Podcasts about Plotly

Heute wird's bunt, spannend und ein bisschen investigativ! In dieser Folge von Female TechTalk dreht sich alles um Datenvisualisierungen. Wir zeigen euch, wie Diagramme, Infografiken & Heatmaps uns helfen, riesige Datenmengen verständlich zu machen und trotzdem mit Vorsicht zu genießen sind.Was hat es mit Scatterplots auf sich, und wie hilft uns die lineare Regression, aus Daten echte Zusammenhänge zu erkennen? Außerdem stellen wir euch einige der mächtigsten Tools vor – von Matplotlib & Seaborn bis hin zu Plotly & D3.js.Und natürlich darf die große Frage nicht fehlen: Korrelation oder Kausalität? Macht Schokolade essen wirklich schlauer? Oder gibt es vielleicht einen versteckten dritten Faktor?

AI, data apps, and a preview of FinovateEurope.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Mech Interp Challenge: September - Deciphering the Addition Model, published by TheMcDouglas on September 13, 2023 on The AI Alignment Forum. I'm writing this post to discuss solutions to the August challenge, and present the challenge for this September. Apologies for this coming so late in the month (EAGxBerlin was taking up much of my focus in the first half of this month). If you've not read the first post in this sequence, I'd recommend starting there - it outlines the purpose behind these challenges, and recommended prerequisite material. September Problem The problem for this month (or at least as much of the month as remains!) is interpreting a model which has been trained to perform simple addition. The model was fed input in the form of a sequence of digits (plus special + and = characters with token ids 10 and 11), and was tasked with predicting the sum of digits one sequence position before they would appear. Cross entropy loss was only applied to these four token positions, so the model's output at other sequence positions is meaningless. The model is attention-only, with 2 layers, and 3 heads per layer. It was trained with layernorm, weight decay, and an Adam optimizer with linearly decaying learning rate. You can find more details on the Streamlit page. Feel free to reach out if you have any questions! August Problem - Solutions You can read full solutions on the Streamlit page, or on my personal website. Both of these sources host interactive charts (Plotly and Circuitsvis) so are more suitable than a LessWrong/AF post to discuss the solutions in depth. However, I've presented a much shorter version of the solution below. If you're interested in these problems, I'd recommend having a try before you read on! The key idea with this model is path decomposition (see the corresponding section of A Mathematical Framework for Transformer Circuits). There are several different important types of path in this model, with different interpretations & purposes. We might call these negative paths and positive paths. The negative paths are designed to suppress repeated tokens, and the positive paths are designed to boost tokens which are more likely to be the first unique token. Let's start with the negative paths. Some layer 0 heads are duplicate token heads; they're composing with layer 1 heads to cause those heads to attend to & suppress duplicated tokens. This is done both with K-composition (heads in layer 1 suppress duplicated tokens because they attend to them more), and V-composition (the actual outputs of the DTHs are used as value input to heads in layer 1 to suppress duplicated tokens). Below is an example, where the second and third instances of a attend back to the first instance of a in head 0.2, and this composes with head 1.0 which attends back to (and suppresses) all the duplicated a tokens. Now, let's move on to the positive paths. Heads in layer 0 will attend to early tokens which aren't the same as the current destination token, because both these bits of evidence correlate with this token being the first unique token at this position (this is most obvious with the second token, since the first token is the correct answer here if and only if it doesn't equal the second token). Additionally, the outputs of heads in layer 0 are used as value input to heads in layer 1 to boost these tokens, i.e. as a virtual OV circuit. These paths aren't as obviously visible in the attention probabilities, because they're distributed: many tokens will weakly attend to some early token in a layer-0 head, and then all of those tokens will be weakly attended to by some layer-1 head. But the paths can be seen when we plot all the OV circuits, coloring each value by how much the final logits for that token are affected at the destination position: Another interesting o...

In this episode, Kelly and Sean meet with Adam Schroeder, community manager at Plotly and author of The Book of Dash. We'll talk about how teachers can use data visualization in the classroom with Dash to bring data to life for their students. If you're not familiar with the Dash and Plotly Python libraries, you can get started here with us! Dash is a library for building web-based dashboards to visualize your Python data. It uses Plotly, a library for making beautiful visualizations of data that are highly interactive. Both libraries are open-source and maintained by the Plotly company, which provides enterprise-grade implementations of Dash and Plotly for business customers. Special Guest: Adam Schroeder.

Chris Parmer, Chief Product Officer and Co-Founder of Plotly talks to us about how he's working to ensure that even the most advanced analytic insights are accessible by everyone – whether or not they know how to code! In this week's episode of the Business of Data podcast, host Catherine King talks with Chris Parmer, Chief Product Officer and Co-Founder of software company, Plotly. Together they walk through the challenges and benefits of empowering data scientists with data visualization and data apps. In the discussion this week: Communicating complex information to the business Role development for data scientists, enabling data scientists to publish and share their work How visualization is going to shape data adoption in the market The benefit of interactive data apps as a technology Today's podcast episode was made possible by our partnership with Plotly! Plotly is a software company whose mission is to enable every company to build data apps. Their product, Dash Enterprise, is a platform of best-in-class development tools to quickly and easily analyze and visualize data with Python from virtually any data source. With customers across the Fortune 500, Plotly is a category-defining leader in enabling data-driven decisions from advanced analytics, machine learning, and artificial intelligence. For more information, visit Plotly here.

Do you struggle to know where to start in the wide range of Python's visualization frameworks? Not sure when to use Plotly vs. Matplotlib vs. Altair? Then this episode is for you. We have Chris Moffitt, a Talk Python course author and founder of Practical Business Python, back on the show to discuss getting started with Python's data visualization frameworks. Links from the show Chris on Twitter: @chris1610 Python Data Visualization course: talkpython.fm 10 tips to move from Excel to Python episode: talkpython.fm Escaping Excel Hell with Python and Pandas episode: talkpython.fm PB Python: pbpython.com matplotlib: matplotlib.org Seaborn example: seaborn.pydata.org Altair: altair-viz.github.io Plotly sunburst: plotly.com Plotly treemap: plotly.com streamlit: streamlit.io Dash: dash.gallery Streamlit Talk Python episode: talkpython.fm splink package: github.com redframes package: github.com Edward Tufte book: edwardtufte.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Microsoft Talk Python Training AssemblyAI

Mathias Brandewinder enjoys solving challenging business problems with software engineering and applied mathematics techniques, and some creativity. His current focus is on functional programming with F#, machine learning, and data science, and on putting them together to help companies make smarter decisions with their data. He loves teaching and mentoring, and is a regular speaker at conferences and community events all over the world. A former Microsoft F# MVP, he holds an MS in Operations Research from Stanford, an MS in Economics from University of Paris X, and an MBA from ESSEC.   Topics of Discussion: [3:15] Mathias talks about how his love of math got him into programming. [5:57] Mathias discusses what intrigued him about F# and the scripting environment. [6:29] What about when a computer's version of a number doesn't really line up with the math version of a number? [11:51] What issues does F# help more than C#? [15:22] What is Mathias's favorite charting component for .NET? [18:27] What inspired Mathias to write his book, Machine Learning Projects for .NET Developers, and is there a new book on the horizon? [20:09] Mathias is here to say math can be genuinely fun! [24:03] Jupiter appears to be an on-the-fly evaluator of code that runs on a server somewhere through the browser. [29:13] What other math-related libraries should we be aware of, and what are some other resources we can check out?   Mentioned in this Episode: Architect Tips — New video podcast! Azure DevOps Clear Measure (Sponsor) .NET DevOps for Azure: A Developer's Guide to DevOps Architecture the Right Way, by Jeffrey Palermo — Available on Amazon! Jeffrey Palermo's YouTube Jeffrey Palermo's Twitter — Follow to stay informed about future events! Programming with Palermo programming@palermo.net Mathias Brandewinder Website Mathias Brandewinder Bio mathias@brandewinder.com Five obscure charting tips with Plotly.NET Machine Learning Projects for .NET Developers DiffSharp Math.Net Numerics F# Get Started with OR-Tools for C#   Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

The Practical Guide is a series of articles about how to start your own marketplace business by Christóbal Gracia and Juho Makkonen, available in Marketplace Academy. This podcast brings you the content in audio format.Article available at: 11 marketplace metrics you should be tracking to measure your success, Juho MakkonenSources and further reading:  Ramen Profitable, Paul Graham  Growing a marketplace? How to balance sellers and buyers using customer lifetime value, cleverlayover  How to design the booking flow of your service marketplace, Marketplace Academy  Metrics For Marketplaces, Sangeet Paul Choudary  Net promoter score, Wikipedia  The One Number You Need to Grow, Harvard Business Review  The Sean Ellis Test, Johan Steenkamp  How to find product/market fit, cobloom  A Guide to Marketplaces: Third Edition, Angela Tran Kingyens & Boris Wertz  Vanity Metrics vs. Actionable Metrics, Eric Ries Analytics and tracking tools mentioned in the chapter:  Marketplace KPI Spreadsheet, by Version One Ventures  Marketplace KPI Spreadsheet by daphni  KPIs & Dashboards - MUST HAVES for Early Stage Startups, Vijay Nagappan  Keen.io  Plotly  mixpanel  UserReport  UserVoice  Qualaroo  SurveyMonkey Learn more about building a marketplace at www.sharetribe.com/academy/.Looking to build a marketplace of your own? Visit www.sharetribe.com!

We are excited to introduce our first guest for season 2, Ali Shobeiri! Ali's impressive industry experience includes Software Engineering, Data Science, and Machine Learning roles in exciting tech companies like Microsoft, Ubisoft, Unity, Behavior Interactive, SAP and Plotly. He's been a mentor to many, myself included, and we were incredibly impressed by the pace and trajectory of his career! In our podcast with him, we cover a lot of topics including the origins of McGill's AI Bootcamp, how to land Machine Learning roles as an undergraduate student, and also how Ali structured his life at university.

welcome to the nonlinear library, where we use text-to-speech software to convert the best writing from the rationalist and ea communities into audio. this is: EffectiveAltruismData.com: A Website for Aggregating and Visualising EA Data, published by Hamish Huggard on the effective altruism forum. There's lots of cool data floating around in EA: grant databases, survey results, growth metrics, etc. I'm a data scientist and enjoy data visualisation, so thought it would be a fun project to build a website which aggregates EA data into interactive plots. The website is now live at EffectiveAltruismData.com. Source code is available on Github. This project is still a work in progress: the data is pretty out of date and I've got lots of future work planned. But it's far enough along that I'd like some public feedback. The website is responsive and should look good on any desktop or tablet screen. If you're viewing on a phone it will probably look ok, but you may need to alternate between portrait and landscape. Here are a few screenshots: Implementation Details The website is mostly coded in Python. The main libraries I used were: Pandas for data handling. Plotly for creating the interactive plots. Dash for the web framework. I also wrote a bunch of vanilla CSS for the frontend styling. The web server is currently deployed with Heroku, which costs $7/month. I have vague ambitions to re-implement the frontend with D3.js or Chart.js. This should cut down the loading time and give me more control over how the visualisations work. Design Philosophy I aimed to follow the data visualisation principles laid out in Information Dashboard Design and Storytelling with Data. These include: Minimise the “ink-to-data” (or “pixel-to-data”) ratio to avoid distracting clutter. Encoding data in length or distance is much higher fidelity than area or angle. Avoid pie charts, stacked area plots, radar charts, violin plots. Stick to bar charts, scatter plots, and line graphs as much as possible. Don't make the reader rotate their head. Use horizontal bar charts rather than vertical ones. Minimise the total length the eye has to travel to take in all the data. Avoid legends. On line graphs, put the labels directly on the ends of the lines. Why I Did This I said earlier that this project was motivated by my fancy for data visualisation. But I do think there's a lot of scope for valuable data visualisation and data wrangling work within Effective Altruism. For example, I've found it difficult to get a sense of the scale of donations within EA. Are total donations basically Open Philanthropy plus a rounding error? Or do donations from all the little guys like me actually make a difference in the big picture? This isn't just an interesting question in itself: it also informs my life decisions. If the total donations of people in my reference class is enough to make a noticeable change to AMF funding, then I'm more likely to steadily earn-to-give on a moderately affluent career path. If my reference class is totally overwhelmed by a handful of mega-donors, then I'm more likely to drop everything and spend a year figuring out if I can contribute to AI safety. This was some of the motivation behind the first panel of EffectiveAltruismData.com. Ultimately, I'd like to have a plot which puts all the major stocks and flows of EA money on a common scale and puts my personal earning-to-give into perspective. Another example: I have a vague sense that EAs are getting more diverse over time. Is this true? Currently, answering this question would require going through all the EA survey reports, reading numbers from images of plots, and typing them into a spreadsheet. It would be nice if all the data was easily accessible from some central repository and ready for analysis. Data visualisation is a great tool for getting lots of people quickly up to speed on quantitative facts. Gapminder and Our World in Data do this to great effect. If we want EA to be an efficient ...

I got a series of questions from a guy on Meetup.com. These are my answers on developing a trading bot via video but the dangerous content ha to be on this alternative platform. Hi, Bryan, Thank you for responding to my email. I am trying to learn (and kinda make) a bot for a couple of months now. I know that you can't make a bot in an hour but, I would suggest that the subject would be just a small part of making a bot OR the whole hour could be questions and answers session where people can ask you and also discuss among themselves. I am looking to meet people whom I can ask simple questions and exchange experiences and ideas, once a week maybe. My problem is that I know what I want to achieve but coding is something of a challenge at the moment. I have accounts with Kraken and Interactive Brokers and I am interested in making a real deal bot. One of the topics I would suggest: – incorporating TA-Lib, Plotly and Dash to make a bot with reach user interface. I found a lot of resources about Dash and about trading bot, but none which would incorporate both. There is no clear info about how Dash works with additional python code. For example, if you want to make a bot which will research thousands of stocks, do TA on them and then fine-tune (fitting) – it could take hours. The question is where you code that code? Before layout section, inside def function section? It looks it would make Dash unresponsive for a long time. I couldn't find info about how Dash checks for input: is it like continuous loop just for Dash and nothing else until there is change on the dashboard, or, works more like a keyboard – checks dashboards once every 100 milliseconds and does other tasks in the meantime? I believe I found a solution but doesn't really look practical: to use dcc.interval and from def section call a function placed before layout for, let's say 900 milliseconds,  remember and return where looping through strategy stopped, return value and show on dashboard loops and positions, and go back to looping after about another 100 ms (next dcc.interval call). Another subject for meetings could be “success stories” – what worked and what didn't… what people found to be the best, or just working, solution for a particular problem. Services I mention in the videos below Lowest monthly cost you will get https://quantlabs.net/shop/product/quant-analytics/ 50% discount applied https://quantlabs.net/shop/product/quant-analytics-subscription-6-month-promo/ Please let me know what you think. Get a free trading PDFs https://quantlabs.net/contact-quantlabsnet/ Here is my Telegram https://t.me/quantlabs Discord server https://discord.gg/RhdbV2hx

Os dejamos con la miel en los labios, pero hasta ese momento hablamos de la interacción entre las clases abstractas y MYPY, más complejidad en la sintaxis de Python con el PEP 637, PIP, dependencias varias y seguridad, y los tipos de PEP que hay. ¿Bokeh o Plotly? Haberte conectado en directo... https://podcast.jcea.es/python/22 Participantes: Jesús Cea, email: jcea@jcea.es, twitter: @jcea, https://blog.jcea.es/, https://www.jcea.es/. Conectando desde Madrid. Javier, conectando desde Madrid. Miguel Sánchez, email: msanchez@uninet.edu, conectando desde Las Palmas. Eduardo Castro, email: info@ecdesign.es. Conectando desde A Guarda. Víctor Ramírez, twitter: @virako, programador python y amante de vim, conectando desde Huelva. José Juan. Audio editado por Pablo Gómez, twitter: @julebek. La música de la entrada y la salida es "Lightning Bugs", de Jason Shaw. Publicada en https://audionautix.com/ con licencia - Creative Commons Attribution 4.0 International License. [00:53] Vamos progresando en la publicación de las grabaciones. Pablo Gómez, twitter: @julebek, nuestro sufrido editor de audio, está enviando a Jesús Cea ya algunos audios procesados. La calidad de los audios originales es bastante mala. Capítulos en audios: Biblioteca toc2audio https://docs.jcea.es/toc2audio/. Jesús Cea quiere notas exhaustivas de cada grabación. El audio es casi redundante. Lo que estás leyendo ahora mismo, vaya. [06:45] El aviso legal de rigor para grabar los audios y publicarlos luego. [06:58] Presentaciones. [09:58] Jesús Cea habla mucho. [10:53] Interacción entre clases abstractas https://docs.python.org/3/library/abc.html y MYPY http://mypy-lang.org/. [18:28] Expansión explosiva del tamaño del bytecode https://es.wikipedia.org/wiki/Bytecode generado por un programa trivial Python. Ojo, teclear este código tal cual puede tumbar tu máquina y puedes necesitar reiniciar, perdiendo lo que estés haciendo con el ordenador. (1

Show Notes(2:20) Pier shared his college experience at the University of Southampton studying Electronic Engineering.(3:46) For his final undergraduate project, Pier developed a suite of games and used machine learning to analyze brainwaves data that can classify whether a child is affected or not by autism.(11:26) Pier went over his favorite courses and involvement with the AI Society during his additional year at the University of Southampton to get a Master’s in Artificial Intelligence.(13:40) For his Master’s thesis called “Causal Reasoning in Machine Learning,” Pier created and deployed a suite of Agent-Based and Compartmental Models to simulate epidemic disease developments in different types of communities.(26:51) Pier went over his stints as a developer intern at Fidessa and a freelance data scientist at Digital-Dandelion.(29:21) Pier reflected on his time (so far) as a data scientist at SAS Institute, where he helps their customers solve various data-driven challenges using cloud-based technologies and DevOps processes.(33:37) Pier discussed the key benefits that writing and editing technical content for Towards Data Science to his professional development.(36:31) Pier covered the threads that he kept pulling with his blog posts.(38:50) Pier talked about his Augmented Reality Personal Business Card created in HTML using the AR.js library.(41:12) Pier brought up data structures in two other impressive JavaScript projects using TensorFlow.js and ml5.js.(44:19) Pier went over his experience working with data visualization tools such as Plotly, R Shiny, and Streamlit.(47:27) Pier talked about his work on a chapter for a book called “Applied Data Science in Tourism” that is going to be published with Springer this year.(48:37) Pier shared his thoughts regarding the tech community in London.(49:19) Closing segment.Pier’s Contact InfoWebsiteLinkedInTwitterGitHubMediumPatreonKaggleMentioned Content“Alleviate Children’s Health Issues Through Games and Machine Learning”“Causal Reasoning in Machine Learning”Andrej Karpathy (Director of AI and Autopilot at Tesla)Cassie Kozyrkov (Chief Decision Scientist at Google)Iain Brown (Head of Data Science at SAS)“The Book Of Why” (By Judea Pearl)“Pattern Recognition and Machine Learning” (by Christopher Bishop)

Show Notes(2:20) Pier shared his college experience at the University of Southampton studying Electronic Engineering.(3:46) For his final undergraduate project, Pier developed a suite of games and used machine learning to analyze brainwaves data that can classify whether a child is affected or not by autism.(11:26) Pier went over his favorite courses and involvement with the AI Society during his additional year at the University of Southampton to get a Master’s in Artificial Intelligence.(13:40) For his Master’s thesis called “Causal Reasoning in Machine Learning,” Pier created and deployed a suite of Agent-Based and Compartmental Models to simulate epidemic disease developments in different types of communities.(26:51) Pier went over his stints as a developer intern at Fidessa and a freelance data scientist at Digital-Dandelion.(29:21) Pier reflected on his time (so far) as a data scientist at SAS Institute, where he helps their customers solve various data-driven challenges using cloud-based technologies and DevOps processes.(33:37) Pier discussed the key benefits that writing and editing technical content for Towards Data Science to his professional development.(36:31) Pier covered the threads that he kept pulling with his blog posts.(38:50) Pier talked about his Augmented Reality Personal Business Card created in HTML using the AR.js library.(41:12) Pier brought up data structures in two other impressive JavaScript projects using TensorFlow.js and ml5.js.(44:19) Pier went over his experience working with data visualization tools such as Plotly, R Shiny, and Streamlit.(47:27) Pier talked about his work on a chapter for a book called “Applied Data Science in Tourism” that is going to be published with Springer this year.(48:37) Pier shared his thoughts regarding the tech community in London.(49:19) Closing segment.Pier’s Contact InfoWebsiteLinkedInTwitterGitHubMediumPatreonKaggleMentioned Content“Alleviate Children’s Health Issues Through Games and Machine Learning”“Causal Reasoning in Machine Learning”Andrej Karpathy (Director of AI and Autopilot at Tesla)Cassie Kozyrkov (Chief Decision Scientist at Google)Iain Brown (Head of Data Science at SAS)“The Book Of Why” (By Judea Pearl)“Pattern Recognition and Machine Learning” (by Christopher Bishop)

This episode of #DataFemme is the second sponsored by the wonderful Plotly. Hear from Laura Gray, People and Operations at Plotly, about supporting new parents at work and defining success in a way that fits you.Learn more about Plotly at plotly.com. Support #DataFemme with some cool new prizes at www.patreon.com/datafemme

In this episode of #DataFemme, Emmanuelle Gouillart, Developer at Plotly, discusses the Python community, how image processing and data visualization work in tandem and best practices for contributing to open source.This episode is kindly sponsored by Plotly.To learn more about Plotly and using their framework Dash for building web applications with R and Python, visit: https://dash.plotly.com/Any questions? Plotly would like to answer! Contact them here: https://plotly.com/contact-usIf you enjoyed this episode, support the creation of future content by becoming a patron of #DataFemme on Patreon: www.patreon.com/datafemme

In this episode of the B.Next Outbreak Analytics & Forecasting series, Dr. Dylan George is joined by Nicolas Kruchten, VP of Product at Plotly, an In-Q-Tel portfolio company helping leading organizations close the gap between data science and the rest of the organization. Learn about the power of good data visualizations and how they can help guide decision-making and aid in COVID-19 response. References: www.plotly.com Roundup of Dash apps made by community and partners: https://moderndata.plotly.com/covid-19-dash-apps/ Singapore Ministry of Health: https://covidsitrep.moh.gov.sg/  Nursing home data and app: https://covid-19-response.demo.socrata.com/stories/s/3eae-9zd5 and https://tylertech.plotly.com/ Johns Hopkins Coronavirus Resource Center: https://coronavirus.jhu.edu/data/new-cases Tracking and Comparing projections: https://www.covid-projections.com/ NVIDIA RAPIDS app: https://github.com/rapidsai/plotly-dash-rapids-census-demo/tree/covid-19  Traffic analysis app by Stantec: https://covid-vmt.stantec.global/

On Episode 29 of Scatter Podcast, Joel interviews guest Alex Johnson, Founder of Plotly, a leading platform and library (Python & R) for creating data analytics visualizations.

GotTechED the Podcast Episode #(61) 20 GSuite Apps and Add-Ons In this episode of GotTechED the Podcast we discuss: 40 G-Suite Activities to Try in Your Classroom If you are a new listener to GotTechED the Podcast, we would love to hear from you. Please visit our (https://gotteched.com/contact-us/#) and let us know how we can help you today! Welcome back to gotteched the podcast. Episodses 58 - 63 are part of a 5-episode series devoted to G-Suite Activities, Addons, Teacher Extensions, and Student Extensions. In this episode we’ll share 20 of our favorite GSuite apps and add-ons that you and your students can start using today. In episodes 62 and 63 we’ll break down some of our favorite extensions for teachers and for students when working in the classroom. If you need some inspiration for digital lessons, these are the episodes you’ve been waiting for. Check it out. Segment 1: Share, Review, Spread the WordDo us 3 favors: Subscribe to GotTechED the Podcast Apple Podcasts (https://itunes.apple.com/us/podcast/gotteched/id1358366637?mt=2) Spotify (https://open.spotify.com/show/7zyzfCkSDNHkKdqxmh9XLB?si=YhSdMa6BQVmcLHbSrYxE9Q) Google Podcasts (https://play.google.com/music/listen?u=0#/ps/Indeizidhz4h37mawfylwdgco4y)    Stitcher (https://www.stitcher.com/search?q=gotteched)    YouTube (https://www.youtube.com/channel/UCMIQwu39Tkow3kduRQAH85w?view_as=subscriber)    Twitter (https://twitter.com/WeGotTechED)     Facebook (https://www.facebook.com/WeGotTechED/) Write us a review, leave us a comment or rate us on whatever platform you listen Tell your friends about www.gotteched.com (http://www.gotteched.com) Segment 2: #G-Suite and Chrome Add-OnsWhat are add-ons? https://developers.google.com/gsuite/add-ons/overview (https://developers.google.com/gsuite/add-ons/overview) Is an add-on different then an extension? Sort of! https://technology.dearbornschools.org/2019/04/25/chrome-extensions-vs-add-ons/ (https://technology.dearbornschools.org/2019/04/25/chrome-extensions-vs-add-ons/) Go to the Add-Ons Store for any G-Suite App by opening a doc, slide, or sheet, etc clicking the “Add-Ons” button right next to Tools. Copy Folder (https://chrome.google.com/webstore/detail/copy-folder/kfbicpdhiofpicipfggljdhjokjblnhl?hl=en) (copies the folder, sub-folders, and documents within the folders) Plotly (https://chrome.google.com/webstore/detail/plotly/khajkhinhblhaenlhpodnblkmpdgclne?hl=en) (Online, free, graphing and spreadsheet tool) Voice Note II (https://chrome.google.com/webstore/detail/voicenote-ii-speech-to-te/hfknjgplnkgjihghcidajejfmldhibfm?hl=en) (Voice typing, copy and paste script) Beautiful Audio Editor (https://chrome.google.com/webstore/detail/beautiful-audio-editor/okiblndpcefmebnkjnjfplijnelbcjmm?hl=en) (Record and Edit Audio)  Telly Prompt (https://chrome.google.com/webstore/detail/tellyprompt/okglebjjanfmfeifbdlapeanoidbkaif?hl=en) (Online Teleprompter) Video Cutter (https://chrome.google.com/webstore/detail/video-cutter/nodkcjollmmjidmcnhloaoahmciabnai?hl=en) (Cut portions of your videos)  Feedly (https://chrome.google.com/webstore/detail/feedly/hipbfijinpcgfogaopmgehiegacbhmob?hl=en) (All your favorite sites in one place) Beatlab (https://chrome.google.com/webstore/detail/beatlab/alnfdikmbdfgkcbdodjcbmedanjinmkk?hl=en) (Make beats for your presentations) Camera (https://chrome.google.com/webstore/detail/camera/hfhhnacclhffhdffklopdkcgdhifgngh?hl=en) (Pictures and Video) EasyBib Bibliography Creator (https://gsuite.google.com/marketplace/app/easybib_bibliography_creator/351103992693?pann=cwsdp&hl=en) (Docs Add-On) MindMeister (https://gsuite.google.com/marketplace/app/mindmeister/274005578028?pann=cwsdp&hl=en) (Docs Add-On) - turn a bullet pointed list into an appealing visual mind map

Adatépítész -az első magyar datapodcast Minden ami hír, érdekesség, esemény vagy tudásmorzsa az  adat, datascience, adatbányászat és hasonló kockaságok világából. Iratkozz fel az új epizódokért! Az epizód linkjei: AMD Ryzen-Powered Raspberry Pi Rival Uses Radeon Vega Graphics Colab Pro Plotly and NVIDIA Partner to Integrate Dash and RAPIDS

It’s easy to think of data scientists as “people who explore and model data”. Bur in reality, the job description is much more flexible: your job as a data scientist is to solve problems that people actually have with data. You’ll notice that I wrote “problems that people actually have” rather than “build models”. It’s relatively rare that the problems people have actually need to be solved using a predictive model. Instead, a good visualization or interactive chart is almost always the first step of the problem-solving process, and can often be the last as well. And you know who understands visualization strategy really, really well? Plotly, that’s who. Plotly is a company that builds a ton of great open-source visualization, exploration and data infrastructure tools (and some proprietary commercial ones, too). Today, their tooling is being used by over 50 million people worldwide, and they’ve developed a number of tools and libraries that are now industry standard. So you can imagine how excited I was to speak with Plotly co-founder and Chief Product Officer Chris Parmer. Chris had some great insights to share about data science and analytics tooling, including the future direction he sees the space moving in. But as his job title suggests, he’s also focused on another key characteristic that all great data scientists develop early on: product instinct (AKA: “knowing what to build next”).

Die sechste Folge beschäftigt sich mit einer der wohl bekanntesten und meistgenutzten Python-Bibliotheken: "Pandas" Diesmal haben wir als Expertengast Simon dabei, der uns mehr über die Funktionen von Pandas erzählt. Shownotes Unsere E-Mail für Fragen, Anregungen & Kommentare: hallo@python-podcast.de News Shared memory for multiprocessing (struct, wenn man das von Hand machen will) Operatoren für Dictionaries Pandas Pandas Cheatsheets Teil 1, Teil 2 Tutorialnotebook von Jochen Maßeinheiten für dataframes mit pint (noch nicht released) - verwendet die neue extension array api Erster Einblick in die Daten im Pandas Workflow mit df.head() df.tail() und df.describe() df.apply() Eher für Fortgeschrittene: Modern Pandas Artikel über pivot, stack und unstack Pandas 2.0 Podcasts und Talks Jeff Reback - What is the Future of Pandas Wes McKinney's Career In Python For Data Analysis - Episode 203 Episode #200: Escaping Excel Hell with Python and Pandas R R, R studio Shiny Picks Django Chat Podcast (Jochen) Django-ORM-like, aber für flat files:  alkali (Jochen) Matplotlib to Plotly (Simon) pickle (Dominik) Öffentlicher Tag auf konektom

Добрый день уважаемые слушатели. Представляем новый выпуск подкаста RWpod. В этом выпуске: Ruby An Update on Bundler 2.0, Skip devise trackable module for API calls to avoid users table getting locked и Implementing Google Authenticator in active admin JavaScript Introducing reCAPTCHA v3: the new way to stop bots, Why React's new Hooks API is a game changer и Why JWT shouldn't be stored in Local Storage The Evolution of Async JavaScript: From Callbacks, to Promises, to Async/Await, An introduction to plotly.js — an open source graphing library и Ervy - bring charts to terminal

Reactivity, 7 tips for Nuxt, vue-test-utils, Plotly.js, Ably Realtime, Vuex with TypeScript, Nuxt.js and Contentful, Vue Native Starter Kit, Handwriting with MyScript, and Vuelidate validation messages.

Support these videos: http://pgbovine.net/support.htmhttp://pgbovine.net/PG-Vlog-143-output-of-academia-is-people-2.htm- [PG Vlog #40 - The Purpose of Technology-Related Research](http://pgbovine.net/PG-Vlog-40-technology-research.htm)- [Plotly company webpage](https://plot.ly/)- [Academic profile of Plotly founder Alex Johnson](http://environment.harvard.edu/about/fellows/alex-johnson)- [ACM Software System Award](https://awards.acm.org/software-system)Recorded: 2018-05-03 (2)

Data visualisation describes any effort to help people understand the significance of data by placing it in a visual context. Huge amounts of data are transformed into easily digestible formats that can be used to communicate complex ideas and patterns, which help businesses evaluate data to show performance. Data visualisation is becoming a valuable tool for marketers, as it easily conveys data to mass audiences and can streamline client reports. In this edition, we spoke to our own art director, Rob Harris, about data visualisation. Rob told us why data visualisation is so important, how he uses it for work, and what developments he sees coming up on the horizon. Find out more about some of the products Rob mentioned in the podcast: Plotly: https://plot.ly/ Data Studio by Google: https://www.google.com/analytics/data-studio/ Datawrapper: https://www.datawrapper.de/ Tableau: https://www.tableau.com/ Visually: https://visual.ly/ DataHero: https://datahero.com/ Google Charts: https://developers.google.com/chart/ Music: "Halter Top" by Podington Bear, used under CC BY / Cut down from original

This episode features Plotly, Prezi, Quizlet, Cold Turkey, and f.lux.

In this week's episode of the PolicyViz Podcast, I speak to Chris Parmer, Chief Product Officer and co-founder of Plotly, the online data visualization and analytics firm. Chris and I talk about Plotly's tool, their business model, open source data... The post Episode #33: Chris Parmer from Plotly appeared first on PolicyViz.