Podcast appearances and mentions of Hadley Wickham

Play Episode Listen Later May 27, 2022 47:07

Episode 30 of the Shiny Developer Series reveals just how the power of open source software can be used to provide meaningful improvement to our daily lives. In the first of a two-part series, chief data scientist Mike Thomas reveals the motivation behind his brilliant COVID-19 test locator Shiny application, empowering a local community in Connecticut to efficiently report and track availability of test kits in a huge time of need. After a tour of the application interface, Mike shares his favorite techniques to bring an efficient user experience and the backend integrations with APIs to bring production-grade features to life.Resources mentioned in the episodeCOVID-19 At-Home Test Spotter (App) - ketchbrookanalytics.shinyapps.io/covid_test_spotterCOVID-19 At-Home Test Spotter (Code) - github.com/ketchbrookanalytics/covid_test_spotterApp blog post - www.ketchbrookanalytics.com/post/ketchbrook-analytics-launches-website-to-help-connecticut-residents-find-covid-19-test-kitsOlivia Adams' interview with CNN - www.cnn.com/videos/health/2021/02/08/software-developer-builds-simple-massachusetts-covid-19-vaccine-website-olivia-adams-intv-newday-vpx.cnnR Packages by Hadley Wickham and Jenny Bryan - r-pkgs.org{googleWay} Shiny vignette - symbolixau.github.io/googleway/articles/googleway-vignette.html#shinyEpisode Timestamps00:00:00 Episode Introduction 00:01:31 Mike's introductiona and journey with R & Shiny 00:07:20 Data science consulting and Ketchbrook Analytics 00:11:40 Olivia Adams' inspiring story 00:17:40 Demo of Mike's COVID-19 At-Home Test Spotter App 00:31:55 App code introduction 00:32:10 googleway package integrating the Google Maps API 00:36:25 Pulling addresses from map searches 00:41:10 Using MongoDB for records collection 00:43:15 bslib to simulate the multi-page app experience 00:46:20 Episode wrapup shinydevseries::session_info()

covid-19 data cnn connecticut app pulling demo apis shiny ubuntu spotter mike thomas obs studio popos system76 hadley wickham google maps api

Episode 30: The Connecticut COVID-19 Test Spotter App (Part 1)

Play Episode Listen Later May 27, 2022 47:06

Episode 30 of the Shiny Developer Series reveals just how the power of open source software can be used to provide meaningful improvement to our daily lives. In the first of a two-part series, chief data scientist Mike Thomas reveals the motivation behind his brilliant COVID-19 test locator Shiny application, empowering a local community in Connecticut to efficiently report and track availability of test kits in a huge time of need. After a tour of the application interface, Mike shares his favorite techniques to bring an efficient user experience and the backend integrations with APIs to bring production-grade features to life. Resources mentioned in the episode COVID-19 At-Home Test Spotter (App) - ketchbrookanalytics.shinyapps.io/covidtestspotter (https://ketchbrookanalytics.shinyapps.io/covid_test_spotter/) COVID-19 At-Home Test Spotter (Code) - github.com/ketchbrookanalytics/covidtestspotter (https://github.com/ketchbrookanalytics/covid_test_spotter) App blog post - www.ketchbrookanalytics.com/post/ketchbrook-analytics-launches-website-to-help-connecticut-residents-find-covid-19-test-kits (https://www.ketchbrookanalytics.com/post/ketchbrook-analytics-launches-website-to-help-connecticut-residents-find-covid-19-test-kits) Olivia Adams' interview with CNN - www.cnn.com/videos/health/2021/02/08/software-developer-builds-simple-massachusetts-covid-19-vaccine-website-olivia-adams-intv-newday-vpx.cnn (https://www.cnn.com/videos/health/2021/02/08/software-developer-builds-simple-massachusetts-covid-19-vaccine-website-olivia-adams-intv-newday-vpx.cnn) R Packages by Hadley Wickham and Jenny Bryan - r-pkgs.org (https://r-pkgs.org/) {googleWay} Shiny vignette - symbolixau.github.io/googleway/articles/googleway-vignette.html#shiny (https://symbolixau.github.io/googleway/articles/googleway-vignette.html#shiny) Episode Timestamps 00:00:00 (https://youtube.com/watch?v=21MnLDuRbS8&t=0s) Episode Introduction 00:01:31 (https://youtube.com/watch?v=21MnLDuRbS8&t=91s) Mike's introductiona and journey with R & Shiny 00:07:20 (https://youtube.com/watch?v=21MnLDuRbS8&t=440s) Data science consulting and Ketchbrook Analytics 00:11:40 (https://youtube.com/watch?v=21MnLDuRbS8&t=700s) Olivia Adams' inspiring story 00:17:40 (https://youtube.com/watch?v=21MnLDuRbS8&t=1060s) Demo of Mike's COVID-19 At-Home Test Spotter App 00:31:55 (https://youtube.com/watch?v=21MnLDuRbS8&t=1915s) App code introduction 00:32:10 (https://youtube.com/watch?v=21MnLDuRbS8&t=1930s) googleway package integrating the Google Maps API 00:36:25 (https://youtube.com/watch?v=21MnLDuRbS8&t=2185s) Pulling addresses from map searches 00:41:10 (https://youtube.com/watch?v=21MnLDuRbS8&t=2470s) Using MongoDB for records collection 00:43:15 (https://youtube.com/watch?v=21MnLDuRbS8&t=2595s) bslib to simulate the multi-page app experience 00:46:20 (https://youtube.com/watch?v=21MnLDuRbS8&t=2780s) Episode wrapup shinydevseries::session_info()

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So you think you're not a “data person”: Ryan Estrellado on creating communities with data and data science

DatabasED

Play Episode Listen Later Apr 4, 2022 62:07

In this episode, we talk with educational consultant, data scientist, book author, podcast host, and former educational psychologist Ryan Estrellado about living in the Venn diagrams of overlapping professional communities and finding your (our) place in those intersections. We talk about practical strategies for working with educators who have a wide variety of interests in data and specifically tools for data analysis, and about breaking down common misconceptions about data analysis or data science as it can be used to answer real questions in local educational settings. Although we had planned to focus the episode on Ryan's new book, we accidentally ended up spending an entire hour discussing about unusual professional trajectories and how data and coding have influenced all of us (for the better). Some day we will do another episode where we actually talk about Ryan's book, but in the meantime, we recommend that you go ahead and check it out! The content is truly accessible to all educators, wherever you happen to be in terms of your data journey (whether that journey was embarked upon voluntarily or otherwise). Check out the link below to get a 20% off code for the book! Abbreviations: IDE: Integrated development environment https://www.codecademy.com/article/what-is-an-ide Show notes: Go to ryanestrellado.com to download a free chapter of The K–12 Educator's Data Guidebook and a 20% off code The K-12 Educator's Data Guidebook: Reimagining Practical Data Use in Schools Donuts in the Lounge: A Podcast for Educators Follow on Twitter & Instagram @ry_estrellado Data Science in Education using R (free open-source book with hands-on activities to learn R using real-world education contexts): https://datascienceineducation.com/ Strategic Data Project at Harvard: https://sdp.cepr.harvard.edu/home South County SELPA at the San Diego County Office of Education: https://www.sdcoe.net/special-populations/selpas/scselpa Chase Jarvis, Creative Calling https://www.creativelive.com/class/creative-calling-chase-jarvis/lessons/amplify-your-community About Practice podcast (Ryan Estrellado & Joshua Rosenberg): https://open.spotify.com/show/4TzYLKTen3ZiJxiiKdHAsa R Studio (free download): https://www.rstudio.com/products/rstudio/ Tidyverse: https://www.tidyverse.org/ Hadley Wickham's website and list of books: https://hadley.nz/ Julia Silge: https://juliasilge.com/about/ Kieran Healy: Data Visualization: A Practical Introduction https://socviz.co/ Rosh's favorite vending machine video: https://www.youtube.com/watch?v=mMW6JKNop1Y Music: Exploring The World by Vlad Gluschenko is licensed under a Creative Commons License. https://creativecommons.org/licenses/... https://soundcloud.com/vgl9 Support by RFM - NCM

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Tidy Data with Hadley Wickham

The Data Wranglers

Play Episode Listen Later Jan 13, 2022 39:47

What is the Tidyverse and why is it important? Hadley Wickham is a leading data scientist and advocate for improving data science with tidy data and data hygiene. He's the Chief Scientist at RStudio and an Adjunct Professor of Statistics at the University of Oakland, Stanford University and Rice University. Join the Tidyverse discussion with The Data Wranglers Joe Hellerstein and Jeffrey Heer. #TheDataWranglers

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Episode 19: Climbing the Ladder of Shiny Mastery with Hadley Wickham

Play Episode Listen Later Mar 10, 2021 44:58

A brand new season of the Shiny Developer Series kicks off with RStudio's chief scientist Hadley Wickham! Hadley joins Eric in episode 19 to discuss his exciting new book, Mastering Shiny. As author of many Tidyverse packages and long time contributor to the data science community, he has poured his wealth of experience into this book dedicated to helping you become a better Shiny developer. We talk about the origins of Shiny and advice for those just starting out. For those already familiar with Shiny we discuss debugging, how to level up your skills, and best practices for seeking help and contributing to the community.Resources mentioned in the episodeMastering Shiny (online)Advanced R: Second EditionR for Data ScienceEpisode Timestamps0:00 Episode introduction 2:05 Hadley's involvement with Shiny's early development 5:22 Writing Mastering Shiny 8:30 Shiny touches on data analysis alongside software engineering and programming 12:41 Best ways to get started with Shiny 14:53 Value of tidy evaluation with Shiny 19:41 Importance & challenge of reactivity 24:30 Getting help with Shiny 28:43 Becoming a better Shiny developer and collaborator 33:51 Shiny community engagement 38:12 Where to find Mastering Shiny 40:01 How to level-up your skills as a Shiny developer 41:53 Recap and closing remarks

mastery climbing ladder shiny rstudio hadley wickham tidyverse

Episode 19: Climbing the Ladder of Shiny Mastery with Hadley Wickham

Software Engineering Radio - The Podcast for Professional Software Developers

Play Episode Listen Later Mar 10, 2021 43:50

A brand new season of the Shiny Developer Series kicks off with RStudio's chief scientist Hadley Wickham! Hadley joins Eric in episode 19 to discuss his exciting new book, Mastering Shiny. As author of many Tidyverse packages and long time contributor to the data science community, he has poured his wealth of experience into this book dedicated to helping you become a better Shiny developer. We talk about the origins of Shiny and advice for those just starting out. For those already familiar with Shiny we discuss debugging, how to level up your skills, and best practices for seeking help and contributing to the community. Resources mentioned in the episode Mastering Shiny (online) (https://mastering-shiny.org) Advanced R: Second Edition (https://adv-r.hadley.nz) R for Data Science (https://r4ds.had.co.nz) Episode Timestamps 0:00 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=0m0s) Episode introduction 2:05 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=2m05s) Hadley's involvement with Shiny's early development 5:22 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=5m22s) Writing Mastering Shiny 8:30 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=8m30s) Shiny touches on data analysis alongside software engineering and programming 12:41 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=12m41s) Best ways to get started with Shiny 14:53 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=14m53s) Value of tidy evaluation with Shiny 19:41 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=19m41s) Importance & challenge of reactivity 24:30 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=24m30s) Getting help with Shiny 28:43 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=28m43s) Becoming a better Shiny developer and collaborator 33:51 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=33m51s) Shiny community engagement 38:12 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=38m12s) Where to find Mastering Shiny 40:01 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=40m01s) How to level-up your skills as a Shiny developer 41:53 (https://www.youtube.com/watch?v=PJsIO8C9xp0&t=41m53s) Recap and closing remarks

mastery climbing ladder data science shiny rstudio hadley wickham tidyverse

Episode 450: Hadley Wickham on R and Tidyverse

Play Episode Listen Later Mar 9, 2021 51:17

Hadley Wickham, chief scientist at RStudio and creator of the Tidyverse, discusses how R and its data science package the TidyVerse are used and created. Host Felienne speaks with Wickham about the design philosophy of the Tidyverse, and how it supports..

data science wickham usertesting rstudio hadley wickham tidyverse

Hadley Wickham talks about his journey in data science, tidy data concepts, and his many books.

IBM Analytics Insights Podcasts

Play Episode Listen Later Jul 15, 2020 40:51

Want to be featured as a guest on Making Data Simple? Reach out to us at [almartintalksdata@gmail.com] and tell us why you should be next. AbstractHosted by Al Martin, VP, Data and AI Expert Services and Learning at IBM, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.This week on Making Data Simple, we have Hadley Wickham is Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University. He builds tools that make data science easier and faster, including the famous tidy verse packages for the R programming language. He was named a Fellow by the American Statistical Association for "pivotal contributions to statistical practice through innovative and pioneering research in statistical graphics and computing". Show Notes2:39 – Hadley talks about his journey 5:22 – Hadley talks about his American Statistical Association for "pivotal contributions to statistical practice"8:00 – Tidy data concept9:02 - How Hadley became interested in big data and R10:12 – Python and R12:30 – What Hadley is doing now13:47 – Top 3 packages that help data scientists 17:47 – Hadley discusses his book 22:48 – Writing a book vs. code29:40 – What language is going to take over31:01 – What’s next for data31:54 – What’s cool for Hadley36:26 – Hadley’s Role modelHadley Wickham’s booksGgplot2R for Data ScienceAdvanced RR PackagesHadley Wickham’s BlogHadley Wickham’s LinkedInHadley Wickham’s Twitter RStudio BlogConnect with the TeamProducer Kate Brown -

university learning books writing data fellow ibm stanford university statistics concepts adjunct professor auckland python data science rice university chief scientist tidy r10 r12 rstudio american statistical association al martin hadley wickham making data simple reach abstracthosted

Hadley Wickham talks about his journey in data science, tidy data concepts, and his many books.

Making Data Simple

Play Episode Listen Later Jul 15, 2020 40:51

Want to be featured as a guest on Making Data Simple? Reach out to us at [almartintalksdata@gmail.com] and tell us why you should be next. AbstractHosted by Al Martin, VP, Data and AI Expert Services and Learning at IBM, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.This week on Making Data Simple, we have Hadley Wickham is Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University. He builds tools that make data science easier and faster, including the famous tidy verse packages for the R programming language. He was named a Fellow by the American Statistical Association for "pivotal contributions to statistical practice through innovative and pioneering research in statistical graphics and computing". Show Notes2:39 – Hadley talks about his journey 5:22 – Hadley talks about his American Statistical Association for "pivotal contributions to statistical practice"8:00 – Tidy data concept9:02 - How Hadley became interested in big data and R10:12 – Python and R12:30 – What Hadley is doing now13:47 – Top 3 packages that help data scientists 17:47 – Hadley discusses his book 22:48 – Writing a book vs. code29:40 – What language is going to take over31:01 – What’s next for data31:54 – What’s cool for Hadley36:26 – Hadley’s Role modelHadley Wickham’s booksGgplot2R for Data ScienceAdvanced RR PackagesHadley Wickham’s BlogHadley Wickham’s LinkedInHadley Wickham’s Twitter RStudio BlogConnect with the TeamProducer Kate Brown -

university learning books writing data fellow ibm stanford university statistics concepts adjunct professor auckland python data science rice university chief scientist tidy r10 r12 rstudio american statistical association al martin hadley wickham making data simple reach abstracthosted

Episode 10: The Importance of User Experience with John Coene

Play Episode Listen Later Jul 7, 2020 62:24

In this packed episode of the Shiny Developer Series, we are joined by the very talented John (JP) Coene to explore tools and skills that can ease your journey in creating production-grade Shiny applications! We examine the backstory of John's highly-regarded Coronavirus mobile-first Shiny application, how you can use his excellent {waiter} and {server} packages to improve user experience, and much more!Resources mentioned in the episodeCovid19 tracker - A popular Coronavirus tracker application built upon {shinyMobile}: johncoene.shinyapps.io/contest-coronavirusJohn's blog post about the application: blog.john-coene.com/posts/2020-02-08-ncov-2019/RStudio Community post: community.rstudio.com/t/coronavirus-2020-shiny-contest-submission/53061{waiter} - Loading screens for Shiny: shiny.john-coene.com/waiter/{sever} - Good-looking problems by customizing your Shiny disconnected screen and error messages: sever.john-coene.com/{echarts4r} - Interactive visualizations for R: echarts4r.john-coene.com/How to build htmlwidgets (e-Rum 2020 virtual workshop): htmlwidgets.john-coene.com/Blog post on building htmlwidgets for Shiny apps: blog.john-coene.com/posts/2018-01-01-widgetEngineering Production-Grade Shiny Apps by Colin Faye: engineering-shiny.org/Episode Timestamps0:00 - Intro2:37 - Covid19 tracker - A popular Coronavirus Tracker app, built on shiny mobile: https://johncoene.shinyapps.io/contest-coronavirus5:37 - Golem - Within the Covid19 Tracker discussion, on the usefulness of golem for building shiny apps with best practices. 8:57 - On working with shinyMobile https://rinterface.github.io/shinyMobile, Maintainer; David Granjon12:47 - Shiny with R in Production and at scale. 17:27 - Waiter package. A key, simple way to improve the experience of your shiny app is giving the user clear information about how quickly app-components will take to load. https://shiny.john-coene.com/waiter/32:57 - On the importance of ensuring your shiny apps looks and feels good to the end user34:10 sever. More pleasing and better handling of shiny app error & disconnection messages. https://sever.john-coene.com38:57 - Building htmlwidgets for shiny apps https://blog.john-coene.com/posts/2018-01-01-widget/44:14 echarts4r. A package for powerful visualizations with R. It is a good showcase for how htmlwidgets can interact with your shiny-app, sending messages between your user and your server to improve the types of interactions possible with your visualizations. https://echarts4r.john-coene.com57:27 Advice for Shiny developers that want to take that “next step” in building complex applications for production use. Hadley Wickham's Mastering-shiny.org (still under development)Divad nojnarg - outstanding shiny ui, https://divadnojnarg.github.io/outstanding-shiny-ui/intro.htmlColin Fay's “Engineering Production-Grade Shiny Apps” at https://engineering-shiny.org/1:00:46 Wrapup

covid-19 coronavirus advice building blog production mastering interactive rum shiny user experience wrap up loading waiters maintainer hadley wickham divad

Episode 10: The Importance of User Experience with John Coene

learning science data grammar graphics linear digressions hadley wickham

Play Episode Listen Later Jul 7, 2020 62:24

In this packed episode of the Shiny Developer Series, we are joined by the very talented John (JP) Coene to explore tools and skills that can ease your journey in creating production-grade Shiny applications! We examine the backstory of John's highly-regarded Coronavirus mobile-first Shiny application, how you can use his excellent {waiter} and {server} packages to improve user experience, and much more! Resources mentioned in the episode Covid19 tracker - A popular Coronavirus tracker application built upon {shinyMobile} (https://rinterface.github.io/shinyMobile/): johncoene.shinyapps.io/contest-coronavirus (https://johncoene.shinyapps.io/contest-coronavirus) John's blog post about the application: blog.john-coene.com/posts/2020-02-08-ncov-2019/ (https://blog.john-coene.com/posts/2020-02-08-ncov-2019/) RStudio Community post: community.rstudio.com/t/coronavirus-2020-shiny-contest-submission/53061 (https://community.rstudio.com/t/coronavirus-2020-shiny-contest-submission/53061) {waiter} - Loading screens for Shiny: shiny.john-coene.com/waiter/ (https://shiny.john-coene.com/waiter) {sever} - Good-looking problems by customizing your Shiny disconnected screen and error messages: sever.john-coene.com/ (https://sever.john-coene.com/) {echarts4r} - Interactive visualizations for R: echarts4r.john-coene.com/ (https://echarts4r.john-coene.com/) How to build htmlwidgets (e-Rum 2020 virtual workshop): htmlwidgets.john-coene.com/ (https://htmlwidgets.john-coene.com/) Blog post on building htmlwidgets for Shiny apps: blog.john-coene.com/posts/2018-01-01-widget (https://blog.john-coene.com/posts/2018-01-01-widget/) Engineering Production-Grade Shiny Apps by Colin Faye: engineering-shiny.org/ (https://engineering-shiny.org/) Episode Timestamps 0:00 - Intro 2:37 (https://www.youtube.com/watch?v=BYtoQQHmmOM&t=2m37s) - Covid19 tracker - A popular Coronavirus Tracker app, built on shiny mobile: https://johncoene.shinyapps.io/contest-coronavirus 5:37 (https://www.youtube.com/watch?v=BYtoQQHmmOM&t=5m37s) - Golem - Within the Covid19 Tracker discussion, on the usefulness of golem for building shiny apps with best practices. 8:57 (https://www.youtube.com/watch?v=BYtoQQHmmOM&t=8m57s) - On working with shinyMobile https://rinterface.github.io/shinyMobile, Maintainer; David Granjon 12:47 (https://www.youtube.com/watch?v=BYtoQQHmmOM&t=12m47s) - Shiny with R in Production and at scale. 17:27 (https://www.youtube.com/watch?v=BYtoQQHmmOM&t=17m27s) - Waiter package. A key, simple way to improve the experience of your shiny app is giving the user clear information about how quickly app-components will take to load. https://shiny.john-coene.com/waiter/ 32:57 (https://www.youtube.com/watch?v=BYtoQQHmmOM&t=32m57s) - On the importance of ensuring your shiny apps looks and feels good to the end user 34:10 (https://www.youtube.com/watch?v=BYtoQQHmmOM&t=34m10s) sever. More pleasing and better handling of shiny app error & disconnection messages. https://sever.john-coene.com 38:57 (https://www.youtube.com/watch?v=BYtoQQHmmOM&t=38m57s) - Building htmlwidgets for shiny apps https://blog.john-coene.com/posts/2018-01-01-widget/ 44:14 (https://www.youtube.com/watch?v=BYtoQQHmmOM&t=44m14s) echarts4r. A package for powerful visualizations with R. It is a good showcase for how htmlwidgets can interact with your shiny-app, sending messages between your user and your server to improve the types of interactions possible with your visualizations. https://echarts4r.john-coene.com 57:27 (https://www.youtube.com/watch?v=BYtoQQHmmOM&t=57m27s) Advice for Shiny developers that want to take that “next step” in building complex applications for production use. - Hadley Wickham’s Mastering-shiny.org (still under development) - Divad nojnarg - outstanding shiny ui, https://divadnojnarg.github.io/outstanding-shiny-ui/intro.html - Colin Fay’s “Engineering Production-Grade Shiny Apps” at https://engineering-shiny.org/ 1:00:46 Wrapup

covid-19 coronavirus advice building blog production mastering interactive rum shiny user experience wrap up loading waiters maintainer hadley wickham divad

The Grammar Of Graphics

Linear Digressions

Play Episode Listen Later May 3, 2020 35:38

You may not realize it consciously, but beautiful visualizations have rules. The rules are often implict and manifest themselves as expectations about how the data is summarized, presented, and annotated so you can quickly extract the information in the underlying data using just visual cues. It’s a bit abstract but very profound, and these principles underlie the ggplot2 package in R that makes famously beautiful plots with minimal code. This episode covers a paper by Hadley Wickham (author of ggplot2, among other R packages) that unpacks the layered approach to graphics taken in ggplot2, and makes clear the assumptions and structure of many familiar data visualizations.

Chris Hyde: Making a Difference with Data Analytics

Great Tech Pros with Wylie Blanchard

Play Episode Listen Later Apr 19, 2020 35:23

Chris Hyde, Data Analytics professional, met with us to share his experience with Data Analytics, Business Intelligence and Data Science. In this discussion Chris shares: Data can make a difference in people's lives. Increased use of statistics in data science. Programming languages used to query and manipulate data. The importance of a network and building relationships. WATCH Subscribe to Great Tech Pros video on YouTube, Facebook and Apple Podcasts. LISTEN Subscribe to Great Tech Pros podcast on Apple Podcasts, Spotify and Google Podcasts. Questions that Chris answers: [01:33] What does a business intelligence, data analytics and database administration professional do? [03:40] What is the difference between business intelligence and data science? [06:22] How did you start your data career? [08:09] What interesting thing have you learned from data? [10:45] How can people move into a field related to business intelligence or data science? [12:23] What are the top programming/querying languages that you are using? [15:11] How can a full-time employee make the transition to working as an independent consultant? [20:17] What tools/resources have you found helpful in your career? [21:43] What events/conferences do you use to build your network? [23:38] What courses and books do you recommend for new data professionals? [25:09] Where can people find Chris Hyde? [26:14] What kind of projects do you work on? (AUDIENCE MEMBER QUESTION) [29:25] How do you give a customer something that they can manage on their own? (AUDIENCE MEMBER QUESTION) [30:36] Additional recommendations for groups and events to begin building your network in data analytics. (AUDIENCE MEMBER QUESTION) [32:29] How difficult is your job? Useful Links: The best way to connect with Chris Hyde is on Twitter. He can also be found on Linkedin. Chris Recommends the following books: Learning R by Richard Cotton. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund. Think Stats: Exploratory Data Analysis by Allen Downey. Special thanks to the students, alumni and administration from the University of Illinois at Urbana-Champaign and the College of DuPage for their help in making this episode a success.

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Chris Hyde: Making a Difference with Data Analytics

Great Tech Pros with Wylie Blanchard

Play Episode Listen Later Apr 19, 2020 35:23

Chris Hyde, Data Analytics professional, met with us to share his experience with Data Analytics, Business Intelligence and Data Science. In this discussion Chris shares: Data can make a difference in people's lives. Increased use of statistics in data science. Programming languages used to query and manipulate data. The importance of a network and building relationships. WATCH Subscribe to Great Tech Pros video on YouTube, Facebook and Apple Podcasts. LISTEN Subscribe to Great Tech Pros podcast on Apple Podcasts, Spotify and Google Podcasts. Questions that Chris answers: [01:33] What does a business intelligence, data analytics and database administration professional do? [03:40] What is the difference between business intelligence and data science? [06:22] How did you start your data career? [08:09] What interesting thing have you learned from data? [10:45] How can people move into a field related to business intelligence or data science? [12:23] What are the top programming/querying languages that you are using? [15:11] How can a full-time employee make the transition to working as an independent consultant? [20:17] What tools/resources have you found helpful in your career? [21:43] What events/conferences do you use to build your network? [23:38] What courses and books do you recommend for new data professionals? [25:09] Where can people find Chris Hyde? [26:14] What kind of projects do you work on? (AUDIENCE MEMBER QUESTION) [29:25] How do you give a customer something that they can manage on their own? (AUDIENCE MEMBER QUESTION) [30:36] Additional recommendations for groups and events to begin building your network in data analytics. (AUDIENCE MEMBER QUESTION) [32:29] How difficult is your job? Useful Links: The best way to connect with Chris Hyde is on Twitter. He can also be found on Linkedin. Chris Recommends the following books: Learning R by Richard Cotton. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund. Think Stats: Exploratory Data Analysis by Allen Downey. Special thanks to the students, alumni and administration from the University of Illinois at Urbana-Champaign and the College of DuPage for their help in making this episode a success.

university spotify college data illinois transform google podcasts programming increased hyde data science making a difference visualize data analytics business intelligence urbana champaign tidy dupage hadley wickham

Episode 9: Shiny Dev Center & Education with Mine Cetinkaya-Rundel

Play Episode Listen Later Mar 10, 2020 39:46

The Shiny Developer Series is back! In our first session of 2020, we are joined by professional educator and data scientist Mine Cetinkaya-Rundel to get the inside story of the newly updated Shiny Dev Center (including a major revision of the Shiny Gallery) and the insights RStudio gained from the Shiny Contest. Also Mine shares her advice on developing educational material for Shiny users and where she sees future opportunities in this evolving space.ResourcesShiny Gallery: shiny.rstudio.com/galleryShiny Tutorials: shiny.rstudio.com/tutorialReprex guide for Shiny questions on RStudio Community: community.rstudio.com/100012020 Shiny Contest information: blog.rstudio.com/2020/02/12/shiny-contest-2020-is-hereMastering Shiny by Hadley Wickham: mastering-shiny.orgEpisode Timestamps00:56 Mine Introduction. 03:01 Shiny Gallery - walkthrough and recent revamp. 04:15 Shiny Demos - live examples and deep-dive into shiny's features. 05:20 Shiny User Showcase - A large set of example shiny apps, by the Shiny community. Including code on github, and interactive code on rstudio.cloud. 06:10 Shiny Contest 2019 lead to the revamp of the showcase. 09:00 Example running an app on the Shiny Showcase interactively with rstudio.cloud. 12:40 Shiny Gallery walkthrough - Mine's learnings from reviewing the Shiny apps on the gallery. There's a lot to learn from, but Mine highlighted just a few: 13:40 1. BYOD Apps - Bring your own data shiny app best practices. 14:42 2. App Walkthroughs and jintrojs package. iSEE Shiny App. 17:15 3. Apps that don't look like Shiny apps. 17:30 Example 1. 69 Love Songs by the Magnetic Fields. 18:25 Example 2. Uber explorer app - another example that doesn't look like a standard shiny app. 19:04 Example 3. CRAN Explorer. 19:55 Example 4. Hex memory game. An example of a game Shiny app. 22:15 Teaching and Educational Materials for Shiny - Shiny development has its own set of challenges and Mine spends a lot of time thinking about teaching Shiny. 23:45 Learn Shiny tutorial - a nice short intro to Shiny. Includes written articles, videos, and code examples. shiny.rstudio.com/tutorial. 24:30 Asking good questions about the issues you're having with your Shiny app. What are good workflows for seeking help? Barret's Shiny debugging and reprex guide http://community.rstudio.com/t/10001 26:17 Materials for intermediate Shiny users. Articles on shiny.rstudio.com, workshop and conference videos, and a call to the Shiny Community. 28:00 Also for advanced Shiny developers, Hadley's Mastering Shiny book. Coming late 2020 mastering-shiny.org. 30:10 Shiny Contest 2020 - learnings from last year's contest, and advice to folks submitting to the next contest. https://blog.rstudio.com/2020/02/12/shiny-contest-2020-is-here/ 39:12 Shiny Dev Series Outtro

education teaching uber mine apps includes materials love songs shiny hex magnetic fields barret rstudio educational materials hadley wickham

Episode 9: Shiny Dev Center & Education with Mine Cetinkaya-Rundel

future career integration python user conference hadley wickham

Play Episode Listen Later Mar 10, 2020 39:46

The Shiny Developer Series is back! In our first session of 2020, we are joined by professional educator and data scientist Mine Cetinkaya-Rundel to get the inside story of the newly updated Shiny Dev Center (https://shiny.rstudio.com) (including a major revision of the Shiny Gallery) and the insights RStudio gained from the Shiny Contest. Also Mine shares her advice on developing educational material for Shiny users and where she sees future opportunities in this evolving space. Resources Shiny Gallery: shiny.rstudio.com/gallery (https://shiny.rstudio.com/gallery) Shiny Tutorials: shiny.rstudio.com/tutorial (https://shiny.rstudio.com/tutorial) Reprex guide for Shiny questions on RStudio Community: community.rstudio.com/10001 (https://community.rstudio.com/10001) 2020 Shiny Contest information: blog.rstudio.com/2020/02/12/shiny-contest-2020-is-here (https://blog.rstudio.com/2020/02/12/shiny-contest-2020-is-here/) Mastering Shiny by Hadley Wickham: mastering-shiny.org (https://mastering-shiny.org/) Episode Timestamps 00:56 Mine Introduction. 03:01 Shiny Gallery - walkthrough and recent revamp. 04:15 Shiny Demos - live examples and deep-dive into shiny's features. 05:20 Shiny User Showcase - A large set of example shiny apps, by the Shiny community. Including code on github, and interactive code on rstudio.cloud. 06:10 Shiny Contest 2019 lead to the revamp of the showcase. 09:00 Example running an app on the Shiny Showcase interactively with rstudio.cloud. 12:40 Shiny Gallery walkthrough - Mine's learnings from reviewing the Shiny apps on the gallery. There's a lot to learn from, but Mine highlighted just a few: 13:40 1. BYOD Apps - Bring your own data shiny app best practices. 14:42 2. App Walkthroughs and jintrojs package. iSEE Shiny App. 17:15 3. Apps that don't look like Shiny apps. 17:30 Example 1. 69 Love Songs by the Magnetic Fields. 18:25 Example 2. Uber explorer app - another example that doesn't look like a standard shiny app. 19:04 Example 3. CRAN Explorer. 19:55 Example 4. Hex memory game. An example of a game Shiny app. 22:15 Teaching and Educational Materials for Shiny - Shiny development has its own set of challenges and Mine spends a lot of time thinking about teaching Shiny. 23:45 Learn Shiny tutorial - a nice short intro to Shiny. Includes written articles, videos, and code examples. shiny.rstudio.com/tutorial. 24:30 Asking good questions about the issues you're having with your Shiny app. What are good workflows for seeking help? Barret's Shiny debugging and reprex guide http://community.rstudio.com/t/10001 26:17 Materials for intermediate Shiny users. Articles on shiny.rstudio.com, workshop and conference videos, and a call to the Shiny Community. 28:00 Also for advanced Shiny developers, Hadley's Mastering Shiny book. Coming late 2020 mastering-shiny.org. 30:10 Shiny Contest 2020 - learnings from last year's contest, and advice to folks submitting to the next contest. https://blog.rstudio.com/2020/02/12/shiny-contest-2020-is-here/ 39:12 Shiny Dev Series Outtro

education teaching uber mine apps includes materials love songs shiny hex magnetic fields rstudio educational materials hadley wickham

SDS 337: Hadley Wickham Talks Integration and Future of R and Python

SuperDataScience

Play Episode Listen Later Feb 5, 2020 74:35

Hadley Wickham, a huge presence in data science, sits down to talk about R, Python, and the future of potential integrations, as well as some Q&A with our listeners through LinkedIn about programming languages and how to make data science accessible for all. In this episode you will learn: • Hadley’s R packages [8:26] • Better integrations between R and Python [20:11] • LinkedIn Q&A [33:34] • useR Conference vs. RStudio Conference [50:46] • LinkedIn Q&A: Career-related questions [1:01:06] • LinkedIn Q&A: Future-related questions [1:08:01] Additional materials: www.superdatascience.com/337

A Chat with Hadley Wickham

The Random Sample

Play Episode Listen Later Dec 3, 2019 32:00

The world of data science has seen a massive explosion of interest in the last decade. It's more important than ever before to be able to work with data, visualise it and find the meaning in it. One man who is helping hundreds of thousands of people do just that – is statistician Hadley Wickham. Hadley is now the Chief Scientist at RStudio.In this episode, Hadley talks about his groundbreaking work, and shares his journey on how he got to where he is now.The Random Sample is a podcast by the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). In this show, we share stories about mathematics, statistics and the people involved. To learn more about ACEMS, visit https://acems.org.au.See omnystudio.com/listener for privacy information.

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Tacos de Codorniz

Quail data por tacos de datos

Play Episode Listen Later Oct 8, 2019 27:52

Esta semana tenemos otro anuncio: Quail data. Un podcast más efímero, rústico, y rápido estilo más como boletín de noticias en el que estaremos compartiendo bibliotecas, paquetes, cursos, eventos, tutoriales, etc. Quail data va a ser públicado una o dos veces por semana y tocaremos estos recursos de manera más superficial. tacosdedatos, el podcast obviamente seguirá publicandose. En este podcast tocaremos los temas más a fondo y tendremos conversaciones más substantivas. Sin más, aquí esta Quail data. En el primer episodio de Quail data aprendemos de 7 recursos: fishualize textstat mockaroo drawdata.xyz Intro a R y RStudio de Rafa Gouveia Intro to Python de Simplificando Datos R para Ciencia de Datos de Hadley Wickham en español Bonus: Bar Chart Races por Rafael Gouveia en tacosdedatos.com Manda tus sugerencias a sugerencias@tacosdedatos.com por twitter a @tacosdedatos.com o dejando un comentario en tacosdedatos.fm Recursos de las respuestas de tacosdedatos, el podcast: Curso de Python para Ciencia de Datos (en íngles) en Edx.org Datos de EEUU en ipums.org y en international.ipums.org Curso de programación básica de Platzi ¡Apoya este podcast! --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app · Anchor: The easiest way to make a podcast. https://anchor.fm/app --- Send in a voice message: https://anchor.fm/tacosdedatos/message Support this podcast: https://anchor.fm/tacosdedatos/support

sin tacos python curso datos ciencia eeuu quail edx rstudio hadley wickham

Quail data 0001 - fishualize, textstat, mockaroo y más

Quail data

Play Episode Listen Later Oct 5, 2019 11:02

En el primer episodio de Quail data aprendemos de 7 recursos: fishualize textstat mockaroo drawdata.xyz Intro a R y RStudio de Rafa Gouveia Intro to Python de Simplificando Datos R para Ciencia de Datos de Hadley Wickham en español Bonus: Bar Chart Races por Rafael Gouveia en tacosdedatos.com Manda tus sugerencias a sugerencias@tacosdedatos.com por twitter a @tacosdedatos.com o dejando un comentario en tacosdedatos.fm ¡Apoya este podcast! --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app --- Send in a voice message: https://anchor.fm/quaildata/message Support this podcast: https://anchor.fm/quaildata/support

data python datos ciencia apoya quail rstudio hadley wickham

Episode 8 - Mucking around and making things work Ft. Tom Liptrot

PyDataMCR

Play Episode Listen Later Sep 24, 2019 47:08

Welcome to PyDataMCR episode 8 , today we are talking to Tom Liptrot who is a Consultant Data Scientist at Ortom, a Manchester based Data Science Consultancy. We talk about how flexibility at work can lead to great data products, some various meetups Tom will be attending and even some stand up comedy. Show Notes Sponsors Arctic Shores - arcticshores.com/ Cathcart Associates - cathcartassociates.com/ Meetups Pydatamcr - https://www.meetup.com/PyData-Manchester/ Macnml - https://www.mancml.io/ Chester Data Insights - https://www.meetup.com/Chester-Data-Insights/ AiFrenzy - labs.uk.barclays/ai Crap talks - https://www.meetup.com/CRAP-Talks-CRO-Analytics-Product-Manchester/ Bright festival - http://www.brightclub.org/ Tom Ortom Data Science Consultancy - https://ortom.co.uk/ DSF blog post - https://ortom.co.uk/2019/03/22/data-fest-2019.html Packages Tidyverse - https://www.tidyverse.org/ Pandas - https://pandas.pydata.org/ dplyr - https://dplyr.tidyverse.org/ brms - https://cran.r-project.org/web/packages/brms/index.html pymc3 - https://docs.pymc.io/ XGBoost - https://cran.r-project.org/web/packages/xgboost/index.html glmnet - https://cran.r-project.org/web/packages/glmnet/index.html keras - https://keras.io/ Book recommendation Andrew Gelman - https://www.amazon.com/Red-State-Blue-Rich-Poor/dp/0691143935 Who you admire Demis Hassabis - twitter.com/demishassabis?lang=en Jeff Dean - en.m.wikipedia.org/wiki/Jeff_Dean_(computer_scientist) Hadley Wickham - http://hadley.nz/ Hannah Fry - http://www.hannahfry.co.uk/ Andy Clark - https://twitter.com/fluffycyborg?lang=en Karl J. Friston - en.m.wikipedia.org/wiki/Karl_J._Friston Martin Eastwood - http://www.pena.lt/y/blog.html Peak.ai - https://peak.ai/ Kayle Haynes - https://twitter.com/KayleaHaynes Chris boddington - https://www.linkedin.com/in/christopher-boddington-449555112/

manchester pandas making things dsf andy clark hannah fry mucking jeff dean friston xgboost hadley wickham karl j friston

Episode 9: Diving Into Data Engineering with Mark Sellors

DataCast

Play Episode Listen Later Feb 1, 2019 67:46

Show Notes: (2:05) Mark revisited his brief stint during college back in the 90s. (3:38) Mark’s first job as a Business Consultant at Dixon Stores Group was a humbling experience. (5:41) Mark stressed the importance of customer empathy that lasts with him throughout his career. (6:45) Mark talked about his next long-term job as a Configuration & Support Engineer at Orange PCS, where he built solid technical skills in IT and data work. (12:51) Mark discussed his next gig working as a Principal Specialist at T-Systems and then doing freelance work in IT and data analytics. (16:23) Mark reflected on the sabbatical years he took a break from working. (19:10) Mark shared an overview about his current employer, Mango Solutions. (20:34) Mark discussed his first big project working at Mango as a senior IT consultant. (25:57) Mark then transitioned into a technical architect role, where he became the bridge between the data science and the IT worlds. (28:33) In reference to his talk “An operating model for R”, Mark stressed the importance of policy, procedure, people and policing to build an effective operating model that connects data scientists and IT specialists. (34:32) Mark gave a client use case that his Data Engineering team has been involved with at Mango. (36:48) Mark talked about the cultural challenges of deploying code into production within an organization. (39:35) Mark talked about his key accomplishments in his leadership role as Head of Data Engineering. (43:27) In reference to his talk “R is production safe”, Mark discussed the existing challenges in the R-community to write production code. (50:39) In reference to his book “Field Guide to the R Ecosystem”, Mark shared the key developments in the R ecosystem in 2019 that he’s most excited about. (53:27) Mark gave thoughts on the rise of cloud-based processing technologies for data engineering’s best practices. (56:42) Mark mentioned the Google Cloud Certified Data Engineer Exam that people can take to learn about data engineering. (59:35) Mark emphasized the importance of communication skills to become an organizational leader. (01:01:13) Mark shared his view on the data science ecosystem in the UK. (01:02:18) Closing segments. His Contact Info: Website Twitter GitHub LinkedIn His Recommended Resources: rayshader R package TensorFlow for R sparklyr R interface for Apache Spark Amazon S3 for Data Storage Google Cloud Platform Podcast Hudl RStudio R for Data Science by Hadley Wickham and Garrett Grolemund

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Episode 9: Diving Into Data Engineering with Mark Sellors

Datacast

Play Episode Listen Later Feb 1, 2019 67:46

Show Notes: (2:05) Mark revisited his brief stint during college back in the 90s. (3:38) Mark’s first job as a Business Consultant at Dixon Stores Group was a humbling experience. (5:41) Mark stressed the importance of customer empathy that lasts with him throughout his career. (6:45) Mark talked about his next long-term job as a Configuration & Support Engineer at Orange PCS, where he built solid technical skills in IT and data work. (12:51) Mark discussed his next gig working as a Principal Specialist at T-Systems and then doing freelance work in IT and data analytics. (16:23) Mark reflected on the sabbatical years he took a break from working. (19:10) Mark shared an overview about his current employer, Mango Solutions. (20:34) Mark discussed his first big project working at Mango as a senior IT consultant. (25:57) Mark then transitioned into a technical architect role, where he became the bridge between the data science and the IT worlds. (28:33) In reference to his talk “An operating model for R”, Mark stressed the importance of policy, procedure, people and policing to build an effective operating model that connects data scientists and IT specialists. (34:32) Mark gave a client use case that his Data Engineering team has been involved with at Mango. (36:48) Mark talked about the cultural challenges of deploying code into production within an organization. (39:35) Mark talked about his key accomplishments in his leadership role as Head of Data Engineering. (43:27) In reference to his talk “R is production safe”, Mark discussed the existing challenges in the R-community to write production code. (50:39) In reference to his book “Field Guide to the R Ecosystem”, Mark shared the key developments in the R ecosystem in 2019 that he’s most excited about. (53:27) Mark gave thoughts on the rise of cloud-based processing technologies for data engineering’s best practices. (56:42) Mark mentioned the Google Cloud Certified Data Engineer Exam that people can take to learn about data engineering. (59:35) Mark emphasized the importance of communication skills to become an organizational leader. (01:01:13) Mark shared his view on the data science ecosystem in the UK. (01:02:18) Closing segments. His Contact Info: Website Twitter GitHub LinkedIn His Recommended Resources: rayshader R package TensorFlow for R sparklyr R interface for Apache Spark Amazon S3 for Data Storage Google Cloud Platform Podcast Hudl RStudio R for Data Science by Hadley Wickham and Garrett Grolemund

head united kingdom consulting diving data science mango devops business consultants cloud computing field guides configuration data engineering t systems support engineer hadley wickham

Episode 1: Molecular Biologist turned Data Scientist with Jonathan Leslie

Datacast

Play Episode Listen Later Aug 20, 2018 53:31

Show Notes: (2:10) Jon reflects on his academic career and explained why he pursued an advanced degree in Biology. He also went over his post-doc fellowship at Cancer Research UK. (4:45) Jon discusses the zebra-fish project he worked on during his post-doc research at King’s College London. (8:13) Jon talks about the developmental biology work he did at the embryology lab at University College London. (10:00) Jon explains why biological data are noisy. (10:58) Jon explains his motivation behind his transition from academia to industry. (15:04) Jon’s advice for Ph.D. students and post-doc researchers who want to make a similar transition. (16:20) Jon talks about his freelance data science project to build a recommendation systems aiming at women struggling with Type 2 diabetes. (21:32) Jon talks about his experience running his data science consultancy business. (25:32) Jon’s advice to improve data storytelling skill. (26:59) Jon presents a high-level overview of his company Pivigo. (31:11) Jon talks about his role as Pivigo’s Head of Data Science. (34:50) Jon goes over the industries that are Pivigo’s clients. (36:30) Jon goes over the skills that data science talents need to develop. (37:53) Jon’s advice to transition from individual contributor to manager. (41:51) Jon mentions the talks he gave recently at O’Reilly Strata Data Conference in London. (46:48) Closing segment. His contact info: Twitter LinkedIn GitHub His recommended resources: Datacamp David Robinson's "Understanding empirical Bayes estimation" "R for Data Science" by Hadley Wickham and Garrett Grolemund #rstats

head biology freelance data science university college london college london biologists data scientists cancer research uk bayes molecular biologist hadley wickham

Episode 1: Molecular Biologist turned Data Scientist with Jonathan Leslie

DataCast

Play Episode Listen Later Aug 20, 2018 53:31

Show Notes: (2:10) Jon reflects on his academic career and explained why he pursued an advanced degree in Biology. He also went over his post-doc fellowship at Cancer Research UK. (4:45) Jon discusses the zebra-fish project he worked on during his post-doc research at King’s College London. (8:13) Jon talks about the developmental biology work he did at the embryology lab at University College London. (10:00) Jon explains why biological data are noisy. (10:58) Jon explains his motivation behind his transition from academia to industry. (15:04) Jon’s advice for Ph.D. students and post-doc researchers who want to make a similar transition. (16:20) Jon talks about his freelance data science project to build a recommendation systems aiming at women struggling with Type 2 diabetes. (21:32) Jon talks about his experience running his data science consultancy business. (25:32) Jon’s advice to improve data storytelling skill. (26:59) Jon presents a high-level overview of his company Pivigo. (31:11) Jon talks about his role as Pivigo’s Head of Data Science. (34:50) Jon goes over the industries that are Pivigo’s clients. (36:30) Jon goes over the skills that data science talents need to develop. (37:53) Jon’s advice to transition from individual contributor to manager. (41:51) Jon mentions the talks he gave recently at O’Reilly Strata Data Conference in London. (46:48) Closing segment. His contact info: Twitter LinkedIn GitHub His recommended resources: Datacamp David Robinson's "Understanding empirical Bayes estimation" "R for Data Science" by Hadley Wickham and Garrett Grolemund #rstats

head biology freelance data science university college london college london biologists data scientists cancer research uk bayes molecular biologist hadley wickham

R R R R R ... Not Just For Pirates Anymore | Stats + Stories Episode 62

Stats + Stories

Play Episode Listen Later Aug 2, 2018 26:53

Hadley Wickham (@hadleywickham) is Chief Scientist at RStudio, a member of the R Foundation, and Adjunct Professor at Stanford University and the University of Auckland. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. His work includes packages for data science (the tidyverse: including ggplot2, dplyr, tidyr, purrr, and readr) and principled software development (roxygen2, testthat, devtools). He is also a writer, educator, and speaker promoting the use of R for data science ( http://hadley.nz ).>.

university stories pirates stanford university stats adjunct professor auckland chief scientist rstudio hadley wickham

11. パッケージ開発とオープンソースのメンテナンス

R Radio for the Rest of us.

Play Episode Listen Later Mar 21, 2018 28:08

前回に引き続き、y__mattuとuriboがggplot2パイプ演算子問題やfelpパッケージをはじめとした最近の話題について喋ります関連リンク ggplot2 パイプ演算子実装問題 Thanks to a question on https://t.co/fkYCCcgnXm from @dataandme, I did a little write up on why ggplot2 should use the pipe, but unfortunately never will: https://t.co/BD8xpg3f6j #rstats— Hadley Wickham (@hadleywickham) January 29, 2018 ggplot2は+が意味的に正しい！ %>%なんていらない！、というのは俺もそんな風に感じる一人だけど、Hadleyのこのことばがすべてだと思う。 https://t.co/Ny3yhuAwNo— Hiroaki Yutani (@yutannihilation) February 25, 2018 Introducing the ‘ggplot2pipes’ package ggplot2pipes パッケージ開発運用・メンテナンスコスト Pandocに依存しているrmarkdownパッケージの.travis R packages: Checking after every commit with Travis Continuous integration for your private R projects with CircleCI r-docker-tutorial @nozmaさんの翻訳データ分析環境の構築にDockerを利用しよう #017 Dependencies Glue vs stringr::str_interp felp felp: An R package to display source and help of a function simultaneously 関数の中身とヘルプを同時にすぐ見たい(felp me!) (R) felpパッケージとfelp関数の紹介（と、主に裏側の話） Fukuoka.R fukuoka.R #10 Fukuoka.R #10 R introduction R で友達を助けた話 @y__mattu リポジトリ rtweet&rmarkdownで収集フォトギャラリーを作る @kazutan y__mattu バンド出演情報 Club ROOTS! Rラジオパッケージ r3gr

checking continuous glue docker fukuoka dependencies circleci 5etfw hadley wickham pandoc

PYD40 – Hadley Wickham – Chief Scientist

Podcast Your Data

Play Episode Listen Later Aug 29, 2017 31:06

On this week’s episode, InterWorks Analytics Consultant and Alteryx Ace, Michael Treadwell sits down with Hadley Wickham. As an author, contributor and Chief Scientist at R Studio, Hadley is on the forefront of building better tools for data science. Even if you don’t have a background in coding, he’s all about getting people up to speed quickly and efficiently. An interest in data is not necessary to enjoy this episode. Sit back, relax and push play on Podcast Your Data! Subscribe to Podcast Your Data through iTunes, Stitcher, Pocket Casts or your favorite podcasting app.

stitcher pocket casts chief scientist rstudio hadley wickham

Episode #82: Rune Madsen

rune madsen hadley wickham

Play Episode Listen Later Apr 18, 2017 25:45

The “open” movement consists of different product types–we all know about open data and open source code, for example. But more and more, we're seeing open book writing. Last year, Hadley Wickham wrote his book on R in an open platform.... The post Episode #82: Rune Madsen appeared first on PolicyViz.

Episode #82: Rune Madsen

rune madsen hadley wickham

Play Episode Listen Later Apr 18, 2017 25:46

The “open” movement consists of different product types–we all know about open data and open source code, for example. But more and more, we're seeing open book writing. Last year, Hadley Wickham wrote his book on R in an open platform.... The post Episode #82: Rune Madsen appeared first on PolicyViz.

50. Libros técnicos de trading I

Tecnología y trading

Play Episode Listen Later Feb 3, 2017 18:08

¡Muy buenos días a todos! Hoy vengo a presentaros, como el pasado viernes, libros técnicos de trading. En este caso, más tecnológico y es que creo que para los que quieran combinar la parte más tecnológica con la parte del trading. Voy a subdividirlo en dos vertientes muy diferenciadas y de antemano os aviso que todos los libros que voy a exponer son los que me he leído o los que tengo en cola para leer. Lo que habrá alguno que os recomiende por el hecho que lo he encontrado interesante y esté esperando a leerlo cuando acabe el resto. Otra cosa que cabe decir es que la mayoría de libros (o toda creo de hecho) está en inglés. En castellano no hay nada al respecto que valga la pena o que al menos me haya topado. Antes de empezar por eso quiero decir dos cosas. Esta es la primera parte del podcast de libros técnicos de trading porque da para mucho, sin duda. La segunda cosas es especificar que todos los libros que voy a decir hoy van enfocados sobretodo a gente que sepa programación o que quiera aprender a base de hostias. Siento decirlo así de brusco, pero estos últimos pueden ser gente que quiera aprender a usar sus habilidades de trading para automatizar procesos. Bien, pues estas personas quiero que tengan en cuenta que les costará al principio porque no solo tienen que chocar con la vertiente de entender como hacer las cosas sino amueblar la cabeza a nivel de programación y entender que todo es diferente y lógico. Hay gente que conozco que ha podido y puede de hecho, programar muy bien viniendo de una vertiente de letras. Y es que al final como en todo señores, se han de poner horas de trabajo y estudio. Pues venga, empezamos con la parte generalista. Qué quiere decir. Pues muy fácil: lo que se ha de hacer antes de empezar con la parte técnica, es estudiar programación aplicada. Es decir, los lenguajes de programación que nos van a servir para poder hacer estos códigos. Y es que lo más importante es saber qué tipo de códigos haremos. Podemos hacer lenguajes de programación más aplicados a una plataforma o más específicos. Voy a pone un ejemplo. Cuando estamos dispuestos a usar una plataforma como es Metatrader 4 de Metaquotes, hará falta que sepamos del lenguaje de programación C y que además apliquemos lo que ellos añaden a este lenguaje que sirve para poder operar y poner las operaciones. Esto es no es poca cosa y de hecho, ellos han hecho un manual entero (sin necesidad de libros alternativos) para que puedas leerte su documentación. A parte, hay una larga y extensa documentación de artículos muy interesantes de como usar su plataforma MT4 con infinidad de usos o conectores alternativos. Otros lenguajes como Easylanguage, el cual está usándose en plataformas como ProRealTime, TradeStation o Multicharts, aunque ellos te dan un propio manual para poder aprender este lenguaje que aunque se llame easylanguage, para los iniciados no será tan fácil, ya que se ha de entender, saber y conocer la lógica de programación para poder sacarle todo el partido necesario. Como supongo que entenderéis todos. Pues bien, en la parte generalista hay lenguajes de programación como Python y R. Sobretodo me centraré en estos dos ya que la semana que viene haremos un repaso de que se puede hacer con ellos, que no es para nada poco, ya veréis y es por eso que me centraré sobretodo en estos dos de momento, aunque haré hincapié más adelante y en los cursos a lenguajes de programación de un poco más alto nivel como Java. También haré algo de repaso de C++, aunque de hecho haré menos, ya que hay mucha gente que prefiere no bajar tanto de nivel si no es para una cosa muy especifica. Para aquellos que querais un lenguaje diferente o algo, hacedmelo saber y sin problemas, lo hago! En cuanto a libros de Python (sin tener en cuenta la vertiente de trading) podemos tener la posibilidad de aprender rápido o a un ritmo un poco más normal. En el caso que no tengas tiempo para leer mucho, te recomiendo: – Python: Learn Python in One Day and Learn It Well de Jamie Chan. Este libro se lee solo. Es super-sencillo. En pocas horas aprendes lo básico de python. Lo necesario para plasmar ideas, para aprender a manejarte lo suficiente como para empezar. No necesitas más Yo como soy un grandísimo amante de los libros de O’reilly, voy a recomendaros unos cuantos en este podcast y empezamos por: – Learning Python: Powerful Object-Oriented Programming de Mark Lutz. Es uno de los más completos que he encontrado nunca. La verdad es que si queréis profundizar en Python, no podéis dejar escapar este. Sin duda. – Python for Data Analysis de Wes McKinney. Lo que más me gustó de este libro es que no solo enseña Python desde una vertiente de datos. Sino que explica todo con ejemplos prácticos. Desde el minuto 0 obtiene datos de diferentes fuentes y las trata para demostrarte el potencial de estos lenguajes. Sinceramente, me lo volvería a leer. Ahora vamos con R: – Learning R: A Step-by-Step Function Guide to Data Analysis de Richard Cotton. Es un básico de R. Este libro de hecho esta enfocado a que aprendas a fondo R desde una parte más metodológica. Tanto que incluso explican condicionales, funciones y todo como si no tuvieras ni idea de programación. Para aquellos que quieran empezar, un libro recomendable. – R for Data Science de Hadley Wickham. Este libro está más enfocado a trato de datos, de importación de estos, de trabajar con cantidades grandes de información y de maneras más prácticas y sencillas de un uso masivo de estos datos. La ventaja de este libro es que para aquellos que se quieran ahorrar unos dinerillos, os dejo el link de la página web que ellos mismos han creado para plasmar todo el contenido del libro en versión web: http://r4ds.had.co.nz/. Y como os decía al principio, no quiero alargarme mucho en este podcast ya que de estos 5 libros, me gustaría derivar los interesantes para mi que los haré el viernes que viene. Allí si que hablaré exclusivamente de libros aplicados a finanzas y son los que realmente mucha gente espera como recomendación. En cualquier caso, si necesitáis algún libro más especifico con algún lenguaje de programación aplicada a inversiones especifico, hazedmelo saber a través del formulario de contacto: ferranp.com/contactar y os contesto lo antes posible para poder resolver vuestras dudas. ¡Y por hoy ya está! Tal y como llegó el episodio 1, estoy acabando el 50. Acordaros de suscribiros al canal y de darme un me gusta en iVoox y 5 estrellas en iTunes! ¡Muchas gracias! ¡Buen fin de semana a todos! ¡Hasta el lunes! La entrada 50. Libros técnicos de trading I aparece primero en Ferran P..

sin desde tambi cuando antes esto ahora trading hasta muchas one day tanto tal pues otra otros sino python data science libros java voy siento data analysis sinceramente sobretodo tradestation hadley wickham wes mckinney metatrader mt4 acordaros metaquotes

50. Libros técnicos de trading I

Tecnología y trading

Play Episode Listen Later Feb 3, 2017 18:08

¡Muy buenos días a todos! Hoy vengo a presentaros, como el pasado viernes, libros técnicos de trading. En este caso, más tecnológico y es que creo que para los que quieran combinar la parte más tecnológica con la parte del trading. Voy a subdividirlo en dos vertientes muy diferenciadas y de antemano os aviso que todos los libros que voy a exponer son los que me he leído o los que tengo en cola para leer. Lo que habrá alguno que os recomiende por el hecho que lo he encontrado interesante y esté esperando a leerlo cuando acabe el resto. Otra cosa que cabe decir es que la mayoría de libros (o toda creo de hecho) está en inglés. En castellano no hay nada al respecto que valga la pena o que al menos me haya topado. Antes de empezar por eso quiero decir dos cosas. Esta es la primera parte del podcast de libros técnicos de trading porque da para mucho, sin duda. La segunda cosas es especificar que todos los libros que voy a decir hoy van enfocados sobretodo a gente que sepa programación o que quiera aprender a base de hostias. Siento decirlo así de brusco, pero estos últimos pueden ser gente que quiera aprender a usar sus habilidades de trading para automatizar procesos. Bien, pues estas personas quiero que tengan en cuenta que les costará al principio porque no solo tienen que chocar con la vertiente de entender como hacer las cosas sino amueblar la cabeza a nivel de programación y entender que todo es diferente y lógico. Hay gente que conozco que ha podido y puede de hecho, programar muy bien viniendo de una vertiente de letras. Y es que al final como en todo señores, se han de poner horas de trabajo y estudio. Pues venga, empezamos con la parte generalista. Qué quiere decir. Pues muy fácil: lo que se ha de hacer antes de empezar con la parte técnica, es estudiar programación aplicada. Es decir, los lenguajes de programación que nos van a servir para poder hacer estos códigos. Y es que lo más importante es saber qué tipo de códigos haremos. Podemos hacer lenguajes de programación más aplicados a una plataforma o más específicos. Voy a pone un ejemplo. Cuando estamos dispuestos a usar una plataforma como es Metatrader 4 de Metaquotes, hará falta que sepamos del lenguaje de programación C y que además apliquemos lo que ellos añaden a este lenguaje que sirve para poder operar y poner las operaciones. Esto es no es poca cosa y de hecho, ellos han hecho un manual entero (sin necesidad de libros alternativos) para que puedas leerte su documentación. A parte, hay una larga y extensa documentación de artículos muy interesantes de como usar su plataforma MT4 con infinidad de usos o conectores alternativos. Otros lenguajes como Easylanguage, el cual está usándose en plataformas como ProRealTime, TradeStation o Multicharts, aunque ellos te dan un propio manual para poder aprender este lenguaje que aunque se llame easylanguage, para los iniciados no será tan fácil, ya que se ha de entender, saber y conocer la lógica de programación para poder sacarle todo el partido necesario. Como supongo que entenderéis todos. Pues bien, en la parte generalista hay lenguajes de programación como Python y R. Sobretodo me centraré en estos dos ya que la semana que viene haremos un repaso de que se puede hacer con ellos, que no es para nada poco, ya veréis y es por eso que me centraré sobretodo en estos dos de momento, aunque haré hincapié más adelante y en los cursos a lenguajes de programación de un poco más alto nivel como Java. También haré algo de repaso de C++, aunque de hecho haré menos, ya que hay mucha gente que prefiere no bajar tanto de nivel si no es para una cosa muy especifica. Para aquellos que querais un lenguaje diferente o algo, hacedmelo saber y sin problemas, lo hago! En cuanto a libros de Python (sin tener en cuenta la vertiente de trading) podemos tener la posibilidad de aprender rápido o a un ritmo un poco más normal. En el caso que no tengas tiempo para leer mucho, te recomiendo: – Python: Learn Python in One Day and Learn It Well de Jamie Chan. Este libro se lee solo. Es super-sencillo. En pocas horas aprendes lo básico de python. Lo necesario para plasmar ideas, para aprender a manejarte lo suficiente como para empezar. No necesitas más Yo como soy un grandísimo amante de los libros de O’reilly, voy a recomendaros unos cuantos en este podcast y empezamos por: – Learning Python: Powerful Object-Oriented Programming de Mark Lutz. Es uno de los más completos que he encontrado nunca. La verdad es que si queréis profundizar en Python, no podéis dejar escapar este. Sin duda. – Python for Data Analysis de Wes McKinney. Lo que más me gustó de este libro es que no solo enseña Python desde una vertiente de datos. Sino que explica todo con ejemplos prácticos. Desde el minuto 0 obtiene datos de diferentes fuentes y las trata para demostrarte el potencial de estos lenguajes. Sinceramente, me lo volvería a leer. Ahora vamos con R: – Learning R: A Step-by-Step Function Guide to Data Analysis de Richard Cotton. Es un básico de R. Este libro de hecho esta enfocado a que aprendas a fondo R desde una parte más metodológica. Tanto que incluso explican condicionales, funciones y todo como si no tuvieras ni idea de programación. Para aquellos que quieran empezar, un libro recomendable. – R for Data Science de Hadley Wickham. Este libro está más enfocado a trato de datos, de importación de estos, de trabajar con cantidades grandes de información y de maneras más prácticas y sencillas de un uso masivo de estos datos. La ventaja de este libro es que para aquellos que se quieran ahorrar unos dinerillos, os dejo el link de la página web que ellos mismos han creado para plasmar todo el contenido del libro en versión web: http://r4ds.had.co.nz/. Y como os decía al principio, no quiero alargarme mucho en este podcast ya que de estos 5 libros, me gustaría derivar los interesantes para mi que los haré el viernes que viene. Allí si que hablaré exclusivamente de libros aplicados a finanzas y son los que realmente mucha gente espera como recomendación. En cualquier caso, si necesitáis algún libro más especifico con algún lenguaje de programación aplicada a inversiones especifico, hazedmelo saber a través del formulario de contacto: ferranp.com/contactar y os contesto lo antes posible para poder resolver vuestras dudas. ¡Y por hoy ya está! Tal y como llegó el episodio 1, estoy acabando el 50. Acordaros de suscribiros al canal y de darme un me gusta en iVoox y 5 estrellas en iTunes! ¡Muchas gracias! ¡Buen fin de semana a todos! ¡Hasta el lunes! La entrada 50. Libros técnicos de trading I aparece primero en Ferran P..

sin desde tambi cuando antes esto ahora trading hasta muchas one day tanto tal pues otra otros sino python data science libros java voy siento data analysis sinceramente sobretodo tradestation hadley wickham wes mckinney metatrader mt4 acordaros metaquotes

Episode #69: Hadley Wickham

happy new year hadley wickham

Play Episode Listen Later Jan 10, 2017 22:14

Hi everyone! Happy New Year and welcome back to the PolicyViz Podcast! I hope you had a relaxing holiday season. To kick off 2017, I'm excited to welcome Hadley Wickham to the show to talk about his work with the... The post Episode #69: Hadley Wickham appeared first on PolicyViz.

Episode #69: Hadley Wickham

happy new year hadley wickham

Play Episode Listen Later Jan 10, 2017 22:15

Hi everyone! Happy New Year and welcome back to the PolicyViz Podcast! I hope you had a relaxing holiday season. To kick off 2017, I'm excited to welcome Hadley Wickham to the show to talk about his work with the... The post Episode #69: Hadley Wickham appeared first on PolicyViz.

FEATURED: Our most visited post of 2016 – In this episode of Naturally Speaking Shorts Laurie Baker speaks to R-guru Hadley Wickham, Chief Scientist at RStudio, about his work to make programming and data analysis in the statistical programme R less painful.

conversations jedi chief scientist rstudio hadley wickham

Feather

Data Skeptic

Play Episode Listen Later May 13, 2016 23:04

I'm joined by Wes McKinney (@wesmckinn) and Hadley Wickham (@hadleywickham) on this episode to discuss their joint project Feather. Feather is a file format for storing data frames along with some metadata, to help with interoperability between languages. At the time of recording, libraries are available for R and Python, making it easy for data scientists working in these languages to quickly and effectively share datasets and collaborate.

python feather hadley wickham wes mckinney

Episode 18: Episode 18: Interviews with the RStudio Team