Podcasts about Data science

Send us a textDr. Adrian Soto-Mota is a returning guest on our show! Be sure to check out episode 138 of Boundles Body Radio, which was part of a special series we did, featuring Dr. Nick Norwitz as the guest host!We also hosted Dr. Soto-Mota on episode 340, episode 419, and episode 599 of our show!Dr. Soto-Mota is a MD PhD & Specialist in Internal Medicine and Data Science researcher at the Unidad de Investigación de Emfermedades Metabólicas! Dr. Soto-Mota is passionate about studying low carbohydrate and ketogenic diets, and how they impact human metabolism.Dr. Soto-Mota earned his MD from the Universidad Nacional Autónoma de México, and earned his Ph.D. at Oxford. He has created many resources to help people successfully implement a low carbohydrate diet, and provides that help for both English and Spanish speaking individuals.He is the co-author of a 2022 paper titled The Lipid Energy Model: Reimagining Lipoprotein Function in the Context of Carbohydrate-Restricted Diets, and the co-author of the recent paper titled Plaque Begets Plaque, ApoB Does Not: Longitudinal Data From the KETO-CTA Trial- JACC Journal April 7, 2025, both of which were also co-authored by former guests Dr. Norwitz and Dave Feldman, who we hosted in episode 109 of Boundless Body Radio!Find Dr. Soto-Mota at-TW- @AdrianSotoMotaPlaque Begets Plaque, ApoB Does Not: Longitudinal Data From the KETO-CTA Trial- JACC Journal April 7, 2025Keto Cholesterol study SHOCKS scientific community | LMHRs & heart disease from the Nutrition Made Simple YouTube ChannelAnalyzing the KETO-CTA Study with Dr. Gil Carvalho 813 on Boundless Body Radio!Discussing Keto-CTA with Darius Sharpe with Dave Feldman and Darius SharpeFind Boundless Body at- myboundlessbody.com Book a session with us here!

Wharton's Cade Massey, Eric Bradlow, Shane Jensen, and Adi Wyner speak with Ron Yurko, Assistant Teaching Professor in the Department of Stats & Data Science at Carnegie Mellon University, and Director of the Carnegie Mellon Sports Analytics Center, about Scottie Scheffler's PGA win, golf analytics and modeling, and assessing long-term performance. Hosted on Acast. See acast.com/privacy for more information.

In this week's episode, the last of Season 6,  Patrick and Greg pull back the curtain and reveal how the Quantitude sausage is actually made. Their motivation is to share their own joys and challenges in making a podcast in the hope that others might consider doing this themselves, whether it be for simple self-satisfaction or for using it as a free speech platform in a time when other avenues of communication are feeling increasingly compromised. Along the way they also discuss baring your soul, being 20 minutes away, losing money, Guglielmo Marconi, palak paneer, Taylor Swift, Machiavelli's bad rap, Quincy Jones, hostage negotiations, two blind squirrels, our Innies, for love of the game, Jiffy (in moderation), Blood Meridian, and Edmund Burke.Stay in contact with Quantitude! Web page: quantitudepod.org TwitterX: @quantitudepod YouTube: @quantitudepod Merch: redbubble.com

We've all seen the headlines - AI is revolutionising everything from how students learn to how teachers teach. The promise of personalised learning paths, automated grading, and AI teaching assistants has created a gold rush mentality in education technology. But in our rush to adopt these powerful new tools, are we moving too fast? Today we'll explore why when it comes to AI in education, we need to learn fast but act more slowly and thoughtfully. We'll look at both the tremendous opportunities and serious risks that AI tools present for students and educators. We'll examine where AI can truly add value in education versus where human teachers remain irreplaceable. And most importantly, we'll discuss why comprehensive AI literacy and training is absolutely crucial - not just for educators, but for everyone involved in shaping young minds. Drawing on insights from leading experts on the frontlines of AI in education, we'll provide a framework for thinking about how to implement AI tools responsibly and effectively. Whether you're a teacher, administrator, policymaker or parent, this episode will give you practical guidance for navigating the AI revolution in education. Talking points and questions may include: Opportunities and risks of the tools: Adaptive or personalised learning paths, automated marking and feedback, content generation, analytics and teaching assistants, but also inaccuracy and lack of transparency, data risks, biases, ethics and safeguarding, and like social media, the unintended lasting consequences Where AI is best placed: Is it EdTech and tools in the classroom, the augmentation and elevation of human intelligence, or is it just learning about AI and what it can do and why (is knowledge=power enough?) Why it is so important that understanding and training are emphasised and why everyone needs to have such training Without it there can be safeguarding disasters, skills training can be insufficient, many AI tool providers are offering free training to learn to use their tool but this is consumerised and inadequate and can be ethically questionable; do we want successive generations to only be producing AI tools that are exploitative and using our data and our IP without our consent, or do we want to help people with technology and for the partnership to be of most benefit to them? Guests: Rt. Hon the Lord Knight of Weymouth, Jim Knight Rob Robson, ASCL Trust Leadership Consultant

The folks over at Astral have made some big-time impacts in the Python space with uv and ruff. They are back with another amazing project named ty. You may have known it as Red-Knot. But it's coming up on release time for the first version and with the release it comes with a new official name: ty. We have Charlie Marsh and Carl Meyer on the show to tell us all about this new project. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Talk Python's Rock Solid Python: Type Hints & Modern Tools (Pydantic, FastAPI, and More) Course: training.talkpython.fm Charlie Marsh on Twitter: @charliermarsh Charlie Marsh on Mastodon: @charliermarsh Carl Meyer: @carljm ty on Github: github.com/astral-sh/ty A Very Early Play with Astral's Red Knot Static Type Checker: app.daily.dev Will Red Knot be a drop-in replacement for mypy or pyright?: github.com Hacker News Announcement: news.ycombinator.com Early Explorations of Astral's Red Knot Type Checker: pydevtools.com Astral's Blog: astral.sh Rust Analyzer Salsa Docs: docs.rs Ruff Open Issues (label: red-knot): github.com Ruff Types: types.ruff.rs Ruff Docs (Astral): docs.astral.sh uv Repository: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Biden has been diagnosed with cancer… that he has had for years. How deep does this coverup go? Who has really been running the government? Pope Leo makes some bold moves and Hilary Clinton hates babies. Finally, a Mexican tall ship crashes into the Brooklyn Bridge! All this and more on the LOOPcast!This podcast is sponsored, in part, by the University of Dallas!The University of Dallas MS in Data Science and AI blends technical excellence with human-centered leadership. Designed for working professionals, this program combines hands-on projects in AI, cybersecurity, and analytics with a faith-informed core curriculum. Build skills that matter in real-world settings and graduate ready to lead with purpose. Click https://hubs.ly/Q03fXwV90 to learn more!This podcast is sponsored, in part, by Home Title Lock!Did you know that American Homeowners have over 32 TRILLION DOLLARS in Equity? The best way to protect your equity is with Home Title Lock's exclusive Million Dollar Triple Lock Protection. Go to https://hometitlelock.com/loopcast to save 30% AND you'll also get a free title history report to ensure you're not already a victim. And make sure you check out the Million Dollar Triple Lock Protection details when you get there.0:00 – Welcome back to the LOOPcast!1:15 – University of Dallas2:15 – Biden's Diagnosis25:13 – Home Title Lock26:21 – Pope Leo's First Mass!40:12 – Good News!45:19 – Bombing in CA55:02 – Drinking Coach1:04:48 – Twilight ZoneEMAIL US: loopcast@catholicvote.org SUPPORT LOOPCAST: www.loopcast.orgAll opinions expressed on LOOPcast by the participants are their own and do not necessarily reflect the opinions of CatholicVote.

Topics covered in this episode: pre-commit: install with uv PEP 773: A Python Installation Manager for Windows (Accepted) Changes for Textual The Best Programmers I Know Extras Joke Watch on YouTube About the show Sponsored by NordLayer: pythonbytes.fm/nordlayer Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pre-commit: install with uv Adam Johnson uv tool works great at keeping tools you use on lots of projects up to date quickly, why not use it for pre-commit. The extension of pre-commit-uv will use uv to create virtual environments and install packages fore pre-commit. This speeds up initial pre-commit cache creation. However, Adam is recommending this flavor of using pre-commit because it's just plain easier to install pre-commit and dependencies than the official pre-commit install guide. Win-win. Side note: No Adam, I'm not going to pronounce uv “uhv”, I'll stick with “you vee”, even Astral tells me I'm wrong Michael #2: PEP 773: A Python Installation Manager for Windows (Accepted) via pycoders newsletter One manager to rule them all – PyManager. PEP 773 replaces all existing Windows installers (.exe “traditional” bundle, per-version Windows Store apps, and the separate py.exe launcher) with a single MSIX app called Python Install Manager (nick-named PyManager). PyManager should be mainstream by CPython 3.15, and the traditional installer disappears no earlier than 3.16 (≈ mid-2027). Simple, predictable commands. python → launches “the best” runtime already present or auto-installs the latest CPython if none is found. py → same launcher as today plus management sub-commands: py install, py uninstall, py list, py exec, py help. Optional python3 and python3.x aliases can be enabled by adding one extra PATH entry. Michael #3: Changes for Textual Bittersweet news: the business experiment ends, but the code lives on. Textual began as a hobby project layered on top of Rich, but it has grown into a mature, “makes-the-terminal-do-the-impossible” TUI framework with an active community and standout documentation. Despite Textual's technical success, the team couldn't pinpoint a single pain-point big enough to sustain a business model, so the company will wind down in the coming weeks. The projects themselves aren't going anywhere: they're stable, battle-tested, and will continue under the stewardship of the original author and the broader community. Brian #4: The Best Programmers I Know Matthias Endler “I have met a lot of developers in my life. Lately, I asked myself: “What does it take to be one of the best? What do they all have in common?”” The list Read the reference Know your tools really well Read the error message Break down problems Don't be afraid to get your hands dirty Always help others Write Never stop learning Status doesn't matter Build a reputation Have patience Never blame the computer Don't be afraid to say “I don't know” Don't guess Keep it simple Each topic has a short discussion. So don't just ready the bullet points, check out the article. Extras Brian: I had a great time in Munich last week. I a talk at a company event, met with tons of people, and had a great time. The best part was connecting with people from different divisions working on similar problems. I love the idea of internal conferences to get people to self organize by topic and meet people they wouldn't otherwise, to share ideas. Also got started working on a second book on the plane trip back. Michael: Talk Python Clips (e.g. mullet) Embrace your cloud firewall (example). Python 3.14.0 beta 1 is here Congrats to the new PSF Fellows. Cancelled faster CPython https://bsky.app/profile/snarky.ca/post/3lp5w5j5tws2i Joke: How To Fix Your Computer

What should I do next? A common question, one that seems simple on the surface, but the answer, especially a more optimal answer, can be very difficult to uncover. It may involve information that the asker is not aware of, be unintuitive, or even be counter to our instincts.This month, we discuss a new Klaviyo feature: Next Best Action. Utilizing Klaviyo's knowledge of marketing best practices, we can recommend specific actions that are likely to be highly advantageous

Host Chris Hackett is joined by Victoria Ekstedt, Chief Information Security Officer at Orange Digital Services; Paul de Freene, Founder of NordicDesert AB; Rajeev Kumar Jain, Cyber Engineering Team Leader at IKEA; and Konrad Jelen, VP Data Science & AI at Kolomolo. This panel of experts shares how artificial intelligence is transforming the cybersecurity landscape and reshaping business models. From AI ethics to scalable data infrastructure, this insightful discussion dives deep into the future of secure, intelligent enterprise operations.

with @rhhackett @smc90 @DarenMatsuoka @SamBronerWelcome to web3 with a16z, a show about the next generation of the internet. I'm Robert Hackett.There has been a flurry of stablecoin news lately, so we're doing a special bonus episode to cover everything that's been going on. Sonal and I are joined by a16z crypto's Data Science lead Daren Matsuoka who shares the actual data behind the stablecoin trend. Then we have Sam Broner — who is a Deal Partner here and our frequent author on stablecoins — to analyze the news, and help highlight the signal versus the noise.Here's a selection of the news:USDC issuer Circle filed to go public on the New York Stock ExchangeCoinbase released an agentic payments standard with support for stablecoin paymentsVisa and Mastercard enhanced stablecoin supportStripe announced stablecoin financial account balances, a programmable stablecoin (via Bridge), a stablecoin-backed card, and moreMeta is reportedly in talks to introduce stablecoins as a means for payoutsAnd much moreWe also have one of our regular episodes covering the broader stablecoins trend and big picture, dropping separately in the feed, also with Sam and a16z crypto Founder Chris Dixon, so be sure to check that out next.Timestamps:(0:00) Introduction(1:30) Stablecoin Data Overview(3:55) Stablecoin Adoption and Infrastructure(4:24) Market Share of Issuers and Blockchains(6:10) Stablecoin Growth vs. Crypto Market Cycles(7:45) Stablecoin News and Developments(9:44) Fintech Embraces Stablecoins(12:44) Legacy Payment Systems vs. Stablecoins(17:04) The Future of Stablecoins and Open Networks(22:11) ConclusionLinks to related resources:Everything stablecoins: Big picture, deep dive with Chris Dixon, Sam Broner, and Robert Hackett (a16z crypto, May 2025)A chart of stablecoin usage growth vs. crypto market cyclicality (@DarenMatsuoka on X)The month fintechs embraced stablecoins by Sam Broner (a16z crypto, May 2025)What Stripe's acquisition of Bridge means for fintech and stablecoins by James da Costa and Sam Broner (a16z crypto, April 2025)A guide to stablecoins: What, why, and how by a16z crypto editorial (a16z crypto, April 2025)As a reminder, none of the content should be taken as investment, business, legal, or tax advice; please see a16z.com/disclosures for more important information, including a link to a list of our investments.

Estamos a solo 1 episodio de cerrar la Temporada #12 y ya se acerca nuestro Simposium Anual Datlas Academy este próximo viernes 18 de julio 2025. No podíamos dejar pasar la oportunidad de revivir una de las conferencia más populares que hemos tenido en la historia del Simposium. La Dra Yazmin es Gerente de Analitica Avanzada en Grupo DeAcero y compartió con nosotros algunos de los retos más grandes de hacer ciencia de datos al interior de la organización. Más allá de lo técnico, más allá de lo presupuestal, incluso hablando de personas, de liderazgo, de resistencia al cambio y mucho más¡No te lo puedes perder! Support the show¿Te gusto este episodio? ¿Te interesa adentrarte más al mundo de la analítica? Descubre una experiencia de aprendizaje diseñada para equiparte con las habilidades más demandadas del mercado. En Retos Academy, encontrarás cursos especializados que combinan teoría, práctica y tecnología para que destaques en la era digital. ¿Qué te ofrecemos? Cursos en áreas clave como Storytelling con Power BI, analítica de datos en R y Python y gestión de proyectos. Herramientas exclusivas como la Matriz ILUO para potenciar tus análisis. Acceso a mentorías, talleres y una comunidad de expertos que lideran el cambio en sus industrias. Próxima generación inicia en mayo de 2025.No te quedes fuera de esta experiencia transformadora. Inscríbete hoy en nuestro marketplace y da el primer paso hacia el futuro

In this installment of Sasquatch Tracks, the team presents an update on the well-attended annual Ohio Bigfoot Conference, before taking a deep-dive into one of the most promising new analytical endeavors related to relict hominoid research, The Sasquatch Data Project.  Joining us to discuss this ambitious effort is Terrestrial, the nom de plume of the data scientist behind The Sasquatch Data Project who in the past worked on NASA's Dawn Mission related to the study of the dwarf planet Ceres. Terrestrial has a Bachelor of Science in Earth & Atmospheric Sciences from the Georgia Institute of Technology and is currently pursuing a Blue Ridge Naturalist certification. According to her website, while working as an undergrad at Georgia Tech, she played an integral role in categorizing, identifying, and measuring ground-ice features on Ceres for NASA's Dawn Mission, and first-authored a paper published in JGR: Planets, in addition to co-authoring several papers while working on this mission. Terrestrial tells us her interest in Sasquatch began at the early age of 5 and has only grown since. In 2023 she decided to retire as a professional Twitch gaming live-streamer and devote her time into creating the ultimate data resource to aid in the research of North America's soon-to-be-known great ape, the Sasquatch. Stories and other links discussed in this episode:  The Sasquatch Data Project: Official Website The Sasquatch Data Project on Instagram Song: Summer Night by Pro Tunes Music (Video Link) Connect with Sasquatch Tracks! Get T-shirts, mugs, and more at the Sasquatch Tracks Store on Tee Public. Follow Sasquatch Tracks on Instagram. Follow Sasquatch Tracks on X. Got a news tip or story to share? Send us an Email. Have you seen an animal you can't identify? Submit a report here.

Cassie Kozyrkov created the discipline of decision intelligence, combining the power of AI with data science to enable companies to make better choices. She's now an AI advisor, and she writes the popular Decision Intelligence newsletter. Kozyrkov joined the WorkLab podcast to discuss how leaders can maximize the potential of AI and data, why having too many options is as unhelpful as having too few, and how to avoid falling prey to confirmation bias. WorkLab Subscribe to the WorkLab newsletter  

Looking for powerful AI tools that can dramatically boost your impact, regardless of the size of the businesses you serve? You don't need an enterprise-size budget to transform your work and create massive value for your stakeholders.In this Value Boost episode, Heidi Araya joins Dr Genevieve Hayes to reveal three high-impact, low-cost AI tools that deliver exceptional ROI for both your data science career and for even the most budget-conscious clients.In this episode, you'll uncover:Why Claude consistently outperforms ChatGPT for business applications and how to leverage it as your AI partner for everything from sales coaching to content creation [01:32]How Perplexity delivers real-time research capabilities that save hours of manual work while providing verified sources you can trust [04:02]How Fireflies AI notetaker creates a searchable knowledge base from client conversations that enhances follow-up and project management [07:56]A practical first step to start implementing this maximum-value toolkit in your data science practice tomorrow [09:39]Guest BioHeidi Araya is the CEO and chief AI consultant of BrightLogic, an AI automation agency that specializes in delivering people-first solutions that unlock the potential of small to medium sized businesses. She is also a patented inventor, an international keynote speaker and the author of two upcoming books, one on process improvement for small businesses and the other on career and personal reinvention.LinksConnect with Heidi on LinkedInBrightLogic websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Christopher Watts is currently the senior coordinator of the RECON unit in the NSW Police Force. Prior to this he was the Senior Strength & Conditioning Coach with the Royal Australian Air Force – Human Performance Optimization (HPO) team and other positions include Athlete Development Manager at St Andrews Cathedral School and Strength & Conditioning Coach for Wests Tigers NSW Cup. He has a Masters in Data Science and a Masters in Strength & Conditioning from Edith Cowan University and is an Accredited ASCA Professional L2 S&C Coach. Alongside his civilian career, Chris maintains a part-time military career with the Australian Army Reserves. QUOTES “So my first goal when I arrived here in Wagga was to really make sure that the PTIs understood that I'm not here to do their job, I'm here to actually find ways to highlight the good things that they do” “We try not to use the word sport science or sports physiotherapy or sports nutrition in this tactical domain because there will be people that will automatically turn off and some of the early pushback we'd get is this isn't sport and we're not dealing with athletes, so don't talk to us about sports science. So we change the terminology and most of the time you'll hear us say it's performance science.” “if the PTIs can implement three tailored plyometric regressions and progressions for a group of 60 recruits, if they can do that comfortably with no logistical hiccups, the you can optimize the accuracy of how we are breaking them down into different groups.” “So our job is not just about improving what they do as a recruit, but it's really essentially providing more tools in the toolbox for the aviator. So human performance should be the responsibility of the individual and they (the aviators) should be taking full ownership of their own human performance.” “What we're trying to do is make sure that they're not just fit for service, but fit for life. And when they're finished with their Air Force career, whether that's four years or 25 years later, they have a foundation that can carry them long after their military career” SHOWNOTES 1) Christopher's background and journey in tactical strength & conditioning 2) What it looks like for an initial recruit coming into the Royal Australian Air Force 1RTU and how the performance staff that support the RAAF staff 3) The relationship and interactions between performance staff and physical training instructors (PTIs) 4) The evolution of gold standard and best practice in the RAAF 1RTU physical training 5) Using force plates to make data-driven decisions about training modifications for recruits 6) The role of education in improving RAAF employee's physical performance and “raising the floor” across the RAAF workforce 7) The importance of being able to simplify the complex with S&C PEOPLE MENTIONED Phil Moreland Steve Smith Scott Ryder

Python has many string formatting styles which have been added to the language over the years. Early Python used the % operator to injected formatted values into strings. And we have string.format() which offers several powerful styles. Both were verbose and indirect, so f-strings were added in Python 3.6. But these f-strings lacked security features (think little bobby tables) and they manifested as fully-formed strings to runtime code. Today we talk about the next evolution of Python string formatting for advanced use-cases (SQL, HTML, DSLs, etc): t-strings. We have Paul Everitt, David Peck, and Jim Baker on the show to introduce this upcoming new language feature. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Guests: Paul on X: @paulweveritt Paul on Mastodon: @pauleveritt@fosstodon.org Dave Peck on Github: github.com Jim Baker: github.com PEP 750 – Template Strings: peps.python.org tdom - Placeholder for future library on PyPI using PEP 750 t-strings: github.com PEP 750: Tag Strings For Writing Domain-Specific Languages: discuss.python.org How To Teach This: peps.python.org PEP 501 – General purpose template literal strings: peps.python.org Python's new t-strings: davepeck.org PyFormat: Using % and .format() for great good!: pyformat.info flynt: A tool to automatically convert old string literal formatting to f-strings: github.com Examples of using t-strings as defined in PEP 750: github.com htm.py issue: github.com Exploits of a Mom: xkcd.com pyparsing: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

The crew breaks down Pope Leo XIV's first speech to the cardinals, where he explains his name choice and sets a fiery agenda for tackling the “new revolution” of AI—focusing on human dignity, justice, and the common good. Josh shares his firsthand experience at the Pope's audience with journalists (yes, there were jokes!), while we unpack Leo's Mother's Day homily, his call for peace during the Regina Caeli, and his brother's wild MAGA antics on Newsmax.. Good news? Erika's basically Pope Leo's mom, and Josh celebrates the EPA nixing that annoying car shutdown feature and… does the Pope have to pay taxes?EMAIL US: loopcast@catholicvote.orgSUPPORT LOOPCAST: www.loopcast.orgToday's show is sponsored by:The University of DallasThe University of Dallas MS in Data Science and AI blends technical excellence with human-centered leadership. Designed for working professionals, this program combines hands-on projects in AI, cybersecurity, and analytics with a faith-informed core curriculum. Build skills that matter in real-world settings and graduate ready to lead with purpose. Learn more: https://hubs.ly/Q03fXwV90 Home Title Lock!So, when was the last time you checked on your title?The best way to protect your equity is with Home Title Lock's exclusive Million Dollar Triple Lock Protection. Go to https://hometitlelock.com/loopcast to save 30% AND you'll also get a free title history report to ensure you're not already a victim.All opinions expressed on LOOPcast by the participants are their own and do not necessarily reflect the opinions of CatholicVote.Note: originally the wrong file was uploaded for this episode. It is has been corrected and you are listening to the episode recorded live on 5/12/25!

This is episode 293 recorded on May 6th, 2025 where John & Jason talk the Microsoft Fabric March 2025 Feature Summary including Data Science, Data Warehouse, Real-time Intelligence, and Data Factory.

On this episode of the Scouting For Growth podcast, Sabine VdL talks to Alon Kaufman, CEO and Co-Founder of Duality Technologies, a company that is revolutionising the way organisations collaborate on data while preserving privacy and security. During today’s conversation, we’ll explore the vision behind Duality Technologies, the real-world problems it is solving, and how organisations can future-proof themselves against risks by adopting privacy-preserving technologies. We’ll also dive into Alon’s inspiring journey as a technology leader and his perspective on the ethical and strategic aspects of data collaboration in the age of AI and Big Data. KEY TAKEAWAYS Combining and enhancing data sets is becoming more and more challenging in a world where privacy, security, regulations and data protection are becoming more critical. This is a good thing. What inspires us at Duality is to find a way to allow organisations to unlock the challenges around working together on data in a way that both protects the data and allows you to get the utility out of it. At the source of Duality is: How can we work on data sets without leaking or sharing the data. That’s where homomorphic encryption comes in. This allows us to work on and analyse data while it remains protected or encrypted. Two companies each have a list of customers and they both want to understand how many customers they have in intersection. The way you did this before is for company A to disclose it’s list of customers to company B which does the analysis and fins the intersection or go to a trusted third party. With duality, the two companies can use our software platform to run a computation that comes up with the intersection without either company seeing each other’s data. We all want our governments and law enforcement to be able to do their work, but we don’t want them to pull in every data point that we leave outside. Duality allows law enforcement investigations to run queries and analytics only on data that is allowed and only giving the insights that are needed. Government and healthcare – where data sets are large an sensitive – are big places where Duality has been successful. BEST MOMENTS ‘In order to get the most value out of data, the more you can bring data sets together and enhance them the better off you are.’ ‘Duality’s mission is to run AI data science analytics on data sets that cannot simply be centralised, and doing it where ethe data is while making sure the data isn’t exposed, privacy isn’t leaked or challenges of data localisation and regulation are not violated.’ ‘Companies that already know to work on their own data, and control it, can now go to the next step and do it in a collaborative way.’ ‘Insurance companies need to work together around fraud because the fraudsters utilise the fact different companies don’t talk and will attack one and then the other because they know the level of data shared between them is limited.’ ABOUT THE GUEST Alon Kaufman is the CEO and Co-Founder of Duality Technologies, a pioneering company at the forefront of data encryption and privacy technologies. With over 20 years of experience in technology leadership, Alon has a rich background that spans across Big Data, Data Science, Machine Learning, and Cybersecurity. As a thought leader, Alon frequently speaks on topics related to Big Data, Cybersecurity, and Innovation. He is committed to advancing the conversation around data privacy and security. LinkedIn ABOUT THE HOST Sabine is a corporate strategist turned entrepreneur. She is the CEO and Managing Partner of Alchemy Crew a venture lab that accelerates the curation, validation, & commercialization of new tech business models. Sabine is renowned within the insurance sector for building some of the most renowned tech startup accelerators around the world working with over 30 corporate insurers, accelerated over 100 startup ventures. Sabine is the co-editor of the bestseller The INSURTECH Book, a top 50 Women in Tech, a FinTech and InsurTech Influencer, an investor & multi-award winner. Twitter LinkedIn Instagram Facebook TikTok Email Website

Today's guest is Damion Nero, Head of Data Science at Takeda Pharmaceuticals. With over 15 years of experience applying AI, machine learning, and real-world data to drug development and precision medicine, Damion joins Emerj Managing Editor Matthew DeMello to explore the evolving role of AI in drug development and supply chain management. He breaks down how AI is currently streamlining administrative and regulatory tasks, improving efficiency across clinical trials, and saving valuable time for healthcare professionals. Damion also discusses why broader, transformative supply chain efficiencies are still on the horizon, as AI continues to evolve and scale in the pharmaceutical industry. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on Emerj's flagship ‘AI in Business' podcast!

While most data scientists chase after scraps at the big business table, a hidden gold mine sits completely ignored. Small businesses are desperate for AI solutions but can't get help because everyone thinks they're "too small."The truth? These overlooked clients - representing a staggering 99.8% of all businesses - are willing to pay real money for simple AI implementations that deliver jaw-dropping ROI. We're talking five to seven-figure returns from solutions you could build in your sleep.In this episode, Heidi Araya joins Dr Genevieve Hayes to reveal exactly how data scientists can escape the soul-crushing enterprise world and build a thriving practice serving clients who actually appreciate your genius.Prepare to discover:Why AI implementations for small businesses can deliver dramatically higher ROI than enterprise solutions [12:16]The three pre-built AI solutions that consistently generate the greatest value for resource-constrained clients [12:16]A practical framework for identifying high-impact opportunities even when clients have minimal data [16:59]The "AI receptionist" solution that generated $30 million in new business from dead leads for one small client [21:19]Guest BioHeidi Araya is the CEO and chief AI consultant of BrightLogic, an AI automation agency that specializes in delivering people-first solutions that unlock the potential of small to medium sized businesses. She is also a patented inventor, an international keynote speaker and the author of two upcoming books, one on process improvement for small businesses and the other on career and personal reinvention.LinksConnect with Heidi on LinkedInBrightLogic websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Today's episode is different from all the previous ones, as for the first time on Scaling Theory, we focus on research methodology, exploring how AI is reshaping the very process of doing research and what that shift means for science and society at large.I sat down with James Evans, Professor of Sociology, Computational and Data Science at the University of Chicago, External Professor at the Santa Fe Institute, and Faculty Member at the Complexity Science Hub in Vienna, to explore how AI is transforming the way we simulate, scale, and understand human behavior, and what that shift means for science and society.We dive into his pioneering work on using large language models to simulate individuals, societies, and entire social systems. James and I explore the strengths and limits of AI agents for both the social and hard sciences before reflecting on the future of social science itself. We talk about research centers entirely run by AI and conferences conducted by AI agents, without any human involvement. We also discuss the role of small research teams in disruptive innovation, and how to cultivate proximity and serendipity in a research world where we increasingly cooperate with machines.You can follow me on X (@ProfSchrepel) and BlueSky (@ProfSchrepel) to receive regular updates.References:- Simulating Subjects: The Promise and Peril of AI Stand-ins for Social Agents and Interactions (2025) https://osf.io/preprints/socarxiv/vp3j2_v3- LLM Social Simulations Are a Promising Research Method (2025) https://arxiv.org/pdf/2504.02234- Large teams develop and small teams disrupt science and technology (2019) https://www.nature.com/articles/s41586-019-0941-9?wpisrc=- AI Expands Scientists' Impact but Contracts Science's Focus (2024) https://arxiv.org/abs/2412.07727- The Paradox of Collective Certainty in Science (2024) https://arxiv.org/html/2406.05809v1?utm_source=chatgpt.com- Being Together in Place as a Catalyst for Scientific Advance (Research Policy, 2023) https://www.sciencedirect.com/science/article/pii/S0048733323001956

Visit thedigitalslicepodcast.com for complete show notes of every podcast episode. Join Brad Friedman and Kevin Dean as they discuss the shift in how business is done, AI, and the importance of business owners considering the real value they provide to clients and customers. Kevin J. Dean is leading AI-driven business transformation. As the Founder and CEO of ManoByte, a technology services company specializing in business process automation, CRM, and intelligent automation, Kevin has dedicated his career to helping businesses thrive in the digital age. With over two decades of experience, Kevin's expertise has enabled enterprises to leverage AI for significant growth. Kevin holds a degree in Data Science and Analytics, shaping his data-driven strategies for business optimization. Under his leadership, ManoByte has become a trusted partner for optimizing sales, marketing, and customer service processes, streamlining operations, and enhancing customer experiences. The Digital Slice Podcast is brought to you by Magai. Up your AI game at https://friedmansocialmedia.com/magai

What trends and technologies should you be paying attention to today? Are there hot new database servers you should check out? Or will that just be a flash in the pan? I love these forward looking episodes and this one is super fun. I've put together an amazing panel: Gina Häußge, Ines Montani, Richard Campbell, and Calvin Hendryx-Parker. We dive into the recent Stack Overflow Developer survey results as a sounding board for our thoughts on rising and falling trends in the Python and broader developer space. Episode sponsors NordLayer Auth0 Talk Python Courses Links from the show The Stack Overflow Survey Results: survey.stackoverflow.co/2024 Panelists Gina Häußge: chaos.social/@foosel Ines Montani: ines.io Richard Campbell: about.me/richard.campbell Calvin Hendryx-Parker: github.com/calvinhp Explosion: explosion.ai spaCy: spacy.io OctoPrint: octoprint.org .NET Rocks: dotnetrocks.com Six Feet Up: sixfeetup.com Stack Overflow: stackoverflow.com Python.org: python.org GitHub Copilot: github.com OpenAI ChatGPT: chat.openai.com Claude: anthropic.com LM Studio: lmstudio.ai Hetzner: hetzner.com Docker: docker.com Aider Chat: github.com Goose AI: goose.ai IndyPy: indypy.org OctoPrint Community Forum: community.octoprint.org spaCy GitHub: github.com Hugging Face: huggingface.co Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: pirel: Python release cycle in your terminal FastAPI Cloud Python's new t-strings Extras Joke Watch on YouTube About the show Sponsored by NordLayer: pythonbytes.fm/nordlayer Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: pirel: Python release cycle in your terminal pirel check shows release information about your active Python interpreter. If the active version is end-of-life, the program exits with code 1. If no active Python interpreter is found, the program exits with code 2. pirel list lists all Python releases in a table. Your active Python interpreter is highlighted. A picture is worth many words Brian #2: FastAPI Cloud Sebastián Ramírez, creator of FastAPI, announced today the formation of a new Company, FastAPI Cloud. Here's the announcement blog post: FastAPI Cloud - By The Same Team Behind FastAPI There's a wait list to try it out. Promises to turns deployment into fastapi login; fastapi deploy Side note: announcement includes quote from Daft Punk: Build Harder, Better, Faster, Stronger I just included this in a talk I'm gave last week (and will again next week), where I modify this to “Build Easier, Better, Faster, Stronger” Sebastian and I are both fans of the rocket emoji. BTW, we first covered FastAPI on episode 123 in 2019 Brian #3: Python's new t-strings Dave Peck, one of the authors of PEP 750, which will go into Python 3.14 We covered t-strings in ep 428 In article t-strings security benefits over f-strings How to work with t-strings A Pig Latin example Also, I think I have always done this wrong Is it the first consonant to the end? or the first consonant cluster? So… Brian → Rianbay? or Ianbray? BTW, this is an example of nerdgassing What's next once t-strings ship? On thing that's next (in Python 3.15, maybe, is using t-strings in shlex and subprocess) PEP 787 – Safer subprocess usage using t-strings deferred to 3.15 Michael #4: zev A simple CLI tool to help you remember terminal commands. Examples: # Find running processes zev 'show all running python processes' # File operations zev 'find all .py files modified in the last 24 hours' # System information zev 'show disk usage for current directory' # Network commands zev 'check if google.com is reachable' # Git operations zev 'show uncommitted changes in git' Again, picture worth many words: Extras Brian: Holy Grail turns 50 nerdgassing Michael: Transcripts are a bit better now. Zen is better now Joke: Can my friend come in?

In this episode of HIPcast, Dr. Rebecca Reynolds shares how the HI Profession is a Health Data Science field and how K-12 educators are seeking data science fields for their students. The conversation referenced a white paper about data science you can find here https://www.nsta.org/nstas-official-positions/data-science.  #HIPcast with Shannan and Seth.HIPcast brought to you by Enterprise Social RecordShow Sponsor:SolarityTravis Atchley – Tatchley@solarityhealth.comLearn more at https://solarityhealth.com/

How to make the most competitive AI systems? Renée Cummings, a renowned Professor of Practice in Data Science at the University of Virginia and a leading voice in AI ethics, talks about the importance of trust, transparency, and accountability in AI development, making a strong case for why these elements are essential for businesses aiming to leverage AI effectively. Furthermore, Renée shares her expertise on the intersection of AI and data justice, where AI is applied in high-stakes decision-making process.

The World Federation of Pediatric Intensive & Critical Care Societies (WFPICCS), in collaboration with OPENPediatrics, recognizes World PICU Awareness Week 2025. This initiative aims to raise global awareness about the importance of PICUs and critical care wards worldwide, emphasizing how healthcare professionals in these units, regardless of resource setting, are driving change. Discover how innovation is reshaping pediatric intensive care across Latin America. From telemedicine and AI to regional data networks, leading experts share practical solutions and powerful success stories. HOST Manuel Enrique Soriano Aguilar, MD Pediatric Critical Care Medicine StarMédica Hospital Infantil Privado & Centro Médico ABC Past President of the Mexican Society of Pediatric Critical Care SLACIP (Latin-American Association of Pediatric Critical Care) Representative to the World Federation of Pediatric & Critical Care Societies (WFPICCS) Mexico GUESTS Emmanuelle Dexeus Gabriel Fernández Vera, Paediatric Critical Care Medicine/Big Data in Health Intensive Care Hospital General de Acapulco RENEO and PALS instructor, SLACIP social media spokesperson Mexico Ledis Maria Izquierdo Borrero, MD Paediatrician specialising in Critical Care Medicine and Paediatric Intensive Care. Master in Biomedical Engineering Chief UCIP Hospital Militar Central Associate Professor Universidad Militar Nueva Granada Colombia Alexandra Jimenez Chaves, MD Specialist in Paediatric Intensive Care Mg(c) Artificial Intelligence Support Services Coordinator at Colsubsidio Children's Clinic Teacher Colegio Mayor Nuestra Señora del Rosario Advanced Life Support in Paediatrics Instructor Founding Member Aipocrates (Think Tank Innovation in Health) Colombia Maria del Pilar Arias, MD Specialist in Paediatric Intensive Care - Master in Clinical Effectiveness - Master in Data Science and Knowledge Management Staff physician Intermediate Care Unit. Ricardo Gutierrez Children's Hospital Buenos Aires. Argentina Coordinator of the SATI-Q Quality Benchmarking Program (Argentinean Society of Intensive Care). DATE Initial publication date: May 4, 2025. TRANSCRIPTS English - https://cdn.bfldr.com/D6LGWP8S/as/89x7t8rcm3rbv32vnkxvjh6/WPAW-25_LATAM_Final_English Spanish - https://cdn.bfldr.com/D6LGWP8S/at/px9wtttsw8q68w2h68r8qcpx/WPAW-25_LATAM_Final_Spanish.pdf French - https://cdn.bfldr.com/D6LGWP8S/at/2pn95f94vc9vm9p633zq59/WPAW-25_LATAM_Final_French.pdf Portuguese - https://cdn.bfldr.com/D6LGWP8S/at/z6v47859hk7tpn8vzkvkrbm/WPAW-25_LATAM_Final_Portuguese.pdf Italian - https://cdn.bfldr.com/D6LGWP8S/at/gpqnjkkpth8g7ps84gfgkhr/WPAW-25_LATAM_Final_Italian.pdf German - https://cdn.bfldr.com/D6LGWP8S/at/76n5cr66m6cq9474hwr986tq/WPAW-25_LATAM_Final_German.pdf Arabic - https://cdn.bfldr.com/D6LGWP8S/at/785vh6cqkrsrr8mxf9xwtgkx/WPAW-25_LATAM_Final_Arabic.pdf Please visit: www.openpediatrics.org OPENPediatrics™ is an interactive digital learning platform for healthcare clinicians sponsored by Boston Children's Hospital and in collaboration with the World Federation of Pediatric Intensive and Critical Care Societies. It is designed to promote the exchange of knowledge between healthcare providers around the world caring for critically ill children in all resource settings. The content includes internationally recognized experts teaching the full range of topics on the care of critically ill children. All content is peer-reviewed and open-access, thus at no expense to the user. For further information on how to enroll, please email: openpediatrics@childrens.harvard.edu Please note: OPENPediatrics does not support or control any related videos in the sidebar; these are placed by YouTube. We apologize for any inconvenience this may cause.

Eric Sheng, Bain's Global Lead of Data Science and Machine Learning Engineering Group, shares how his passion for technology led him to leading AI innovation across industries. From early roles at HP and NVIDIA to shaping strategic solutions at Bain, he reflects on the power of technical depth, curiosity, and real-world impact.

In this episode of R Weekly Highlights we hear from industry experts on how they choose a programming language for their projects, a big boost to the use of copilot for building your next Shiny app, and the learning journey of a new R user.Episode LinksThis week's curator: Sam Parmar - @parmsam@fosstodon.org (Mastodon) & @parmsam_ (X/Twitter)Which programming language should I use? A guide for early-career researchersReliable Shiny Code with Copilot and Posit's VS Code ExtensionMy Journey Learning R as a Humanities UndergradEntire issue available at rweekly.org/2025-W18Supplement ResourcesR for Data Science 2nd Edition https://r4ds.hadley.nz/Data Science Learning Community https://dslc.io/IssueTrackeR https://tanguybarthelemy.github.io/IssueTrackeR 3MW (Scalable reporting with Quarto) https://3mw.albert-rapp.de/p/scalable-reporting-with-quartoSupporting the showUse the contact page at https://serve.podhome.fm/custompage/r-weekly-highlights/contact to send us your feedbackR-Weekly Highlights on the Podcastindex.org - You can send a boost into the show directly in the Podcast Index. First, top-up with Alby, and then head over to the R-Weekly Highlights podcast entry on the index.A new way to think about value: https://value4value.infoGet in touch with us on social media Eric Nantz: @rpodcast@podcastindex.social (Mastodon), @rpodcast.bsky.social (BlueSky) and @theRcast (X/Twitter)Mike Thomas: @mike_thomas@fosstodon.org (Mastodon), @mike-thomas.bsky.social (BlueSky), and @mike_ketchbrook (X/Twitter) Music credits powered by OCRemixSecrets Abound - Final Fantasy - Midgarian Sky - https://ocremix.org/remix/OCR02452Voodoo, Roots 'n Grog - The Secret of Monkey Island - Alex Jones, Diggi Dis - https://ocremix.org/remix/OCR02180

In this episode Gudrun speaks with Nadja Klein and Moussa Kassem Sbeyti who work at the Scientific Computing Center (SCC) at KIT in Karlsruhe. Since August 2024, Nadja has been professor at KIT leading the research group Methods for Big Data (MBD) there. She is an Emmy Noether Research Group Leader, and a member of AcademiaNet, and Die Junge Akademie, among others. In 2025, Nadja was awarded the Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award (ELA). The COPSS ELA recognizes early career statistical scientists who show evidence of and potential for leadership and who will help shape and strengthen the field. She finished her doctoral studies in Mathematics at the Universität Göttingen before conducting a postdoc at the University of Melbourne as a Feodor-Lynen fellow by the Alexander von Humboldt Foundation. Afterwards she was a Professor for Statistics and Data Science at the Humboldt-Universität zu Berlin before joining KIT. Moussa joined Nadja's lab as an associated member in 2023 and later as a postdoctoral researcher in 2024. He pursued a PhD at the TU Berlin while working as an AI Research Scientist at the Continental AI Lab in Berlin. His research primarily focuses on deep learning, developing uncertainty-based automated labeling methods for 2D object detection in autonomous driving. Prior to this, Moussa earned his M.Sc. in Mechatronics Engineering from the TU Darmstadt in 2021. The research of Nadja and Moussa is at the intersection of statistics and machine learning. In Nadja's MBD Lab the research spans theoretical analysis, method development and real-world applications. One of their key focuses is Bayesian methods, which allow to incorporate prior knowledge, quantify uncertainties, and bring insights to the “black boxes” of machine learning. By fusing the precision and reliability of Bayesian statistics with the adaptability of machine and deep learning, these methods aim to leverage the best of both worlds. The KIT offers a strong research environment, making it an ideal place to continue their work. They bring new expertise that can be leveraged in various applications and on the other hand Helmholtz offers a great platform in that respect to explore new application areas. For example Moussa decided to join the group at KIT as part of the Helmholtz Pilot Program Core-Informatics at KIT (KiKIT), which is an initiative focused on advancing fundamental research in informatics within the Helmholtz Association. Vision models typically depend on large volumes of labeled data, but collecting and labeling this data is both expensive and prone to errors. During his PhD, his research centered on data-efficient learning using uncertainty-based automated labeling techniques. That means estimating and using the uncertainty of models to select the helpful data samples to train the models to label the rest themselves. Now, within KiKIT, his work has evolved to include knowledge-based approaches in multi-task models, eg. detection and depth estimation — with the broader goal of enabling the development and deployment of reliable, accurate vision systems in real-world applications. Statistics and data science are fascinating fields, offering a wide variety of methods and applications that constantly lead to new insights. Within this domain, Bayesian methods are especially compelling, as they enable the quantification of uncertainty and the incorporation of prior knowledge. These capabilities contribute to making machine learning models more data-efficient, interpretable, and robust, which are essential qualities in safety-critical domains such as autonomous driving and personalized medicine. Nadja is also enthusiastic about the interdisciplinarity of the subject — repeatedly changing the focus from mathematics to economics to statistics to computer science. The combination of theoretical fundamentals and practical applications makes statistics an agile and important field of research in data science. From a deep learning perspective, the focus is on making models both more efficient and more reliable when dealing with large-scale data and complex dependencies. One way to do this is by reducing the need for extensive labeled data. They also work on developing self-aware models that can recognize when they're unsure and even reject their own predictions when necessary. Additionally, they explore model pruning techniques to improve computational efficiency, and specialize in Bayesian deep learning, allowing machine learning models to better handle uncertainty and complex dependencies. Beyond the methods themselves, they also contribute by publishing datasets that help push the development of next-generation, state-of-the-art models. The learning methods are applied across different domains such as object detection, depth estimation, semantic segmentation, and trajectory prediction — especially in the context of autonomous driving and agricultural applications. As deep learning technologies continue to evolve, they're also expanding into new application areas such as medical imaging. Unlike traditional deep learning, Bayesian deep learning provides uncertainty estimates alongside predictions, allowing for more principled decision-making and reducing catastrophic failures in safety-critical application. It has had a growing impact in several real-world domains where uncertainty really matters. Bayesian learning incorporates prior knowledge and updates beliefs as new data comes in, rather than relying purely on data-driven optimization. In healthcare, for example, Bayesian models help quantify uncertainty in medical diagnoses, which supports more risk-aware treatment decisions and can ultimately lead to better patient outcomes. In autonomous vehicles, Bayesian models play a key role in improving safety. By recognizing when the system is uncertain, they help capture edge cases more effectively, reduce false positives and negatives in object detection, and navigate complex, dynamic environments — like bad weather or unexpected road conditions — more reliably. In finance, Bayesian deep learning enhances both risk assessment and fraud detection by allowing the system to assess how confident it is in its predictions. That added layer of information supports more informed decision-making and helps reduce costly errors. Across all these areas, the key advantage is the ability to move beyond just accuracy and incorporate trust and reliability into AI systems. Bayesian methods are traditionally more expensive, but modern approximations (e.g., variational inference or last layer inference) make them feasible. Computational costs depend on the problem — sometimes Bayesian models require fewer data points to achieve better performance. The trade-off is between interpretability and computational efficiency, but hardware improvements are helping bridge this gap. Their research on uncertainty-based automated labeling is designed to make models not just safer and more reliable, but also more efficient. By reducing the need for extensive manual labeling, one improves the overall quality of the dataset while cutting down on human effort and potential labeling errors. Importantly, by selecting informative samples, the model learns from better data — which means it can reach higher performance with fewer training examples. This leads to faster training and better generalization without sacrificing accuracy. They also focus on developing lightweight uncertainty estimation techniques that are computationally efficient, so these benefits don't come with heavy resource demands. In short, this approach helps build models that are more robust, more adaptive to new data, and significantly more efficient to train and deploy — which is critical for real-world systems where both accuracy and speed matter. Statisticians and deep learning researchers often use distinct methodologies, vocabulary and frameworks, making communication and collaboration challenging. Unfortunately, there is a lack of Interdisciplinary education: Traditional academic programs rarely integrate both fields. It is necessary to foster joint programs, workshops, and cross-disciplinary training can help bridge this gap. From Moussa's experience coming through an industrial PhD, he has seen how many industry settings tend to prioritize short-term gains — favoring quick wins in deep learning over deeper, more fundamental improvements. To overcome this, we need to build long-term research partnerships between academia and industry — ones that allow for foundational work to evolve alongside practical applications. That kind of collaboration can drive more sustainable, impactful innovation in the long run, something we do at methods for big data. Looking ahead, one of the major directions for deep learning in the next five to ten years is the shift toward trustworthy AI. We're already seeing growing attention on making models more explainable, fair, and robust — especially as AI systems are being deployed in critical areas like healthcare, mobility, and finance. The group also expect to see more hybrid models — combining deep learning with Bayesian methods, physics-based models, or symbolic reasoning. These approaches can help bridge the gap between raw performance and interpretability, and often lead to more data-efficient solutions. Another big trend is the rise of uncertainty-aware AI. As AI moves into more high-risk, real-world applications, it becomes essential that systems understand and communicate their own confidence. This is where uncertainty modeling will play a key role — helping to make AI not just more powerful, but also more safe and reliable. The lecture "Advanced Bayesian Data Analysis" covers fundamental concepts in Bayesian statistics, including parametric and non-parametric regression, computational techniques such as MCMC and variational inference, and Bayesian priors for handling high-dimensional data. Additionally, the lecturers offer a Research Seminar on Selected Topics in Statistical Learning and Data Science. The workgroup offers a variety of Master's thesis topics at the intersection of statistics and deep learning, focusing on Bayesian modeling, uncertainty quantification, and high-dimensional methods. Current topics include predictive information criteria for Bayesian models and uncertainty quantification in deep learning. Topics span theoretical, methodological, computational and applied projects. Students interested in rigorous theoretical and applied research are encouraged to explore our available projects and contact us for further details. The general advice of Nadja and Moussa for everybody interested to enter the field is: "Develop a strong foundation in statistical and mathematical principles, rather than focusing solely on the latest trends. Gain expertise in both theory and practical applications, as real-world impact requires a balance of both. Be open to interdisciplinary collaboration. Some of the most exciting and meaningful innovations happen at the intersection of fields — whether that's statistics and deep learning, or AI and domain-specific areas like medicine or mobility. So don't be afraid to step outside your comfort zone, ask questions across disciplines, and look for ways to connect different perspectives. That's often where real breakthroughs happen. With every new challenge comes an opportunity to innovate, and that's what keeps this work exciting. We're always pushing for more robust, efficient, and trustworthy AI. And we're also growing — so if you're a motivated researcher interested in this space, we'd love to hear from you." Literature and further information Webpage of the group G. Nuti, Lluis A.J. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arxiv Jan 2019 Wikipedia: Expected value of sample information C. Howson & P. Urbach: Scientific Reasoning: The Bayesian Approach (3rd ed.). Open Court Publishing Company. ISBN 978-0-8126-9578-6, 2005. A.Gelman e.a.: Bayesian Data Analysis Third Edition. Chapman and Hall/CRC. ISBN 978-1-4398-4095-5, 2013. Yu, Angela: Introduction to Bayesian Decision Theory cogsci.ucsd.edu, 2013. Devin Soni: Introduction to Bayesian Networks, 2015. G. Nuti, L. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arXiv:1901.03214 stat.ML, 2019. M. Carlan, T. Kneib and N. Klein: Bayesian conditional transformation models, Journal of the American Statistical Association, 119(546):1360-1373, 2024. N. Klein: Distributional regression for data analysis , Annual Review of Statistics and Its Application, 11:321-346, 2024 C.Hoffmann and N.Klein: Marginally calibrated response distributions for end-to-end learning in autonomous driving, Annals of Applied Statistics, 17(2):1740-1763, 2023 Kassem Sbeyti, M., Karg, M., Wirth, C., Klein, N., & Albayrak, S. (2024, September). Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection. In Uncertainty in Artificial Intelligence (pp. 1890-1900). PMLR. M. K. Sbeyti, N. Klein, A. Nowzad, F. Sivrikaya and S. Albayrak: Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection pdf. To appear in Transactions on Machine Learning Research, 2025 Podcasts Learning, Teaching, and Building in the Age of AI Ep 42 of Vanishing Gradient, Jan 2025. O. Beige, G. Thäter: Risikoentscheidungsprozesse, Gespräch im Modellansatz Podcast, Folge 193, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2019.

Mike & Tommy are joined on an episode with Ginger Grant as we conclude our series on Fabric & Data Science - does AI, Fabric, and fast moving technology change what organizations need in a data scientist? Get in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Seth: https://www.linkedin.com/in/seth-bauer/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Presenting: The 2025 Energy Central Innovation Awards, a moment to celebrate forward-thinkers, risk-takers, and the big ideas kind of people who are building the future of the energy industry. We're in a moment of unprecedented change, but one constant remains: smart people who think about where we are *and* where we're going are giving us a shot at a better future. And this year's winners—chosen from a record number of Energy Central community nominations for our annual Innovation Awards—are exactly those kind of people. Hear from… Yuval Lubowich, CTO and co-founder of HData (HData.com) Stephanie Gossman, Manager of Grid Transformation for Georgia Power (https://www.southerncompany.com/innovation.html) Pranav Gupta, Manager of Data Science at ICF David Sypnieski, CEO and Founder of Athena Intelligence And get ready to think big. Thanks for listening, and we hope you enjoy.

  In this episode, Kathryn speaks with Liz Teran, Chief Marketing Officer at Owlet Baby Care, discussing how to market to a rapidly changing addressable market with very short buying cycles. Liz shares her insights into leveraging both quantitative and qualitative data, as well as how they tweak their messaging to accommodate for different mindsets throughout the buyers' pregnancy journey. Guest Quote: “Owlet has one of the largest infant health data sets in the world. so this is really exciting. We're just getting started, but there's so many opportunities for us to use that information to, you know, empower parents further to just delight them in their product experience. For example, things we're talking about but haven't implemented yet, might be like, ‘Oh, we can see that your baby woke several times last night. It looks like you had a rough night, mom. You know, here's a Starbucks gift card on us. Have a good day.' You know, so it's not just the product pieces of like, oh, your child might be getting sick, or this, you know, key health metric has changed for them, but also just provide them a delightful brand experience as well. And I think that's really magical.”Episode Breakdown: [03:51] Alchemy Unveiled: Take the path less traveledDigging into the data often leads you down a path that differs from industry trends or traditional approaches. Make sure to leverage scrappy, intuitive data to truly understand buyer needs.  [11:09] From Nuggets to Campaign Gold: Nothing beats word-of-mouthWhen Owlet learned that 50 percent of their customers first heard of them through a friend or family member, they changed their strategy, leaning into parent stories over features. [20:02] Gold Rush! Gain Inspiration As a Consumer Sometimes, the best inspiration and way to get a pulse on the marketing industry, is to pay attention to what grabs you as as a consumer. Pull inspiration from other disciplines and keep your marketing hat on as you move through the world (and in the internet).  Links & Resources:Connect with KathrynConnect with LizLearn more about DeluxeLearn more about Owlet Baby Care

Would you believe that sharing a conversation in the lunch room could be more valuable to your data science career than spending countless hours behind a computer, perfecting algorithms and models? It's a radical idea, but it's exactly the kind of thinking that transforms good data scientists into exceptional ones.In this Value Boost episode, AI strategist Gregory Lewandowski joins Dr Genevieve Hayes to explain his controversial 90-10 rule: that success in AI and data science is 90% about people and only 10% about technology - and shares a surprisingly simple way to put this principle into practice.You'll learn:Why focusing purely on technology creates a dangerous blind spot [01:53]The critical success factor that most data science teams overlook [03:54]The "toasted sandwich strategy" for building crucial relationships [05:54]Guest BioGregory Lewandowski is the Chief AI Strategist and Founder of GLEW, a consultancy focussing on the business side of AI ROI.LinksConnect with Gregory on LinkedInGLEW Services websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Adatépítész -az első magyar datapodcast Minden ami hír, érdekesség, esemény vagy tudásmorzsa az  adat, datascience, adatbányászat és hasonló kockaságok világából. Become a Patron! Fehérházi susmus KamuAI HBR kutatás

In this week's episode Greg and Patrick explore the extremely clever yet inexplicably underused method of dominance analysis which offers a set of techniques for determining the relative importance of predictors in a regression model. Along the way they also discuss giving compliments, looking tired, Indy vs. F1, chicken paprikas, Gustav Holst, Fozzie Bear, not paying attention while recording, Lewis Hamilton pin-ups, Lando Calrissian, equation forts, being appallingly cool, making no sense at all, and magnums of champagne. Stay in contact with Quantitude! Web page: quantitudepod.org TwitterX: @quantitudepod YouTube: @quantitudepod Merch: redbubble.com

Dr Benjamin Pope is an Associate Professor in Statistical Data Science at Macquarie University at the Astrophysics and Space Technologies Research Centre. Since being awarded a DPhil in Astrophysics from the University of Oxford, Ben has been a NASA Sagan Postdoctoral Fellow and an ARC Decra Fellow among other accomplishments. Ben is active in public science communication and other meaningful civil society engagement. This is a great introduction to concepts related to Astrostatistics and Astrophysics and data science, This is a great introduction to concepts related to Astrostatistics and Astrophysics and data science, and Ben's approach of combining flexible statistical models with rigorous physical models. There are discussions of astronomical imaging, philosophy of science debates in statistical inference reasoning, and some history of radio astronomy in Australia including the pioneering work of Ruby Payne-Scott.

Fluent Fiction - Japanese: Unraveling Tokyo's Algorithm: The Ethics of Innovation Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ja/episode/2025-04-29-22-34-01-ja Story Transcript:Ja: 東京の春、日差しがちょうど良い温もりを届ける頃、東京スタートアップ・インキュベーターはアイデア溢れる若者たちで賑わっていました。En: In the spring of Tokyo, when the sunlight delivers just the right warmth, the Tokyo Startup Incubator was bustling with young people brimming with ideas.Ja: ハルトはその中で、成功心に駆られ働く野心的な起業家でした。En: Among them was Haruto, an ambitious entrepreneur driven by the desire for success.Ja: 彼の後ろの席には、優れたデータサイエンティストのユキが座っていました。En: Seated behind him was Yuki, an excellent data scientist.Ja: ユキは新しいアルゴリズムを作り上げた人物で、その正確さには驚かざるを得ないものでした。En: She was the creator of a new algorithm, whose accuracy was astonishing.Ja: ミカは、このアルゴリズムに興味津々でした。En: Mika was deeply intrigued by this algorithm.Ja: 皆、彼女がどれほどこの技術の可能性に期待しているか知っていました。En: Everyone knew just how much she anticipated the potential of this technology.Ja: しかし、ハルトには何か不安がありました。En: However, Haruto had some concerns.Ja: このアルゴリズムには何か秘密が隠されているのではないかと疑ったのです。En: He suspected that there might be some hidden secret within this algorithm.Ja: ある日、ハルトはミカと話していました。En: One day, Haruto was speaking with Mika.Ja: ミカ、このアルゴリズム、何かおかしいと思わないか？とハルトは問いかけました。En: Mika, don't you think there's something strange about this algorithm? Haruto inquired.Ja: ミカは笑いながら答えました。 でも、すごいじゃない。どうしてこんなに正確なんだろう？En: With a laugh, Mika replied, But it's amazing, isn't it? How can it be so accurate?Ja: ハルトの表情は変わりませんでした。En: Haruto's expression remained unchanged.Ja: 僕には、気になる部分がある。En: There's something that's bothering me, he said.Ja: ユキと親しくなる決心したハルトは、彼女にアプローチを始めました。En: Haruto decided to get closer to Yuki and began to approach her.Ja: ゆっくりと話をするうちに、ユキは心を開きました。En: As they slowly started talking, Yuki opened up.Ja: 彼女はやはり何か知っているようでした。En: She indeed seemed to know something.Ja: ある日の朝、彼女はハルトに秘密を打ち明けました。実は、このアルゴリズムには欠点があるの。En: One morning, she confided in Haruto, Actually, there is a flaw in this algorithm.Ja: その欠点を知ったハルトとミカは驚愕しました。En: Learning about this flaw left Haruto and Mika astonished.Ja: アルゴリズムは人の行動を予測するだけではなく、操作できる可能性があったのです。En: The algorithm not only predicted human behavior but also had the potential to manipulate it.Ja: ハルトはこれを見過ごすことができませんでした。En: Haruto couldn't overlook this.Ja: 彼は上司たちにこの事実を知らせるべきだと思いました。En: He believed he should inform the higher-ups about this fact.Ja: そして、ゴールデンウィークの終わりを迎える前の日、重要な会議が開かれました。En: Then, on the day before the end of Golden Week, an important meeting was held.Ja: ハルトは勇気を出し、ゆっくりとしかし確信を持って事実を述べました。 このアルゴリズムをリリースする前に、倫理的な考慮が必要です。En: With courage, Haruto slowly but confidently stated the facts, We need ethical considerations before releasing this algorithm.Ja: その場には緊張が漂いましたが、彼の言葉は響き、上司たちはリリースを一時中止することに同意しました。En: Tension filled the room, but his words resonated, and the superiors agreed to temporarily halt the release.Ja: この出来事を通じて、ハルトは技術の倫理的責任の重要性を学びました。En: Through this incident, Haruto learned the importance of ethical responsibility in technology.Ja: 彼にとっての成功は、ただの財政的な成功ではなく、社会に良い影響を与えることが含まれるものであると理解したのです。En: He realized that for him, success wasn't merely financial but also involved making a positive impact on society.Ja: 優しい春の風が東京の街を吹き渡る中、ハルトの心にも新しい決意が芽生え始めました。En: As the gentle spring breeze blew through the streets of Tokyo, a new determination began to sprout in Haruto's heart. Vocabulary Words:spring: 春incubator: インキュベーターentrepreneur: 起業家astonishing: 驚かざるを得ないintrigued: 興味津々algorithm: アルゴリズムanticipate: 期待concerns: 不安suspect: 疑うconfided: 打ち明けましたflaw: 欠点astonished: 驚愕manipulate: 操作できるoverlook: 見過ごすhigher-ups: 上司ethical: 倫理的considerations: 考慮tension: 緊張resonate: 響きtemporarily: 一時halt: 中止responsibility: 責任impact: 影響positive: 良いdetermination: 決意delivers: 届けるbustling: 賑わっていましたdata scientist: データサイエンティストcreator: 作り上げた人物potential: 可能性

Pandas is at a the core of virtually all data science done in Python, that is virtually all data science. Since it's beginning, Pandas has been based upon numpy. But changes are afoot to update those internals and you can now optionally use PyArrow. PyArrow comes with a ton of benefits including it's columnar format which makes answering analytical questions faster, support for a range of high performance file formats, inter-machine data streaming, faster file IO and more. Reuven Lerner is here to give us the low-down on the PyArrow revolution. Episode sponsors NordLayer Auth0 Talk Python Courses Links from the show Reuven: github.com/reuven Apache Arrow: github.com Parquet: parquet.apache.org Feather format: arrow.apache.org Python Workout Book: manning.com Pandas Workout Book: manning.com Pandas: pandas.pydata.org PyArrow CSV docs: arrow.apache.org Future string inference in Pandas: pandas.pydata.org Pandas NA/nullable dtypes: pandas.pydata.org Pandas `.iloc` indexing: pandas.pydata.org DuckDB: duckdb.org Pandas user guide: pandas.pydata.org Pandas GitHub issues: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: pip 25.1 has dependency groups, pylock.toml, plus more aiohttp goes free threaded uv 0.6.15 supports pylock.toml Whenever Extras Joke Watch on YouTube About the show Sponsored by Porkbun! Use our link pythonbytes.fm/porkbun and get a .app or .dev domain for $5.99 at Porkbun. Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pip 25.1 has dependency groups, pylock.toml, plus more post What's new in pip 25.1 - Dependency groups! Richard Si Discovered this through Hugo van Kemenade Dependency groups, PEP 735, supported # pyproject.toml [dependency-groups] test = ["pytest", "pytest-xdist"] lint = ["mypy", "isort"] # Dependency Groups can include other groups! ✨ dev = [ {include-group = "test"}, {include-group = "lint"} ] Package installation progress bar Resumable downloads Experimental lockfile generation, PEP 751, with pip lock so cool pip index versions is stable, no longer experimental use this to get a list of available versions ex: python3 -m pip index versions pytest-check combine with --json to get a nice script readable output Michael #2: aiohttp goes free threaded Thanks to months of consistent contributions by Lysandros Nikolaou, all of the mandatory dependencies of #aiohttp now ship free-threaded variants of #wheels! This unlocks the same in aiohttp! Brian #3: uv 0.6.15 supports pylock.toml Discovered through Brett Cannon So far, these projects support pylock.toml pip pip-audit pdm uv With uv To export a uv.lock to the pylock.toml format, run: uv export -o pylock.toml To generate a pylock.toml file from a set of requirements, run: uv pip compile -o pylock.toml -r requirements.in To install from a pylock.toml file, run: uv pip sync pylock.toml or uv pip install -r pylock.toml Michael #4: Whenever via Pat Decker Typed and DST-safe datetimes for Python, available in Rust or pure Python. Whenever helps you write correct and type checked datetime code. It's also way faster than other third-party libraries—and usually the standard library as well. Extras Brian: Every UUID Michael: New Vulnerability in GitHub Copilot and Cursor: How Hackers Can Weaponize Code Agents via Brian Skinn And typosquatting in the AI age Firefox Send alternatives file.pizza via @rafaelwo bitwarden send Joke: Can you Vibe? Interview with Vibe Coder in 2025 Senior Engineer tries Vibe Coding

Unser heutiger Gast hat nach dem Abitur Psychologie und Betriebswirtschaft studiert – zuerst an der eufom Business School, später an der FernUni Schweiz, wo sie ihren Master in Psychologie abschloss. Ihre anfängliche Begeisterung für Statistik und Data Science führte sie in ein junges KI-Unternehmen, das sich mit Machine Learning in der Luftfahrt beschäftigte. Dabei entdeckte sie das Themenfeld Predictive Maintenance: die Möglichkeit, Maschinenausfälle durch frühzeitige Prognosen zu verhindern. Aus dieser Faszination heraus entstand ihr eigenes Start-up, das bereits kurz nach seiner Gründung inmitten der Corona-Zeit die ersten Kunden gewinnen konnte. Heute führt sie als CEO ein interdisziplinäres Team, das einen digitalen Wartungsassistenten entwickelt hat: Dieser erkennt Anomalien in Maschinen, bevor teure Stillstände auftreten. Engagiert ist sie zudem als Landessprecherin im Startup-Verband, als Plenumsmitglied der Handelskammer Hamburg und als Botschafterin für junge Talente bei proTechnicale. Ihr Antrieb: KI nicht nur als Technologie, sondern als Chance begreifen, Arbeitsprozesse klüger und menschlicher zu gestalten – weil sie fest daran glaubt, dass die Psychologie hinter Big Data und Algorithmen eine entscheidende Rolle spielt. Seit fast acht Jahren beschäftigen wir uns in diesem Podcast mit der Frage, wie Arbeit den Menschen stärkt, statt ihn zu schwächen. In über 480 Gesprächen haben wir mit fast 600 Persönlichkeiten darüber gesprochen, was sich für sie geändert hat und was sich weiter ändern muss. Wie gelingt es, ein KI-Produkt zu entwickeln, das nicht nur die Technik, sondern auch die menschlichen Faktoren in der Industrie berücksichtigt? Welche Rolle spielt Psychologie, wenn es darum geht, in vermeintlichen „Männerbranchen“ das eigene Unternehmen zu gründen und zu führen? Und wie kann KI tatsächlich „New Work“ befördern, indem sie Routinearbeit abnimmt und Raum für menschliche Kreativität schafft? Fest steht: „New Work“ wird heute oft verzwergt – viele denken dabei an bunte Sitzsäcke, flache Hierarchien und Obstkörbe. Ursprünglich jedoch wurde der New-Work-Gedanke in einer Krise geboren, als neuer Weg zwischen Kapitalismus und Sozialismus, um den Menschen Raum zur Entfaltung zu geben und die Chancen neuer Technologien für echte Problemlösungen zu nutzen. Genau deshalb suchen wir weiter nach Methoden, Vorbildern, Erfahrungen, Tools und Ideen, die uns dem Kern von New Work näher bringen. Darüber hinaus beschäftigt uns von Anfang an die Frage, ob wirklich alle Menschen das finden und leben können, was sie im Innersten wirklich, wirklich wollen. Ihr seid bei „On the Way to New Work“ – heute mit Lena Weirauch. [Hier](https://linktr.ee/onthewaytonewwork) findet ihr alle Links zum Podcast und unseren aktuellen Werbepartnern

Georgia Technology Summit brings together 1000+ Georgia-focused technologists to network, learn, and engage with the latest trends in Georgia innovation. This year's summit was held at the Woodruff Arts Center, a stunning and iconic cultural landmark located in the heart of Midtown Atlanta. Dr. Beverly Wright serves as VP of Data Science & AI at Wavicle […] The post Georgia Technology Summit 2025: Beverly Wright with Wavicle Data Solutions appeared first on Business RadioX ®.

If you want to succeed in data science, you need to create business value. But what does business value actually mean to the executives with the power to make or break your data science initiative?In this episode, AI strategist Gregory Lewandowski joins Dr Genevieve Hayes to share the five executive priorities he discovered while leading analytics for major enterprises - and explain why the future belongs to data scientists who understand them.This episode reveals:The two priorities that can unlock budget even mid-cycle (and why cost savings isn't one of them) [07:50]How executive priorities evolve across technology adoption cycles [10:16]Why misaligned compensation metrics doom data science projects [13:03]The "follow the money" framework for understanding what drives business decisions [12:22]Guest BioGregory Lewandowski is the Chief AI Strategist and Founder of GLEW, a consultancy focussing on the business side of AI ROI.LinksConnect with Gregory on LinkedInGLEW Services websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Do you or your company need accounting software? Well, there are plenty of SaaS products out there that you can give your data to. but maybe you also really like Django and would rather have a foundation to build your own accounting system exactly as you need for your company or your product. On this episode, we're diving into Django Ledger, created by Miguel Sanda, which can do just that. Episode sponsors Auth0 Talk Python Courses Links from the show Miguel Sanda on Twitter: @elarroba Miguel on Mastodon: @elarroba@fosstodon.org Miguel on GitHub: github.com Django Ledger on Github: github.com Django Ledger Discord: discord.gg Get Started with Django MongoDB Backend: mongodb.com Wagtail CMS: wagtail.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: Huly CVE Foundation formed to take over CVE program from MITRE drawdb 14 Advanced Python Features Extras Joke Watch on YouTube About the show Sponsored by Posit Workbench: pythonbytes.fm/workbench Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Huly All-in-One Project Management Platform (alternative to Linear, Jira, Slack, Notion, Motion) If you're primarily interested in self-hosting Huly without the intention to modify or contribute to its development, please use huly-selfhost. Manage your tasks efficiently with Huly's bidirectional GitHub synchronization. Use Huly as an advanced front-end for GitHub Issues and GitHub Projects. Connect every element of your workflow to build a dynamic knowledge base. Everything you need for productive team work: Team Planner • Project Management • Virtual Office • Chat • Documents • Inbox Self hosting as a service: elest.io Brian #2: CVE Foundation formed to take over CVE program from MITRE Back story: CVE, global source of cybersecurity info, was hours from being cut by DHS The 25-year-old CVE program, an essential part of global cybersecurity, is cited in nearly any discussion or response to a computer security issue. CVE was at real risk of closure after its contract was set to expire on April 16. The nonprofit MITRE runs CVE on a contract with the DHS. A letter last Tuesday sent Tuesday by Yosry Barsoum, vice president of MITRE, gave notice of the potential halt to operations. Another possible victim of the current administration. CVE Foundation Launched to Secure the Future of the CVE Program CVE Board members have spent the past year developing a strategy to transition CVE to a dedicated, non-profit foundation. The new CVE Foundation will focus solely on continuing the mission of delivering high-quality vulnerability identification and maintaining the integrity and availability of CVE data for defenders worldwide. Over the coming days, the Foundation will release more information about its structure, transition planning, and opportunities for involvement from the broader community. Michael #3: drawdb Free and open source, simple, and intuitive database design editor, data-modeler, and SQL generator. Great drag-drop relationship manager Define your DB visually, export as SQL create scripts Or import existing SQL to kickstart the diagramming. Brian #4: 14 Advanced Python Features Edward Li Picking some favorites 1. Typing Overloads 2. Keyword-only and Positional-only Arguments 9. Python Nitpicks For-else statements Walrus operator Short Circuit Evaluation Operator Chaining Extras Michael: Thunderbird send / other firefox things. Joke: Python Tariffs Thanks wagenrace Thanks Campfire Tales

Russ D'Argento, CEO and founder of FINTRX, sits down with CJ in this episode to discuss building a Data as a Service (DaaS) business while also sharing insight into the highly lucrative world of family offices. He explains what a family office is (name a billionaire, they probably have one), their roles in wealth management, and how he identified this niche. He sheds light on FINTRX, what it does, and why building a verticalized data business for a niche market can be both exceptionally complicated and rewarding. He delves into the nuances of DaaS business models, what makes them different from SaaS, why it's so important to own your own data, and the unique challenges one faces in a business like this. He also talks bout the fine art of workflow integration and how allowing customers to access FINTRX's data in other APIs has been surprisingly beneficial.If you're looking for an ERP, head to NetSuite: https://netsuite.com/metrics and get the CFO's Guide to AI and Machine Learning.—SPONSORS:Rippling Spend is a spend management solution that handles your entire company's spending in one unified system. It enables you to bring your corporate cards, expense management, bill pay, and more into one place to achieve real-time visibility and uniquely granular control with automated policy controls across every type of spend. Get a demo to see how much time your org would save at rippling.com/metrics.Vanta's trust management platform takes the manual work out of your security and compliance process and replaces it with continuous automation. Over 9000 businesses use it to automate compliance needs across over 35 frameworks like SOC 2 and ISO 27001. Centralize security workflows, complete questionnaires up to five times faster, and proactively manage vendor risk. For a limited time, get $1,000 off Vanta at vanta.com/metrics.Tropic is an intelligent spend management solution that consolidates your spend data and processes into one unified offering, enabling insights and decisive action. From spotting hidden optimization opportunities to automating painful procurement workflows and giving you the best market data to turn vendor negotiations in your favor, Tropic combines smart insights with real human expertise to keep you ahead of the curve. Visit tropicapp.io/mostlymetrics to learn how.NetSuite provides financial software for all your business needs. More than 40,000 companies have already upgraded to NetSuite, gaining visibility and control over their financials, inventory, HR, eCommerce, and more. If you're looking for an ERP platform, head to NetSuite https://netsuite.com/metrics and get the CFO's Guide to AI and Machine Learning.Planful is a financial performance management platform designed to streamline financial tasks for businesses. It helps with budgeting, closing the books, and financial reporting, all on a cloud-based platform. By improving the efficiency and accuracy of these processes, Planful allows businesses to make better financial decisions. Find out more at www.planful.com/metrics.Subscript is a modern billing and revenue recognition platform designed for SaaS finance teams that need flexibility and accuracy. From automated invoicing and dunning to compliant, transparent revenue recognition and real-time analytics, Subscript eliminates manual work, reduces errors, and gives you a single source of truth for all your financial data. Book a free demo at subscript.com.—FOLLOW US ON X:@cjgustafson222 (CJ)—TIMESTAMPS:(00:00) Preview and Intro(02:57) Sponsor – Rippling Spend | Vanta | Tropic(07:23) What Is a Family Office(11:49) Why Family Offices Are an Attractive Place To Grow Your Career(14:06) Examples of Family and Multi-Family Offices(18:04) Sponsor – NetSuite | Planful | Subscript(21:13) The Founding Story of FINTRX(23:38) What FINTRX Does(25:02) The Data as a Service Business Model(28:50) The Challenges of Starting a DaaS Business(34:07) Broad Versus Narrow in DaaS(37:57) Building FINTRX's Ideal Customer Profile(42:20) Sourcing Your Own Data Versus Partnering With Others(46:09) Hiring for Engineering and Data Science(51:07) Using FINTRX's Interface Versus Accessing the Data in Another Tool(1:02:26) Advice for Someone Launching a DaaS Business(1:02:02) A Habit That Russ Swears By as a Founder(1:03:24) A Lesson From Something That Went Awry Get full access to Mostly metrics at www.mostlymetrics.com/subscribe

In this episode of the Research 2 Reps Roundtable, hosts Dr. Pat Ivy, Dr. Ted, and Dr. Ernie Reimer dive deep into the world of sports science with special guest Felix Proessl, Senior Director of Sport and Data Science for the Colorado Rapids. Felix shares his journey from collegiate soccer player in Germany to leading sport science initiatives in Major League Soccer, blending his academic background with real-world applications. The conversation explores the balance between acute needs and long-term performance goals, as Felix reflects on how his research and data-driven decisions are shaping the training and recovery strategies for professional athletes. The roundtable also tackles the complex challenges of altitude, travel logistics, and recovery in the MLS. Felix discusses the impact of travel on performance, particularly when teams face the daunting task of adjusting to different time zones and environmental factors. The group also delves into the intersection of science and superstition, with Felix explaining how they navigate players' personal routines and beliefs while maintaining a data-informed approach. This episode offers a fascinating look at the evolving role of sports science in professional soccer, emphasizing the importance of collaboration, flexibility, and thoughtful decision-making in optimizing athlete performance. A big thanks to show sponsors, Sorinex and EliteForm, for making these episodes possible.

In this week's episode Patrick and Greg have some serious fun with song lyrics they misunderstood at some point in their personal lives. They then use this as a thinly veiled excuse to explore some very basic statistical things that they have also misunderstood at some point in their professional lives. Along the way they discuss over-engineered front ends, mumbling, Scaramouche, mondegreens, Tony Danza, Bingo Jed, word salad, containers, sitting next to Kurt Cobain, kicking cats, tiddles, ears ringing, the Dunder Chief, wrinkles in the space time continuum, naked or not, missing data bouncer, colite gas, and dying on the dance floor. Stay in contact with Quantitude! Web page: quantitudepod.org TwitterX: @quantitudepod YouTube: @quantitudepod Merch: redbubble.com

Have you ever spent an afternoon wrestling with a Jupyter notebook, hoping that you ran the cells in just the right order, only to realize your outputs were completely out of sync? Today's guest has a fresh take on solving that exact problem. Akshay Agrawal is here to introduce Marimo, a reactive Python notebook that ensures your code and outputs always stay in lockstep. And that's just the start! We'll also dig into Akshay's background at Google Brain and Stanford, what it's like to work on the cutting edge of AI, and how Marimo is uniting the best of data science exploration and real software engineering. Episode sponsors Worth Search Talk Python Courses Links from the show Akshay Agrawal: akshayagrawal.com YouTube: youtube.com Source: github.com Docs: marimo.io Marimo: marimo.io Discord: marimo.io WASM playground: marimo.new Experimental generate notebooks with AI: marimo.app Pluto.jl: plutojl.org Observable JS: observablehq.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy