Podcasts about Data science

Sharon Ayalon is the co-founder and CEO of UrbanMix, a next-gen platform using AI and 3D to streamline real estate operations. An architect by training, she previously taught at Columbia GSAPP and led advanced housing simulations at Cornell Tech. Sharon pioneered Roosevelt Island's Digital Twin and XR transit experience. Her Ph.D. was awarded the President of Israel's Grant for Scientific Excellence. This is episode was recorded live at Blueprint Vegas 2025. Sharon has been helping shape Gowanus Wharf, a groundbreaking Brooklyn development led by Charney Companies turning a former Superfund site into over 1,000 apartments, parks, and public waterfront. It's one of the most ambitious examples of how environmental cleanup, zoning reform, and innovative tools can unlock transformative urban development.

Ever wonder what it takes to level up your career in data science? Senior Data Scientist Darya Petrashka joins Ned and Kyler to share her personal journey from management and linguistics into data science, the real difference between a junior and a senior role, and helps us get under the “data science umbrella” to see... Read more »

Ever wonder what it takes to level up your career in data science? Senior Data Scientist Darya Petrashka joins Ned and Kyler to share her personal journey from management and linguistics into data science, the real difference between a junior and a senior role, and helps us get under the “data science umbrella” to see... Read more »

This episode features a panel of CNA's own Gayatri Gopavajhala and Lizzy Schneider, data scientists, discussing their paths to being in their roles as data scientists at CNA. Guest Biographies Gayatri Gopavajhala is a Data Analyst in CNA's Data Science for Sustainment Program. Lizzy Schneider is a Research Analyst in CNA's Data Science for Production Program.     

In this episode of "Alter Everything," we sit down with Andrew Merrill, Alteryx product specialist and advocate, to explore best practices for integrating AI and LLMs into data analytics processes. Some topics we discuss include proven design patterns for generative AI, such as feedback loops, routing, and RAG architectures, and learn how to avoid common pitfalls like token overuse and data governance challenges. Andrew shares real-world use cases, tips for leveraging Alteryx Co-pilot, and strategies for prompt engineering to maximize workflow efficiency. Panelists: Andrew Merrill, Alteryx Consultant - @CoG, LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Alteryx Gen AI ToolsAlteryx Co-pilotAlteryx Inspire Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music.

Ever wonder what it takes to level up your career in data science? Senior Data Scientist Darya Petrashka joins Ned and Kyler to share her personal journey from management and linguistics into data science, the real difference between a junior and a senior role, and helps us get under the “data science umbrella” to see... Read more »

Today's guest is Nick Masca, Head of Data Science for Growth & Personalisation at Marks and Spencer. Marks and Spencer plc is a prominent British multinational retailer headquartered in London, England, known for offering a wide range of clothing, beauty items, home goods, and food products. Nick joins us on the program to surmise his views on the data-driven challenges currently facing the retail and eCommerce sectors. With a focus on change management rather than traditional digital transformation, Nick outlines the key obstacles retail leaders encounter when leveraging data tools to optimize processes like price setting, supply chain efficiency, and customer experience. He shares insights on the friction that arises when introducing automation, particularly in areas like content development, and how data teams can work closely with stakeholders to ensure seamless implementation. If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

In the fifth episode our special edition of the Ampersand Podcast, Mark Trodden, Dean of Penn Arts & Sciences and Thomas S. Gates, Jr. Professor of Physics & Astronomy, speaks with Bhuvnesh Jain, Walter H. and Leonore C. Annenberg Professor in the Natural Sciences and the Co-Director of the Penn Data Driven Discovery Initiative and the Penn Center for Particle Cosmology. The two discuss how advances in data science and artificial intelligence are transforming cosmology, teaching, and interdisciplinary research at Penn.  

Topics covered in this episode: Possibility of a new website for Django aiosqlitepool deptry browsr Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters 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: Possibility of a new website for Django Current Django site: djangoproject.com Adam Hill's in progress redesign idea: django-homepage.adamghill.com Commentary in the Want to work on a homepage site redesign? discussion Michael #2: aiosqlitepool

How do you prepare your Python data science projects for production? What are the essential tools and techniques to make your code reproducible, organized, and testable? This week on the show, Khuyen Tran from CodeCut discusses her new book, "Production Ready Data Science."

In this episode, we welcome Tim Gagnon (VP of Analytics and Data Science) and Mark Albrecht (VP of AI and Enterprise Strategy) from C.H. Robinson to discuss how AI is transforming the freight industry. We explore the evolution of AI—from traditional methods like machine learning and robotic process automation, to generative AI, and now Agentic AI, which can plan, use tools, learn from past mistakes, and act like a true assistant.C.H. Robinson's unique approach, “Lean AI,” aligns operational discipline with AI priorities, focusing on high-leverage initiatives while keeping customers and carriers at the center. They also limit AI agency in critical areas like order building to maintain reliability and low failure rates.This episode is a deep dive into how AI is fundamentally rewiring logistics and a must-listen for anyone interested in the state-of-the-practice AI in freight transportation. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Synopsis: Nimbus Therapeutics CEO Abbas Kazimi walks Alok Tayi through the company's evolving pipeline and playbook for choosing the right risks in a noisy biotech environment. From Werner helicase for MSI-high cancers to a highly selective SIK2 program and GLP-1–adjacent strategies focused on body composition, Abbas details how Nimbus balances rigor, speed, and capital efficiency. He shares candid lessons from pausing and later resurrecting AMPK beta in partnership with Eli Lilly, the decision to remain modality-agnostic but small-molecule-centric, and the importance of knowing when not to chase the latest fad. Throughout, Abbas returns to a consistent theme: success at Nimbus comes from disciplined target selection, deep collaboration, and a culture that empowers teams to make hard calls in service of patients rather than headlines. Biography: Abbas Kazimi is the Chief Executive Officer of Nimbus Therapeutics. Previously, he served as Chief Business Officer, leading the company's strategic and corporate development efforts while overseeing business operations. Since joining Nimbus in 2014, he has helped raise over $630 million in equity financing and led transactions totaling more than $8 billion. Notably, Mr. Kazimi spearheaded the $6 billion sale of Nimbus's TYK2 program to Takeda, the $1.2 billion sale of its NASH (ACC) program to Gilead, and multiple licensing deals exceeding $1.5 billion with partners such as Genentech, Celgene/Roche, and Eli Lilly. Under his leadership, Nimbus has advanced four programs into the clinic, returned over $4 billion to investors, and continues to expand its computational drug discovery and clinical development capabilities. In 2025, Mr. Kazimi joined the board of Unnatural Products (UNP), a biotech company pioneering orally delivered macrocyclic peptides to tackle previously undruggable targets. He also serves on the Editorial Advisory Board for In Vivo magazine, a leading publication offering strategic insights and analysis of the pharmaceutical, biotechnology, medtech, and consumer health industries. Along with his family, he established the Kazimi Family Endowment for Data Science in Oncology at MD Anderson Cancer Center. This endowment reflects their personal commitment to philanthropy and their vision for revolutionizing cancer treatment through data-driven innovation. At the core of Mr. Kazimi's leadership is a deep sense of purpose—one that seeks to change the trajectory of medical diagnoses where options are limited. The ability to give patients, prescribers, and families a new outlook on life is a powerful responsibility—and one he knows the biopharmaceutical sector has the ability to fulfill. Before Nimbus, he was at Extera Partners, LLC (formerly PureTech Development, LLC), where he provided strategic advisory, supported fundraising, and executed numerous business development transactions. Earlier in his career, he was with JSB-Partners, LP, a specialized investment banking and advisory firm serving biotech and pharmaceutical companies. Mr. Kazimi holds a B.A. from the University of Texas at Austin and an M.S. from Harvard University.

#HRhelpdesk #IndiaHRGuide #MandeepSingh with 25 years of HR expertise, Mandeep Singh explains what data science really means and how it impacts HR. He describes data science as a way to analyze and interpret data for better decisions, uncovering insights that aren't obvious at first glance. HR holds vast employee data, demographics, compensation, performance, and more, and Mandeep shows how data science can predict attrition, identify patterns, and reveal key factors influencing retention. By using predictive models, organizations can achieve over 90% accuracy in forecasting outcomes and correlations. Mandeep emphasizes that data science enables credible, measurable decisions that directly improve talent attraction, retention, and overall HR effectiveness.

This week we talk about Bolt graphics, a new graphics start up that is working on a graphics card with upgrade-able memory for a competitive price. A software developer has released an open source alternative firmware for the Nest v1 and v2 thermostats, and of course your questions! -- During The Show -- 00:45 Intro Dakota's weather 01:51 Email! - William Listener recommends porkbun (https://porkbun.com/) Mailbox.org (https://mailbox.org/en/) Simple Login (https://simplelogin.io/) DuckDuckGo Email Alias (https://duckduckgo.com/duckduckgo-help-pages/email-protection/duck-addresses) Proton's built in features Reasons for protecting privacy Privacy.com (https://www.privacy.com/) 11:25 Data Science & Self Hosting - Annon Value based approach Consenting data collection Tool vs Pillar A job can be a tool Get a job in a different field hadoop Nextcloud Anti Virus Network Protection Medical Science Red Hat data sciences 22:15 News Wire LXQT 2.3 - lxqt-project.org (https://lxqt-project.org/release/2025/11/05/release-lxqt-2-3-0) Calibre 8.14 - newwin.net (https://www.neowin.net/software/calibre-8140) MKVtoolnix - mkvtoolnix.download (https://mkvtoolnix.download/windows/releases/96.0) Curly COMrades - darkreading.com (https://www.darkreading.com/endpoint-security/pro-russian-hackers-linux-vms-hide-windows) Porteux 2.4 - distrowatch.com (https://distrowatch.com/?newsid=12628) MXLinux 25 - mxlinux.org (https://mxlinux.org/blog/mx-25-infinity-isos-now-available) Devuan 6.0 - serverhost.com (https://serverhost.com/blog/devuan-gnu-linux-6-0-launches-a-systemd-free-distro-based-on-debian-13-trixie) IncusOS - phoronix.com (https://www.phoronix.com/news/Incus-IncusOS-Announced) NVIDIA AI Stack - hpcwire.com (https://www.hpcwire.com/off-the-wire/ciq-integrates-full-nvidia-ai-stack-into-rocky-linux-for-faster-deployment-and-scaling) Omnilingual ASR - venturebeat.com (https://venturebeat.com/ai/meta-returns-to-open-source-ai-with-omnilingual-asr-models-that-can) Kimi K2 Thinking - venturebeat.com (https://venturebeat.com/ai/moonshots-kimi-k2-thinking-emerges-as-leading-open-source-ai-outperforming) Drax - prnewswire.com (https://www.prnewswire.com/news-releases/aiola-unveils-drax-an-open-source-speech-model-with-state-of-the-art-accuracy-and-up-to-5O-faster-than-models-from-direct-competitors-302607278.html) Linux Desktop 5% - webpronews.com (https://www.webpronews.com/linux-breaks-5-desktop-share-in-u-s-signaling-open-source-surge-against-windows-and-macos) 23:50 Jill Mueller - Bolt Graphics Bolt Graphics Key differences Build of the card Why did you come to Ubuntu Summit? 27:25 Antonio Salvemini - Ray Tracing Bolt Graphics Ray Tracing vs Raster Why start with FPGA? Next steps Current Stage Available 2027 Bolt Graphics (https://bolt.graphics/) Untapped markets Upgradeable GPU 34:34 No Longer Evil Thermostat Google EOLs Nest Thermostats Gen 1 and Gen 2 No Longer Evil (https://nolongerevil.com/) FULU Foundation (https://fulu.org/our-story) "Feature Complete" AXIS A1001 Good piece of hardware Reservations Connects to "No Longer Evil Server" Server planned to be open sourced Centralite Perl HA Thermostat (Zigbee) (https://www.amazon.com/Centralite-Thermostat-Xfinity-Comcast-Centrallite/dp/B01LXD3EBN) Needs to be repaired 1 or 2 times a year Temperature Sensors Govee H5075 (https://us.govee.com/products/govee-bluetooth-hygrometer-thermometer-h5075) IoT Maintenance Why temperature sensors ESP32 receive blue-tooth and send to Home Assistant -- The Extra Credit Section -- For links to the articles and material referenced in this week's episode check out this week's page from our podcast dashboard! This Episode's Podcast Dashboard (http://podcast.asknoahshow.com/466) Phone Systems for Ask Noah provided by Voxtelesys (http://www.voxtelesys.com/asknoah) Join us in our dedicated chatroom #GeekLab:linuxdelta.com on Matrix (https://element.linuxdelta.com/#/room/#geeklab:linuxdelta.com) -- Stay In Touch -- Find all the resources for this show on the Ask Noah Dashboard Ask Noah Dashboard (http://www.asknoahshow.com) Need more help than a radio show can offer? Altispeed provides commercial IT services and they're excited to offer you a great deal for listening to the Ask Noah Show. Call today and ask about the discount for listeners of the Ask Noah Show! Altispeed Technologies (http://www.altispeed.com/) Contact Noah live [at] asknoahshow.com -- Twitter -- Noah - Kernellinux (https://twitter.com/kernellinux) Ask Noah Show (https://twitter.com/asknoahshow) Altispeed Technologies (https://twitter.com/altispeed) Special Guests: Antonio Salvemini and Jill Mueller.

Paula Helm articulates an AI vision that goes beyond base performance to include epistemic justice and cultural diversity by focusing on speakers and not language alone. Paula and Kimberly discuss ethics as a science; language as a core element of culture; going beyond superficial diversity; epistemic justice and valuing other's knowledge; the translation fallacy; indigenous languages as oral goods; centering speakers and communities; linguistic autonomy and economic participation; the Māori view on data ownership; the role of data subjects; enabling cultural understanding, self-determination and expression; the limits of synthetic data; ethical issues as power asymmetries; and reflecting on what AI mirrors back to us.  Paula Helm is an Assistant Professor of Empirical Ethics and Data Science at the University of Amsterdam. Her work sits at the intersection of STS, Media Studies and Ethics. In 2022 Paula was recognized as one of the 100 Most Brilliant Women in AI-Ethics.Related ResourcesGenerating Reality and Silencing Debate: Synthetic Data as Discursive Device (paper) https://journals.sagepub.com/doi/full/10.1177/20539517241249447Diversity and Language Technology (paper): https://link.springer.com/article/10.1007/s10676-023-09742-6A transcript of this episode is here.   

There was once a time, when data science was still in its infancy, when demonstrating any attempt to learn Python or machine learning was enough to secure a job interview. The demand for data scientists massively outweighed supply. Ten years later, however, the job market has dramatically shifted - and many data scientists who unexpectedly find themselves out of work face a truly overwhelming experience.In this episode, Sarah Burnett joins Dr. Genevieve Hayes to share how she transformed redundancy from a senior banking role into the launch of her own successful data consultancy, proving that unexpected job loss doesn't have to mean career disaster.In this episode, we explore:Why redundancy is a numbers game, not personal failure [03:54]The power of taking time to process after job loss, instead of rushing back [08:47]How to pivot when your first business idea doesn't work [16:58]Why building side projects and community involvement create career insurance [20:52]Guest BioSarah Burnett is the co-founder of Dub Dub Data, a consultancy that offers human-centric AI and Tableau solutions. She transitioned into independent consulting after navigating redundancy from a senior role at a major bank. She is also the co-host of the podcast unDubbed.LinksConnect with Sarah on LinkedInDub Dub Data WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Swarnendu De dives into frameworks like the SaaS Product Success Strategy™ and TechBlueprint Architecture Framework™, and shares insights on turning product ideas into execution-ready roadmaps, designing modular and scalable systems, and creating AI-powered business automation.Key Highlights:Building Scalable SaaS & AI Products: Learn Swarnendu's approach to designing modular, cloud-native systems that meet investor expectations and real-world demands.AI for Business Automation: Discover how AI can be integrated where it drives real value—automating workflows, improving decision-making, and enhancing product experiences.Mentoring & Leadership: Insights into coaching tech leaders and founders, aligning technology with business goals, and accelerating team performance.From Vision to Execution: Explore Swarnendu's process for taking a concept through rapid workshops, validation, and execution-ready roadmaps.

Topics covered in this episode: httptap 10 Smart Performance Hacks For Faster Python Code FastRTC Explore Python dependencies with pipdeptree and uv pip tree Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters 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: httptap Rich-powered CLI that breaks each HTTP request into DNS, connect, TLS, wait, and transfer phases with waterfall timelines, compact summaries, or metrics-only output. Features Phase-by-phase timing – precise measurements built from httpcore trace hooks (with sane fallbacks when metal-level data is unavailable). All HTTP methods – GET, POST, PUT, PATCH, DELETE, HEAD, OPTIONS with request body support. Request body support – send JSON, XML, or any data inline or from file with automatic Content-Type detection. IPv4/IPv6 aware – the resolver and TLS inspector report both the address and its family. TLS insights – certificate CN, expiry countdown, cipher suite, and protocol version are captured automatically. Multiple output modes – rich waterfall view, compact single-line summaries, or -metrics-only for scripting. JSON export – persist full step data (including redirect chains) for later processing. Extensible – clean Protocol interfaces for DNS, TLS, timing, visualization, and export so you can plug in custom behavior. Example: Brian #2: 10 Smart Performance Hacks For Faster Python Code Dido Grigorov A few from the list Use math functions instead of operators Avoid exception handling in hot loops Use itertools for combinatorial operations - huge speedup Use bisect for sorted list operations - huge speedup Michael #3: FastRTC The Real-Time Communication Library for Python: Turn any python function into a real-time audio and video stream over WebRTC or WebSockets. Features

This week, we're introducing Tangent listeners to another podcast in the commercial real estate ecosystem: In The Loop from LoopNet. How can workplaces endure ecologically, financially, and socially?In this episode of In the Loop, Jordan Goldstein, co-CEO of Gensler, explores the future of sustainable and resilient office design. From global design trends and innovative materials to the role of AI in commercial real estate, Jordan shares how the industry is rethinking sustainability. You'll learn why repositioning existing buildings matters, how policy and incentives drive real change, and what it takes to create workplaces built for tomorrow.In the Loop is LoopNet's commercial real estate podcast highlighting the people, ideas, and innovations shaping the future of work. For bonus video content, check out our YouTube channel.Timestamps00:00 - Welcome to the show and introduction of In The Loop podcast drop 01:01 - Phil Hazelhurst welcomes guest Jordan Goldstein, Co-CEO of Gensler 03:34 - How sustainability, resiliency, and “flight to quality” are reshaping office real estate 06:57 - The rise of AI in architecture & how Gensler uses tech to design smarter, lower-carbon spaces 13:38 - Materials makeover: mass timber, low-carbon concrete, and the future of building systems 19:37 - Global perspectives and the power of repositioning old buildings instead of starting new 26:24 - Gensler's own electrified office and how the firm lives its sustainability mission in practice 37:42 - Final thoughts: Why design is an act of optimism and where sustainable real estate goes next40:19 - Edward recaps conversationLinks & references: • View spaces designed by Gensler • Ready to find your next sustainable commercial space? Start your search• Learn more about this episode• Learn more about Commercial Real Estate"

Send us a textOn this episode of The Get Ready Money Podcast, I spoke with Lily Vittayarukskul, co-founder of Waterlily, and Sumedha Rai, AI strategist, about how AI is reshaping financial planning—from enhancing advisor-client relationships to helping protect consumers from financial scams.We explored how advisors can integrate AI thoughtfully, while keeping empathy and human connection at the center of every conversation.

Today we're digging into the Model Context Protocol, or MCP. Think LSP for AI: build a small Python service once and your tools and data show up across editors and agents like VS Code, Claude Code, and more. My guest, Den Delimarsky from Microsoft, helps build this space and will keep us honest about what's solid versus what's just shiny. We'll keep it practical: transports that actually work, guardrails you can trust, and a tiny server you could ship this week. By the end, you'll have a clear mental model and a path to plug Python into the internet of agents. Episode sponsors Sentry AI Monitoring, Code TALKPYTHON NordStellar Talk Python Courses Links from the show Den Delimarsky: den.dev Agentic AI Programming for Python Course: training.talkpython.fm Model Context Protocol: modelcontextprotocol.io Model Context Protocol Specification (2025-03-26): modelcontextprotocol.io MCP Python Package (PyPI): pypi.org Awesome MCP Servers (punkpeye) GitHub Repo: github.com Visual Studio Code Docs: Copilot MCP Servers: code.visualstudio.com GitHub MCP Server (GitHub repo): github.com GitHub Blog: Meet the GitHub MCP Registry: github.blog MultiViewer App: multiviewer.app GitHub Blog: Spec-driven development with AI (open source toolkit): github.blog Model Context Protocol Registry (GitHub): github.com mcp (GitHub organization): github.com Tailscale: tailscale.com Watch this episode on YouTube: youtube.com Episode #527 deep-dive: talkpython.fm/527 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Software development is undergoing rapid change as AI, DevOps, and data science reshape how teams build and scale products. In this interview, Particle41 CEO and technical co-founder Ben Johnson explains what modern software teams must do to stay competitive. He shares practical insights on boosting developer productivity with AI, building reliable systems through infrastructure-as-code, and adopting modern data architectures that move beyond simple dashboards. Ben also discusses how to lead remote teams effectively and apply OKRs in both work and personal life to stay aligned on what truly matters. If you're involved in building software or leading technical teams, this conversation offers clear, actionable strategies for thriving in the AI-driven era.Check out the full series of “Career Sessions, Career Lessons” podcasts here or visit pathwise.io/podcast/. A full written transcript of this episode is also available at https://pathwise.io/podcasts/ben-johnson.Become a PathWise member today! Join at https://pathwise.io/join-now/

What, so what, now what? That's the heart of data science.In this episode, I'm joined by Sai Popuri, a data science expert who explains the three pillars of analytics:Descriptive – What happened?Predictive – So what's likely to happen next?Prescriptive – Now what should we do about it?We also talk about how internal auditors can play a powerful role in turning data into better business decisions.

In this episode, Aimee Altemus and Adelaide Frimpong speak with Dr. Gary Miller of Columbia University, a widely recognized leader in exposome research. Dr. Miller discusses how the exposome—the full spectrum of environmental exposures throughout a lifetime—is transforming our approach to human and environmental health. He highlights how exposomics, data science, and toxicology contribute to Next Generation Risk Assessment strategies to more accurately predict disease risk and guide public health strategies. From innovative technologies to ethical implications, this conversation offers a compelling look at the future of health science in a complex and rapidly changing world. 

"A gente tem IA, tem machine learning dentro, tem deep learning dentro e dentro de deep learning tem a IA generativa. A quantidade de opções é tão grande que você precisa ter o mínimo de conhecimento de vários deles para saber os pontos fracos e fortes" No décimo segundo episódio do Hipsters.Talks, PAULO SILVEIRA , CVO do Grupo Alun, conversa com FELIPE TEODORO , diretor de Data Science da Kogui, sobre como escolher a ferramenta certa para cada problema: desde estatística básica até redes neurais complexas. Uma conversa que desmistifica o universo da IA e mostra quando usar (e quando NÃO USAR) cada técnica.  Prepare-se para um episódio cheio de conhecimento e inspiração! Espero que aproveitem :) Sinta-se à vontade para compartilhar suas perguntas e comentários. Vamos adorar conversar com vocês!

When most data scientists think about their competitive edge, they focus solely on what goes on their resume - education, work experience, and technical skills. But what if the things that truly make you irreplaceable go far deeper than your LinkedIn profile? Your family background, cultural influences, communication quirks, and even the hobbies that make you nerd out all contribute to what makes you uniquely valuable.In this Value Boost episode, Danny Ruspandini joins Dr. Genevieve Hayes to explore the concept of your "untouchable advantage" - the unique combination of experiences and qualities that make you impossible to replace as a data scientist.You'll discover:Why your untouchable advantage extends far beyond your technical qualifications [02:09]How family influences and personal quirks become professional superpowers [04:14]Why introverts have unique advantages they often don't recognize [10:36]The simple way to uncover your own untouchable advantage starting tomorrow [14:08]Guest BioDanny Ruspandini is a brand strategist, business coach and director of Impact Labs Australia. He is also the creator of One Shiny Object, a program for helping solo creatives package what they do into sellable, fixed-price services.LinksConnect with Danny on LinkedInDownload the One Shiny Object frameworkConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

In this episode, we dive deep into the real-world state of AI adoption across industries, unpacking the findings of a major new survey with Gabriel Krummenacher. We explore which AI use cases actually move the needle—hint: operational efficiency tops the list—and why simply chasing the latest GenAI trends won't deliver results without a solid data science foundation. You'll hear our honest takes on what separates hype from impact, why ethical frameworks and governance drive innovation (not stifle it), and how US and European companies are taking very different paths with AI. If you're wondering where AI is truly transforming business—and what it really takes to succeed—this episode is for you.

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! I wouldn't try to become a data analyst next here. Here's 4 reasons why and what I'd do instead.

This episode of the Scope of Things features an exclusive panel at SCOPE Europe 2025 covering regulatory requirements for AI literacy training, featuring industry executives Jonathan Crowther, head of the operational design center at Merck KGaA; Janie Hansen, global development information management, business systems transformation at Daiichi Sankyo; Francis Kendall, head of statistical programming, digital and data sciences at Biogen; and James Weatherall, vice president and chief data scientist of biopharmaceuticals R&D at AstraZeneca. Plus, host Deborah Borfitz gives the latest news on efforts to reduce excess data collection in studies, whole genome sequencing of breast cancer, a virus cocktail to combat superbugs, and more. Show Notes News Roundup   Collaborative study on data collection in trials News posted on the TransCelerate website Heart benefits of semaglutide Study in The Lancet Whole genome sequencing of breast cancers Study in The Lancet Oncology Pan-cancer immunotherapy heads to trials Research article in Cell Article in Bio-IT World Promising NAD+ “youth molecule” Review article in Nature Aging  Virus cocktail to combat superbugs Article in Nature Microbiology AI annotates medical images News posted on the MIT website Fitbits aid precision health American Life in Realtime study in PNAS Nexus Latest from the Human Epilepsy Project Study in JAMA Neurology Imposter study participants Editorial in The BMJ Guests Jonathan Crowther, Ph.D., Head, Operational Design Center, Merck KGaA, Darmstadt, Germany Janie Hansen, Global Development Information Management, Business Systems Transformation, Daiichi Sankyo Francis Kendall, Head of Statistical Programming, Digital and Data Sciences, Biogen James Weatherall, Ph.D., Vice President & Chief Data Scientist, BioPharmaceuticals R&D, AstraZeneca  The Scope of Things podcast explores clinical research and its possibilities, promise, and pitfalls. Clinical Research News senior writer, Deborah Borfitz, welcomes guests who are visionaries closest to the topics, but who can still see past their piece of the puzzle. Focusing on game-changing trends and out-of-the-box operational approaches in the clinical research field, the Scope of Things podcast is your no-nonsense, insider's look at clinical research today.

Topics covered in this episode: The PSF has withdrawn a $1.5 million proposal to US government grant program A Binary Serializer for Pydantic Models T-strings: Python's Fifth String Formatting Technique? Cronboard Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters 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: The PSF has withdrawn a $1.5 million proposal to US government grant program Related post from Simon Willison ARS Technica: Python plan to boost software security foiled by Trump admin's anti-DEI rules The Register: Python Foundation goes ride or DEI, rejects government grant with strings attached In Jan 2025, the PSF submitted a proposal for a US NSF grant under the Safety, Security, and Privacy of Open Source Ecosystems program. After months of work by the PSF, the proposal was recommended for funding. If the PSF accepted it, however, they would need to agree to the some terms and conditions, including, affirming that the PSF doesn't support diversity. The restriction wouldn't just be around the security work, but around all activity of the PSF as a whole. And further, that any deemed violation would give the NSF the right to ask for the money back. That just won't work, as the PSF would have already spent the money. The PSF mission statement includes "The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to support and facilitate the growth of a diverse and international community of Python programmers." The money would have obviously been very valuable, but the restrictions are just too unacceptable. The PSF withdrew the proposal. This couldn't have been an easy decision, that was a lot of money, but I think the PSF did the right thing. Michael #2: A Binary Serializer for Pydantic Models 7× Smaller Than JSON A compact binary serializer for Pydantic models that dramatically reduces RAM usage compared to JSON. The library is designed for high-load systems (e.g., Redis caching), where millions of models are stored in memory and every byte matters. It serializes Pydantic models into a minimal binary format and deserializes them back with zero extra metadata overhead. Target Audience: This project is intended for developers working with: high-load APIs in-memory caches (Redis, Memcached) message queues cost-sensitive environments where object size matters Brian #3: T-strings: Python's Fifth String Formatting Technique? Trey Hunner Python 3.14 has t-strings. How do they fit in with the rest of the string story? History percent-style (%) strings - been around for a very long time string.Template - and t.substitute() - from Python 2.4, but I don't think I've ever used them bracket variables and .format() - Since Python 2.6 f-strings - Python 3.6 - Now I feel old. These still seem new to me t-strings - Python 3.14, but a totally different beast. These don't return strings. Trey then covers a problem with f-strings in that the substitution happens at definition time. t-strings have substitution happen later. this is essentially “lazy string interpolation” This still takes a bit to get your head around, but I appreciate Trey taking a whack at the explanation. Michael #4: Cronboard Cronboard is a terminal application that allows you to manage and schedule cronjobs on local and remote servers. With Cronboard, you can easily add, edit, and delete cronjobs, as well as view their status. ✨ Features ✔️ Check cron jobs ✔️ Create cron jobs with validation and human-readable feedback ✔️ Pause and resume cron jobs ✔️ Edit existing cron jobs ✔️ Delete cron jobs ✔️ View formatted last and next run times ✔️ Accepts special expressions like @daily, @yearly, @monthly, etc. ✔️ Connect to servers using SSH, using password or SSH keys ✔️ Choose another user to manage cron jobs if you have the permissions to do so (sudo) Extras Brian: PEP 810: Explicit lazy imports, has been unanimously accepted by steering council Lean TDD book will be written in the open. TOC, some details, and a 10 page introduction are now available. Hoping for the first pass to be complete by the end of the year. I'd love feedback to help make it a great book, and keep it small-ish, on a very limited budget. Joke: You are so wrong!

Today, we're talking about building real AI products with foundation models. Not toy demos, not vibes. We'll get into the boring dashboards that save launches, evals that change your mind, and the shift from analyst to AI app builder. Our guide is Hugo Bowne-Anderson, educator, podcaster, and data scientist, who's been in the trenches from scalable Python to LLM apps. If you care about shipping LLM features without burning the house down, stick around. Episode sponsors Posit NordStellar Talk Python Courses Links from the show Hugo Bowne-Anderson: x.com Vanishing Gradients Podcast: vanishinggradients.fireside.fm Fundamentals of Dask: High Performance Data Science Course: training.talkpython.fm Building LLM Applications for Data Scientists and Software Engineers: maven.com marimo: a next-generation Python notebook: marimo.io DevDocs (Offline aggregated docs): devdocs.io Elgato Stream Deck: elgato.com Sentry's Seer: talkpython.fm The End of Programming as We Know It: oreilly.com LorikeetCX AI Concierge: lorikeetcx.ai Text to SQL & AI Query Generator: text2sql.ai Inverse relationship enthusiasm for AI and traditional projects: oreilly.com Watch this episode on YouTube: youtube.com Episode #526 deep-dive: talkpython.fm/526 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Dr. Julia Stoyanovich is Institute Associate Professor of Computer Science and Engineering, Associate Professor of Data Science, Director of the Center for Responsible AI, and member of the Visualization and Data Analytics Research Center at New York University. She is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) and a Senior member of the Association of Computing Machinery (ACM). Julia's goal is to make “Responsible AI” synonymous with “AI”. She works towards this goal by engaging in academic research, education and technology policy, and by speaking about the benefits and harms of AI to practitioners and members of the public. Julia's research interests include AI ethics and legal compliance, and data management and AI systems. Julia is engaged in technology policy and regulation in the US and internationally, having served on the New York City Automated Decision Systems Task Force, by mayoral appointment, among other roles. She received her M.S. and Ph.D. degrees in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics & Statistics from the University of Massachusetts at Amherst.Links:https://engineering.nyu.edu/faculty/julia-stoyanovich https://airesponsibly.net/nyaiexchange_2025/ Hosted on Acast. See acast.com/privacy for more information.

Adatépítész -az első magyar datapodcast Minden ami hír, érdekesség, esemény vagy tudásmorzsa az  adat, datascience, adatbányászat és hasonló kockaságok világából. Become a Patron! https://www.python.org/downloads/release/python-3140 Lufi

Many leaders are trapped between chasing ambitious, ill-defined AI projects and the paralysis of not knowing where to start. Dr. Randall Olson argues that the real opportunity isn't in moonshots, but in the "trillions of dollars of business value" available right now. As co-founder of Wyrd Studios, he bridges the gap between data science, AI engineering, and executive strategy to deliver a practical framework for execution. In this episode, Randy and Hugo lay out how to find and solve what might be considered "boring but valuable" problems, like an EdTech company automating 20% of its support tickets with a simple retrieval bot instead of a complex AI tutor. They discuss how to move incrementally along the "agentic spectrum" and why treating AI evaluation with the same rigor as software engineering is non-negotiable for building a disciplined, high-impact AI strategy. They talk through: How a non-technical leader can prototype a complex insurance claim classifier using just photos and a ChatGPT subscription. The agentic spectrum: Why you should start by automating meeting summaries before attempting to build fully autonomous agents. The practical first step for any executive: Building a personal knowledge base with meeting transcripts and strategy docs to get tailored AI advice. Why treating AI evaluation with the same rigor as unit testing is essential for shipping reliable products. The organizational shift required to unlock long-term AI gains, even if it means a short-term productivity dip. LINKS Randy on LinkedIn (https://www.zenml.io/llmops-database) Wyrd Studios (https://thewyrdstudios.com/) Stop Building AI Agents (https://www.decodingai.com/p/stop-building-ai-agents) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/-YQjKH3wRvc)

In this episode of Climate Positive, Guy Van Syckle and Gil Jenkins sit down with Caroline Spears, Executive Director of Climate Cabinet, a nonprofit dedicated to supporting clean energy and climate policy leaders at state and local levels. These often-forgotten races are sometimes decided by a couple hundred votes and can also decide the fate of billions of dollars of decarbonization investment. Caroline explains how Climate Cabinet strategically identifies target candidates through data science and political expertise, aiming to elect climate champions with the highest potential ability to shape positive change. Through real-world examples, she demonstrates the organization's effectiveness in close political races and the tangible difference their support can make.LinksClimate Cabinet Website Sign up for a monthly donation to help Climate Cabinet find and elect the highest ROI clean energy champions in state and local elections across the U.S. Caroline Spears on LinkedInEpisode recorded on October 2, 2025   Email your feedback to Chad, Gil, Hilary, and Guy at climatepositive@hasi.com.

Every data scientist is running their own business - it's just that most of those businesses are solo operations with one client: their employer. Unfortunately, most data scientists don't realise this and too many fall into the trap of believing their employer will magically take care of their career development, putting them on the right projects and ensuring they get proper training. The reality is that while bosses usually mean well, they have their own careers to worry about.In this episode, Danny Ruspandini joins Dr. Genevieve Hayes to explore how applying a solo business mindset to your data science career can help you take control of your professional destiny, increase your value within organisations, and create opportunities that others miss.You'll learn:How to become the go-to person for specific problems within your organisation [07:11]The "secondary sale" technique that gets your projects approved even when you're not in the room [14:49]Why focusing on one shiny object at a time accelerates your career faster than juggling multiple priorities [19:06]How to find your signature service that makes you indispensable to your employer [23:00]Guest BioDanny Ruspandini is a brand strategist, business coach and director of Impact Labs Australia. He is also the creator of One Shiny Object, a program for helping solo creatives package what they do into sellable, fixed-price services.LinksConnect with Danny on LinkedInDownload the One Shiny Object frameworkConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Building a UI in Python usually means choosing between "quick and limited" or "powerful and painful." What if you could write modern, component-based web apps in pure Python and still keep full control? NiceGUI, pronounced "Nice Guy" sits on FastAPI with a Vue/Quasar front end, gives you real components, live updates over websockets, and it's running in production at Zauberzeug, a German robotic company. On this episode, I'm talking with NiceGUI's creators, Rodja Trappe and Falko Schindler, about how it works, where it shines, and what's coming next. With version 3.0 releasing around the same time this episode comes out, we spend the end of the episode celebrating the 3.0 release. Episode sponsors Posit Agntcy Talk Python Courses Links from the show Rodja Trappe: github.com Falko Schindler: github.com NiceGUI 3.0.0 release: github.com Full LLM/Agentic AI docs instructions for NiceGUI: github.com Zauberzeug: zauberzeug.com NiceGUI: nicegui.io NiceGUI GitHub Repository: github.com NiceGUI Authentication Examples: github.com NiceGUI v3.0.0rc1 Release: github.com Valkey: valkey.io Caddy Web Server: caddyserver.com JustPy: justpy.io Tailwind CSS: tailwindcss.com Quasar ECharts v5 Demo: quasar-echarts-v5.netlify.app AG Grid: ag-grid.com Quasar Framework: quasar.dev NiceGUI Interactive Image Documentation: nicegui.io NiceGUI 3D Scene Documentation: nicegui.io Watch this episode on YouTube: youtube.com Episode #525 deep-dive: talkpython.fm/525 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: Cyclopts: A CLI library * The future of Python web services looks GIL-free* * Free-threaded GC* * Polite lazy imports for Python package maintainers* Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters 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: Cyclopts: A CLI library A CLI library that fixes 13 annoying issues in Typer Much of Cyclopts was inspired by the excellent Typer library. Despite its popularity, Typer has some traits that I (and others) find less than ideal. Part of this stems from Typer's age, with its first release in late 2019, soon after Python 3.8's release. Because of this, most of its API was initially designed around assigning proxy default values to function parameters. This made the decorated command functions difficult to use outside of Typer. With the introduction of Annotated in python3.9, type-hints were able to be directly annotated, allowing for the removal of these proxy defaults. The 13: Argument vs Option Positional or Keyword Arguments Choices Default Command Docstring Parsing Decorator Parentheses Optional Lists Keyword Multiple Values Flag Negation Help Defaults Validation Union/Optional Support Adding a Version Flag Documentation Brian #2: The future of Python web services looks GIL-free Giovanni Barillari “Python 3.14 was released at the beginning of the month. This release was particularly interesting to me because of the improvements on the "free-threaded" variant of the interpreter. Specifically, the two major changes when compared to the free-threaded variant of Python 3.13 are: Free-threaded support now reached phase II, meaning it's no longer considered experimental The implementation is now completed, meaning that the workarounds introduced in Python 3.13 to make code sound without the GIL are now gone, and the free-threaded implementation now uses the adaptive interpreter as the GIL enabled variant. These facts, plus additional optimizations make the performance penalty now way better, moving from a 35% penalty to a 5-10% difference.” Lots of benchmark data, both ASGI and WSGI Lots of great thoughts in the “Final Thoughts” section, including “On asynchronous protocols like ASGI, despite the fact the concurrency model doesn't change that much – we shift from one event loop per process, to one event loop per thread – just the fact we no longer need to scale memory allocations just to use more CPU is a massive improvement. ” “… for everybody out there coding a web application in Python: simplifying the concurrency paradigms and the deployment process of such applications is a good thing.” “… to me the future of Python web services looks GIL-free.” Michael #3: Free-threaded GC The free-threaded build of Python uses a different garbage collector implementation than the default GIL-enabled build. The Default GC: In the standard CPython build, every object that supports garbage collection (like lists or dictionaries) is part of a per-interpreter, doubly-linked list. The list pointers are contained in a PyGC_Head structure. The Free-Threaded GC: Takes a different approach. It scraps the PyGC_Head structure and the linked list entirely. Instead, it allocates these objects from a special memory heap managed by the "mimalloc" library. This allows the GC to find and iterate over all collectible objects using mimalloc's data structures, without needing to link them together manually. The free-threaded GC does NOT support "generations” By marking all objects reachable from these known roots, we can identify a large set of objects that are definitely alive and exclude them from the more expensive cycle-finding part of the GC process. Overall speedup of the free-threaded GC collection is between 2 and 12 times faster than the 3.13 version. Brian #4: Polite lazy imports for Python package maintainers Will McGugan commented on a LI post by Bob Belderbos regarding lazy importing “I'm excited about this PEP. I wrote a lazy loading mechanism for Textual's widgets. Without it, the entire widget library would be imported even if you needed just one widget. Having this as a core language feature would make me very happy.” https://github.com/Textualize/textual/blob/main/src/textual/widgets/__init__.py Well, I was excited about Will's example for how to, essentially, allow users of your package to import only the part they need, when they need it. So I wrote up my thoughts and an explainer for how this works. Special thanks to Trey Hunner's Every dunder method in Python, which I referenced to understand the difference between __getattr__() and __getattribute__(). Extras Brian: Started writing a book on Test Driven Development. Should have an announcement in a week or so. I want to give folks access while I'm writing it, so I'll be opening it up for early access as soon as I have 2-3 chapters ready to review. Sign up for the pythontest newsletter if you'd like to be informed right away when it's ready. Or stay tuned here. Michael: New course!!! Agentic AI Programming for Python I'll be on Vanishing Gradients as a guest talking book + ai for data scientists OpenAI launches ChatGPT Atlas https://github.com/jamesabel/ismain by James Abel Pets in PyCharm Joke: You're absolutely right

Mike Sall is the founder and CEO of Heron Finance, an SEC-registered investment advisor, making investing in the world's leading private market funds more accessible. Earlier in his career, Mike led product analytics at Coinbase, served as Head of Data Science at Medium, and worked in analytics and product roles at Adobe and Deloitte. He earned his Bachelor of Science in Economics from the Wharton School at the University of Pennsylvania.

Rohan Kodialam, cofounder and CEO of Sphinx, is building AI agents that treat data as its own language—one most models and humans still fail to understand. In this episode, he unpacks why data science has lagged behind software engineering, how AI can finally close the gap between business questions and answers, and what happens when small teams gain the analytical power of a thousand person quant desk.What You'll Learn• How AI models that actually see data can unlock insights traditional transformers miss• Why enterprises must rethink dashboards and embrace real time ad hoc analysis• Where AI truly saves the most time across the data lifecycle and why modeling is not the hardest part• How decoupling statistics from business context gives teams freedom to focus on strategy and creativity• Why success in data science now means reclaiming human creativity while automating repetitive workTimestamped Highlights[01:44] Why data is fundamentally different from text and code and why most AI models struggle with it[06:39] The cultural problem with ad hoc being a dirty word in enterprises and why that mindset is changing[11:09] Where AI tools actually fit into the data science workflow[17:09] How to measure success when using an AI data scientist[21:04] What happens when a small team gains the data firepower of a hedge fund quant operation[24:37] Why bad data science is worse than none and why quality matters more than hypeA Thought That Stuck With Us“We are cutting the time to completion by 20x, 40x, even 50x and that remaining human review is not a bottleneck. It is the feature that keeps AI accountable.”Worth FollowingConnect with Rohan Kodialam on X (@KodialamRo) or LinkedIn and learn more about Sphinx AI and how they are transforming enterprise data science.If This ResonatedShare this with someone in the data world who is tired of waiting weeks for insights that should take minutes. Follow The Tech Trek for more conversations about how people and technology create lasting impact.

In this episode of Real Talk with Anant Veeravalli, the discussion revolves around the evolving data landscape and the necessity for strategic partnerships to achieve holistic measurement. The team unpacks the importance of ethical data sourcing, privacy compliance, and the utilization of clean room environments like Snowflake and Databricks to bridge data gaps. Enabling secure and scalable data connectivity and facilitating real-time data sharing is key for brands to derive meaningful intelligence, including predictive modeling and AI-driven insights. This episode is essential listening for anyone focused on governance, security, and future-proofing data systems.Thanks for listening! Follow us on Twitter and Instagram or find us on Facebook.

In this talk, Sebastian, a bioinformatics researcher and software engineer, shares his inspiring journey from wet lab biotechnology to computational bioinformatics. Hosted by Data Talks Club, this session explores how data science, AI, and open-source tools are transforming modern biological research — from DNA sequencing to metagenomics and protein structure prediction.You'll learn about: - The difference between wet lab and dry lab workflows in biotechnology - How bioinformatics enables faster insights through data-driven modeling - The MCW2 Graph Project and its role in studying wastewater microbiomes - Using co-abundance networks and the CC Lasso algorithm to map microbial interactions - How AlphaFold revolutionized protein structure prediction - Building scientific knowledge graphs to integrate biological metadata - Open-source tools like VueGen and VueCore for automating reports and visualizations - The growing impact of AI and large language models (LLMs) in research and documentation - Key differences between R (BioConductor) and Python ecosystems for bioinformaticsThis talk is ideal for data scientists, bioinformaticians, biotech researchers, and AI enthusiasts who want to understand how data science, AI, and biology intersect. Whether you work in genomics, computational biology, or scientific software, you'll gain insights into real-world tools and workflows shaping the future of bioinformatics.Links:- MicW2Graph: https://zenodo.org/records/12507444- VueGen: https://github.com/Multiomics-Analytics-Group/vuegen- Awesome-Bioinformatics: https://github.com/danielecook/Awesome-BioinformaticsTIMECODES00:00 Sebastian's Journey into Bioinformatics06:02 From Wet Lab to Computational Biology08:23 Wet Lab vs Dry Lab Explained12:35 Bioinformatics as Data Science for Biology15:30 How DNA Sequencing Works19:29 MCW2 Graph and Wastewater Microbiomes23:10 Building Microbial Networks with CC Lasso26:54 Protein–Ligand Simulation Basics29:58 Predicting Protein Folding in 3D33:30 AlphaFold Revolution in Protein Prediction36:45 Inside the MCW2 Knowledge Graph39:54 VueGen: Automating Scientific Reports43:56 VueCore: Visualizing OMIX Data47:50 Using AI and LLMs in Bioinformatics50:25 R vs Python in Bioinformatics Tools53:17 Closing Thoughts from EcuadorConnect with SebastianTwitter - https://twitter.com/sayalaruanoLinkedin - https://linkedin.com/in/sayalaruano Github - https://github.com/sayalaruanoWebsite - https://sayalaruano.github.io/Connect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn - https://www.linkedin.com/company/datatalks-club/Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

Sean Bruich, Senior Vice President of AI, Engineering and Data Science at Amgen, joins Barbara Kahn and Dr. Americus Reed, II to discuss how AI's rapid evolution is transforming industries—not by replacing humans, but by creating new opportunities that blend human expertise with advanced technology to drive innovation and efficiency. Hosted on Acast. See acast.com/privacy for more information.

Join us for a masterclass in building data-driven marketing cultures. Moe Kiss, Director of Data Science in Marketing at Canva, shares how she bridges analytics and storytelling to make data approachable across teams. She breaks down the shift from attribution to experimentation, why storytelling is a must-have skill for every data professional, and how to balance intuition with evidence. Discover how Moe's redefining the balance between technology, trust, and empathy in data leadership.Key Moments:Data as “Part of the Meal” (09:09): Using a cooking analogy, Moe describes how data should be integrated into decision-making, not as a garnish or afterthought, but as part of the meal itself. She highlights Canva's culture of balancing intuition with data, ensuring both creative instinct and analytics inform every decision.Storytelling as a Data Superpower (10:57): Moe breaks down her “insight headline” method, turning flat dashboards into stories that drive action. She argues that data storytelling isn't about aesthetics but understanding, and that trust comes from how clearly insights connect to business impact.Technical Fluency in the Age of AI (18:16): While some leaders claim programming is obsolete, Moe insists it's more important than ever. Understanding what's under the hood helps data professionals vet AI outputs, maintain data quality, and confidently guide stakeholders through rapid technological change.Buy vs. Build: The Cost–Benefit Equation (20:11): Moe breaks down how Canva evaluates when to build internal tools versus buy off-the-shelf solutions. She stresses that the best answer depends on scalability, cost, and time to market — and that implementation costs are often underestimated.AI's New Role in Marketing (24:48): Moe shares how AI is reshaping marketing teams and their relationship with data. She highlights how generative AI tools can democratize access to insights, but warns that without trusted, high-quality data foundations, AI can just as easily amplify mistakes.Key Quotes:"Data is about creativity…It's not about it being pretty, it's about being understood.” - Moe Kiss“ We are leveraging AI tools more, and so understanding what's under the hood, I would say, is more essential than ever.” - Moe Kiss“[Know] the business appetite. You need to know how to get the answer with the right level of certainty or uncertainty…at pace with the business.” - Moe KissMentionsAnalytics Power Hour PodcastWhy Are Semantic Layers Suddenly Sexy?The Agentic Semantic Layer and OSI: A New Standard for AIThoughtSpot Joins Forces with Snowflake and Industry Leaders to Spearhead Open Semantic Interchange, Ushering in a New Era of Data and AI InteroperabilityGuest Bio Moe is a Director of Data Science in Marketing at Canva. Prior to that, she was in senior product and marketing analytics roles at THE ICONIC and in an attribution agency, Datalicious. Moe is passionate about growing and developing data scientists, driving industry leading measurement techniques and tooling and expanding the business impact of her team.She is a passionate and active member of the analytics community, co-hosting a bi-weekly podcast, the Analytics Power Hour. She served as the president of the Analytics Association of New South Wales for 7 years and is an ongoing committee member where she helps run Data Analytics Wednesday, a monthly meet up, and Sydney MeasureCamp, yearly unconference. She won the Digital Analytics Association USA's top new practitioner award in 2018. In 2024 was nominated in the Women Leading Tech Awards in the Data Science category and was awarded Snowflake's Data Hero of the Year award. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Brandon Weber, Co-founder & CEO of Nava Benefits, joined us on The Modern People Leader.We talked about why benefits have become the second-largest company expense — and how HR can “moneyball” their healthcare spend, cut down on benefits-related admin work, and deliver better employee outcomes through the emerging “alt marketplace.”---- Nava Links:

Join us on the Alter Everything Podcast as we sit down with Olga Beregovaya, Vice President of AI at Smartling, to explore the evolving landscape of translation technology and AI model strategy. In this episode, Olga shares her 25+ years of experience in language technology, discusses the shift from rule-based to transformer models, and explains the importance of purpose-built AI models for translation.Panelists: Olga Beregovaya, VP of AI @ Smartling - LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: SmartlingDeepLGoogle Vertex AIIBM WatsonxAWS BedrockAzure OpenAIScale AITELUS International Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music.

Today's guest is Xiong Liu, Director of Data Science and AI at Novartis. Novartis is among the world's leading pharmaceutical companies, pioneering data and advanced analytics in the pursuit of new medicines and patient outcomes. Xiong joins Emerj Editorial Director Matthew DeMello to examine how generative AI and foundation models are transforming R&D, clinical workflows, and research collaboration across the life sciences. The discussion highlights how domain-specific data strategies, improved data quality, and shared benchmarks are accelerating discovery and operationalizing AI for measurable ROI in biopharma. 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 the ‘AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

Python in 2025 is different. Threads really are about to run in parallel, installs finish before your coffee cools, and containers are the default. In this episode, we count down 38 things to learn this year: free-threaded CPython, uv for packaging, Docker and Compose, Kubernetes with Tilt, DuckDB and Arrow, PyScript at the edge, plus MCP for sane AI workflows. Expect practical wins and migration paths. No buzzword bingo, just what pays off in real apps. Join me along with Peter Wang and Calvin Hendrix-Parker for a fun, fast-moving conversation. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Calvin Hendryx-Parker: github.com/calvinhp Peter on BSky: @wang.social Free-Threaded Wheels: hugovk.github.io Tilt: tilt.dev The Five Demons of Python Packaging That Fuel Our ...: youtube.com Talos Linux: talos.dev Docker: Accelerated Container Application Development: docker.com Scaf - Six Feet Up: sixfeetup.com BeeWare: beeware.org PyScript: pyscript.net Cursor: The best way to code with AI: cursor.com Cline - AI Coding, Open Source and Uncompromised: cline.bot Watch this episode on YouTube: youtube.com Episode #524 deep-dive: talkpython.fm/524 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: * djrest2 -* A small and simple REST library for Django based on class-based views. Github CLI caniscrape - Know before you scrape. Analyze any website's anti-bot protections in seconds. *

Python typing got fast enough to feel invisible. Pyrefly is a new, open source type checker and IDE language server from Meta, written in Rust, with a focus on instant feedback and real-world DX. Today, we will dig into what it is, why it exists, and how it plays with the rest of the typing ecosystem. We have Abby Mitchell, Danny Yang, and Kyle Into from Pyrefly here to dive into the project. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Abby Mitchell: linkedin.com Danny Yang: linkedin.com Kyle Into: linkedin.com Pyrefly: pyrefly.org Pyrefly Documentation: pyrefly.org Pyrefly Installation Guide: pyrefly.org Pyrefly IDE Guide: pyrefly.org Pyrefly GitHub Repository: github.com Pyrefly VS Code Extension: marketplace.visualstudio.com Introducing Pyrefly: A New Type Checker and IDE Experience for Python: engineering.fb.com Pyrefly on PyPI: pypi.org InfoQ Coverage: Meta Pyrefly Python Typechecker: infoq.com Pyrefly Discord Invite: discord.gg Python Typing Conformance (GitHub): github.com Typing Conformance Leaderboard (HTML Preview): htmlpreview.github.io Watch this episode on YouTube: youtube.com Episode #523 deep-dive: talkpython.fm/523 Episode transcripts: talkpython.fm Theme Song: Developer Rap