre, and the Tennessee Valley Authority are now whipping up the manuals that officially document systems software, compression packages, and client/server applications. When a project is ready to deliver and the documentation is nowhere near ready, more and more technical writers are turning to so-called "documentation mills." An outgrowth of the college termpaper mill business, most of the documentation mills are run by individuals from their homes. The high quality level of college termpapers in 1966 is still evident in the computer documentation of 1996. The mills charge by the page. Many guarantee that their documentation will be indistinguishable "for all practical purposes" from the real thing, and offer a money-back guarantee if any complaints are received about their work. Of the documentation mills we surveyed, none reported ever having to refund money. This has gotten beyond the stage where the main customers are individual technical writers who missed their own deadlines. Many companies are downsizing (i.e., shrinking and pulverizing) their technical writing staff, and outsourcing (i.e., cheaply subcontracting) all of their documentation to documentation mills. ---

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Before being impressed by an out-of-sample statistical result presented, try to question the reliability, or validated forecastability, conditional on the observed out-of-sample results.

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DEADLINE APPROACHING! 🌟 Are you headed to EDM 2024 in Atlanta? Consider submitting to the workshop on Causal Inference in Educational Data Mining (EDM) research! Join SEERNet members Neil Heffernan and Avery Closser as they explore the critical role of causality in enhancing student learning through intelligent tutoring systems, educational games, MOOCs, and more. In today's digital learning landscape, understanding causality goes beyond predicting learner behaviors to unraveling the underlying factors impacting educational outcomes. The workshop aims to stimulate discussion and share insights on examining causality within EDM contexts and leveraging EDM to support causal inference methodologies. 📝 Call for Papers/Presentations: To participate, please submit on topics including A/B testing, Bayesian networks, analyzing data from randomized experiments, multi-armed bandits, causal mechanism analysis, and more. Whether you're presenting completed work or exploring open problems in the field, you are invited to contribute to the conversation! - Submission Deadline: June 10, 2024 [Link: https://bit.ly/3WfbSC2]

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We prioritize reliability and accuracy in probabilistic forecast distributions. TLDR: customers appreciate this rigor. For the deep dive, tune into Salient Climate Scientist Brian Zimmerman's talk on data best practices in S2S forecasting for US CLIVAR: https://lnkd.in/eEMDu8kq

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The {summarytools} #rstats 📦 aims to: “Provide a coherent set of easy-to-use descriptive functions [like] those in commercial statistical software suites such as SAS, SPSS, and Stata “Offer flexibility in terms of output format & content “Integrate well with commonly used software & tools for reporting” Results can be displayed in console or rendered/saved as HTML, plain text, or R Markdown. By Dominic Comtois https://lnkd.in/eGzw3WWE #EDA

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New in CODATA Data Science Journal: Ruth Duerr, Pier Luigi Buttigieg, Gary Berg Cross, Kai Lewis Blumberg, Brandon Whitehead, Nancy Wiegand, Kate Rose Title: Harmonizing GCW Cryosphere Vocabularies with ENVO and SWEET. Towards a General Model for Semantic Harmonization https://lnkd.in/dwYuBgQg #codata #FAIRdata #OpenScience #datascience #opendata #datasciencej

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Excited about AI's capabilities in data analysis, literature reviews, and manuscript proofreading? Concerned it might replace researchers? Join me and Abhigyan Arun Damita Snow, CAE, ENV SP Penelope Lewis and Stephanie Lovegrove Hansen as we explore 'The Other AI: The Role of Actual Intelligence in the Future of Scholarly Publishing.' Let's separate the hype from reality and discuss how AI can complement rather than replace human expertise. Very encouraging to see great participation already! #SSP2024

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Today at 1:15 PM EST learn more about Risk Quadrangle and Applications in Statistics, Data Mining and Portfolio Optimization Stan Uryasev, Professor at Stony Brook University, will discuss the Expectile, Conditional Value-at-Risk (CVaR), Value-at-Risk (VaR), and other risk measures within the framework of Risk Quadrangle (RQ) theory in his presentation. Every Quadrangle includes four stochastic functions: Error, Regret, Risk, and Deviation. These functions are interconnected through a Statistic function. RQ links optimization, risk management, and statistical estimation in one theory. We investigate Support Vector Regression (SVR) with RQ theory. Epsilon-SVR and nu-SVR are reduced to minimization of Vapnik error and Conditional Value-at-Risk (CVaR) norm, accordingly. The Vapnik error and CVaR norm define quadrangles with Statistic equal to an average of two symmetric quantiles. Therefore, epsilon-SVR and nu-SVR are equivalent asymptotically unbiased estimators of an average of two symmetric conditional quantiles. We present also a new Expectile Quadrangle based on Piecewise Linear Error, which is an alternative to the standard Asymmetric Variance Error. We proved equivalence of Expectile regression with these two Errors as well as equivalence of Expectile and Quantile regression. We study also the f-divergence primal and dual quadrangles. The framework provides an interpretation of portfolio optimization (e.g., Markowitz optimization) and regression (e.q., LS regression) as a robust optimization. Theoretical findings are validated with several case studies. To learn more visit: https://lnkd.in/eQjjaD4Z #arpm #datamining #quantbootcamp #portfoliooptimization

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Happy to announce that “Thomas Sowell: A Misunderstood Economist” is OUT NOW and available for purchase on the GGV Publishing website’s and Amazon.com Enjoy your reading https://a.co/d/dD33KiR Abstract: Dr. Thomas Sowell is one of the most influential economists of the late-twentieth century and early-twenty-first century. Beyond being a trained economist, Dr. Sowell has made significant contributions to social theory, political philosophy, and social history. His contributions to scholarship could be reflected in his extraordinary intellectual output as he published more than forty books throughout his intellectual life. Despite being an influential economist, Dr. Sowell was also a very controversial public intellectual. Controversial not for propagating conspiracies but controversial for defending what’s supposed to be obvious to everyone. Throughout his life, Dr. Sowell used the pen as a sword to fight for what believed was right; the preservation of individual liberty and a free society. However, his fight for freedom was resented by the mainstream media, the intelligentsia, the political class, and the Black community. While the mainstream media, the intelligentsia, and the political class labeled him as “cold” and “insensitive” to the struggle of the lower classes; the Black community pigeonholed him as a “sellout,” and an “Uncle Tom.” As a result, Dr. Sowell’s reach to the mainstream was severely limited, and the Nobel Prize in Economics has eluded him. On the other hand, conservatives have highly praised him for his academic rigor, and use of logical reasoning to ascertain his stances. The objective of this biographical work is to provide a detailed account of Dr. Sowell’s work, the rationale behind his stances, and a thorough portrait of the man’s intellectual life, which explains why he was misunderstood.

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Have you heard about our upcoming "Leveraging the Data Explosion for the Public Interest" masterclass? 🧐 Led by the esteemed Danielle Goldfarb, this masterclass will expose participants to a myriad of non-traditional datasets, supporting the understanding of their potential to better target public resources and confronting their risks. Danielle covered some of these themes in an April 2024 paper published by the CSA Group, 'Real Time: Leveraging the Data Explosion for Better Policy.' Join us on June 5! 🗓️ For more information and to register: https://bit.ly/3W9nQ04

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In my data science journey, I'm thrilled to share end-to-end data science project: Predicting Car Prices. This journey has been an exhilarating experience, from wrangling data to deploying a predictive model with Streamlit. https://lnkd.in/ds8SwADR

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Order your copy of “Economic Ignorance” on the GGV Publishing’s website or Amazon.com. This thought-provoking book provides an alternative, yet research-based perspective on the handling of COVID-19, and its aftermath two years after the lockdowns. This book incentivizes readers to be open minded. https://a.co/d/8ZyRXCy #books #publishing #publisher #publishingcompany #publishinghouse #writing #authorship #becomeapublishedauthor #nonfiction #academicbooks #history #politicalscience #economics #politicalhistory #economichistory #socialhistory #legalhistory #internationalrelations #americanpolitics #politicalphilosophy #politicalopinion #commentary #economictheory #historyofeconomicthought #appliedeconomics

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Excerpt: Two fundamental problems remain unsolved. One involves making AI models that are trained on historic data respond understand whatever new situation they are put in and respond appropriately. In the words of Demis Hassabis, head of Google’s AI research division, AI needs to be able to “understand and respond to our complex and dynamic world, just as we do”. That is a tall order. The challenge isn’t just to avoid the “hallucinations”, or occasional glaring mistakes, that AI systems are prone to. It also means having a full understanding of context, in order to consistently deliver truly helpful results https://t.co/sStu9gU04V https://lnkd.in/dSqJxsQg

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