Podcasts about Data science

Topics covered in this episode: Deprecations via warnings docs PyAtlas: interactive map of the top 10,000 Python packages on PyPI. Buckaroo Extras Joke Watch on YouTube About the show 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: Deprecations via warnings Deprecations via warnings don't work for Python libraries Seth Larson How to encourage developers to fix Python warnings for deprecated features Ines Panker Michael #2: docs A collaborative note taking, wiki and documentation platform that scales. Built with Django and React. Made for self hosting Docs is the result of a joint effort led by the French

For years, building interactive widgets in Python notebooks meant wrestling with toolchains, platform quirks, and a mountain of JavaScript machinery. Most developers took one look and backed away slowly. Trevor Manz decided that barrier did not need to exist. His idea was simple: give Python users just enough JavaScript to unlock the web's interactivity, without dragging along the rest of the web ecosystem. That idea became anywidget, and it is quickly becoming the quiet connective tissue of modern interactive computing. Today we dig into how it works, why it has taken off, and how it might change the way we explore data. Episode sponsors Seer: AI Debugging, Code TALKPYTHON PyCharm, code STRONGER PYTHON Talk Python Courses Links from the show Trevor on GitHub: github.com anywidget GitHub: github.com Trevor's SciPy 2024 Talk: www.youtube.com Marimo GitHub: github.com Myst (Markdown docs): mystmd.org Altair: altair-viz.github.io DuckDB: duckdb.org Mosaic: uwdata.github.io ipywidgets: ipywidgets.readthedocs.io Tension between Web and Data Sci Graphic: blobs.talkpython.fm Quak: github.com Walk through building a widget: anywidget.dev Widget Gallery: anywidget.dev Video: How do I anywidget?: www.youtube.com PyCharm + PSF Fundraiser: pycharm-psf-2025 code STRONGER PYTHON Watch this episode on YouTube: youtube.com Episode #530 deep-dive: talkpython.fm/530 Episode transcripts: talkpython.fm Theme Song: Developer Rap

What if everything we think we know about AI understanding is wrong? Is compression the key to intelligence? Or is there something more—a leap from memorization to true abstraction? In this fascinating conversation, we sit down with **Professor Yi Ma**—world-renowned expert in deep learning, IEEE/ACM Fellow, and author of the groundbreaking new book *Learning Deep Representations of Data Distributions*. Professor Ma challenges our assumptions about what large language models actually do, reveals why 3D reconstruction isn't the same as understanding, and presents a unified mathematical theory of intelligence built on just two principles: **parsimony** and **self-consistency**.**SPONSOR MESSAGES START**—Prolific - Quality data. From real people. For faster breakthroughs.https://www.prolific.com/?utm_source=mlst—cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economyHiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst—**END**Key Insights:**LLMs Don't Understand—They Memorize**Language models process text (*already* compressed human knowledge) using the same mechanism we use to learn from raw data. **The Illusion of 3D Vision**Sora and NeRFs etc that can reconstruct 3D scenes still fail miserably at basic spatial reasoning**"All Roads Lead to Rome"**Why adding noise is *necessary* for discovering structure.**Why Gradient Descent Actually Works**Natural optimization landscapes are surprisingly smooth—a "blessing of dimensionality" **Transformers from First Principles**Transformer architectures can be mathematically derived from compression principles—INTERACTIVE AI TRANSCRIPT PLAYER w/REFS (ReScript):https://app.rescript.info/public/share/Z-dMPiUhXaeMEcdeU6Bz84GOVsvdcfxU_8Ptu6CTKMQAbout Professor Yi MaYi Ma is the inaugural director of the School of Computing and Data Science at Hong Kong University and a visiting professor at UC Berkeley. https://people.eecs.berkeley.edu/~yima/https://scholar.google.com/citations?user=XqLiBQMAAAAJ&hl=en https://x.com/YiMaTweets **Slides from this conversation:**https://www.dropbox.com/scl/fi/sbhbyievw7idup8j06mlr/slides.pdf?rlkey=7ptovemezo8bj8tkhfi393fh9&dl=0**Related Talks by Professor Ma:**- Pursuing the Nature of Intelligence (ICLR): https://www.youtube.com/watch?v=LT-F0xSNSjo- Earlier talk at Berkeley: https://www.youtube.com/watch?v=TihaCUjyRLMTIMESTAMPS:00:00:00 Introduction00:02:08 The First Principles Book & Research Vision00:05:21 Two Pillars: Parsimony & Consistency00:09:50 Evolution vs. Learning: The Compression Mechanism00:14:36 LLMs: Memorization Masquerading as Understanding00:19:55 The Leap to Abstraction: Empirical vs. Scientific00:27:30 Platonism, Deduction & The ARC Challenge00:35:57 Specialization & The Cybernetic Legacy00:41:23 Deriving Maximum Rate Reduction00:48:21 The Illusion of 3D Understanding: Sora & NeRF00:54:26 All Roads Lead to Rome: The Role of Noise00:59:56 All Roads Lead to Rome: The Role of Noise01:00:14 Benign Non-Convexity: Why Optimization Works01:06:35 Double Descent & The Myth of Overfitting01:14:26 Self-Consistency: Closed-Loop Learning01:21:03 Deriving Transformers from First Principles01:30:11 Verification & The Kevin Murphy Question01:34:11 CRATE vs. ViT: White-Box AI & ConclusionREFERENCES:Book:[00:03:04] Learning Deep Representations of Data Distributionshttps://ma-lab-berkeley.github.io/deep-representation-learning-book/[00:18:38] A Brief History of Intelligencehttps://www.amazon.co.uk/BRIEF-HISTORY-INTELLIGEN-HB-Evolution/dp/0008560099[00:38:14] Cyberneticshttps://mitpress.mit.edu/9780262730099/cybernetics/Book (Yi Ma):[00:03:14] 3-D Vision bookhttps://link.springer.com/book/10.1007/978-0-387-21779-6 refs on ReScript link/YT

Exploring innovation where education meets entrepreneurship. About Durga Suresh-Menon Durga Suresh-Menon, Ph.D., is Head of School at New England Innovation Academy. An energizing, dynamic and growth-minded educator with a record of inclusive leadership and passionate storytelling, Dr. Suresh-Menon joins NEIA with over two decades of collaborative higher-education experience, academic program development and a unique understanding of what makes students successful. She has a rich background in higher education, leadership, curriculum development, and academic excellence. Before joining NEIA, she served as Dean of the School of Computing and Data Science and Dean of Graduate Education at Wentworth Institute of Technology, as well as an Associate Professor, where she led efforts to implement progressive learning strategies and interdisciplinary curriculum that promoted innovation and global awareness. She is recognized for her work fostering a culture of growth, development and innovation, ensuring that a STEAM curriculum remains aligned with the ever-evolving technological landscape and industry demands. Fluent in multiple languages, Dr. Suresh-Menon loves to connect with tech-minded students and parents from all backgrounds, and brings a global perspective and collaborative spirit to NEIA's academic community. Instagram: https://www.instagram.com/hello.neia/ Twitter: https://x.com/helloneia Facebook: https://www.facebook.com/HelloNEIA/ LinkedIn: https://www.linkedin.com/in/durga-suresh-menon/ About John Camp (he goes by Camp) Camp has been teaching in independent schools for over 25 years. His experience includes English and writing classes as well as interdisciplinary courses such as “The Art and Physics of Time Travel.” At St. Mark's School, which bestowed him with The Trustees Chair and the Kidder Faculty Prize, Camp served as the Director of Experiential Learning and Associate Director of The Center of Innovation in Teaching and Learning. A pair of his pedagogical mantras include “I aim to teach what cannot be Googled” and “I expect you to work hard, so I work hard.” He has a B.A. English/Creative Writing from Middlebury College and M.A.L.S. from Dartmouth College. Instagram: https://www.instagram.com/hello.neia/ Facebook: https://www.facebook.com/HelloNEIA/ LinkedIn: https://www.linkedin.com/in/campsm/ Resources https://neiacademy.org/ https://www.linkedin.com/company/new-england-innovation-academy/  John Mikton on Social Media LinkedIn: https://www.linkedin.com/in/jmikton/ Twitter: https://twitter.com/jmikton Web: beyonddigital.org Dan Taylor on social media: LinkedIn: https://www.linkedin.com/in/appsevents  Twitter: https://twitter.com/appdkt  Web: www.appsevents.com Listen on: iTunes / Podbean / Stitcher / Spotify / YouTube Would you like to have a free 1 month trial of the new Google Workspace Plus (formerly G Suite Enterprise for Education)? Just fill out this form and we'll get you set up bit.ly/GSEFE-Trial

While the transition from academia to industry can be brutal for data scientists, academics don't show up in industry empty-handed. They bring powerful transferable skills that many industry-trained data scientists never develop.In this Value Boost episode, Dr. Sayli Javadekar joins Dr. Genevieve Hayes to flip the script on their previous conversation, exploring the valuable skills that academic-trained data scientists bring to industry and how any data scientist can develop these same strengths.You'll learn:The most valuable skills academics bring to industry [01:30]Why the experimental mindset matters so much [03:43]The hidden benefit of extended research projects [04:54]How mentorship can work both ways for mutual benefit [07:06]Guest BioDr Sayli Javadekar is a data scientist at Thoughtworks, with experience at the World Bank and UNAIDS. Before this, she was an Assistant Professor at the University of Bath and holds a PhD in Econometrics from the University of Geneva.LinksConnect with Sayli on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

In this powerful episode of Change Leadership Conversations, Yvonne Ruke Akpoveta sits down with one of Canada's foremost experts on the intersection of AI and healthcare, Dr. Muhammad Mamdani. With over 600 published studies and leadership roles across Unity Health Toronto, Ontario Health, and the University of Toronto, Dr. Mamdani brings real-world insight into how AI can be responsibly developed and deployed to improve outcomes in life-and-death scenarios.We explore:• The practical realities of applying AI• How AI is disrupting education, critical thinking, and the world• What “responsible AI” really looks like, and why it's urgent• How to manage AI hallucinations in critical contexts like healthcare, and beyond• Building trust and engaging frontline stakeholders for adoption and co-creationWhether you're a change leader, innovator, or just curious about the impact of AI — this conversation will spark ideas and deepen your understanding of the change we're allnavigating.Guest Bio:Dr. Muhammad Mamdani is one of Canada's leading voices on AI in healthcare. He serves as Clinical Lead for AI at Ontario Health, VP of Data Science at Unity Health Toronto, andDirector of T-CAIREM at the University of Toronto. Dr. Mamdani's work bridges advanced analytics with real-world clinical decision-making. He's a Faculty Affiliate at the VectorInstitute, an Affiliate Scientist at IC/ES, and was recognized as one of Canada's Top 40 Under 40. His team received the national Solventum Health Care Innovation Team Award.Resources & Links:Connect with Dr. Muhammad Mamdani on LinkedInConnect with Yvonne Ruke Akpoveta on LinkedInLearn more about the Change Leadership TrainingBrought to You By:The Change Leadership – Your go-to ecosystem for future-ready change leadership training, resources, and the annual Change Leadership Conference. Learn more at TheChangeLeadership.comSubscribe & ReviewIf you enjoyed this episode, don't forget to rate, subscribe, and leave a review. It helps others discover the show, and we appreciate your support!

Topics covered in this episode: PEP 798: Unpacking in Comprehensions Pandas 3.0.0rc0 typos A couple testing topics 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: PEP 798: Unpacking in Comprehensions After careful deliberation, the Python Steering Council is pleased to accept PEP 798 – Unpacking in Comprehensions. Examples [*it for it in its] # list with the concatenation of iterables in 'its' {*it for it in its} # set with the union of iterables in 'its' {**d for d in dicts} # dict with the combination of dicts in 'dicts' (*it for it in its) # generator of the concatenation of iterables in 'its' Also: The Steering Council is happy to unanimously accept “PEP 810, Explicit lazy imports” Brian #2: Pandas 3.0.0rc0 Pandas 3.0.0 will be released soon, and we're on Release candidate 0 Here's What's new in Pands 3.0.0 Dedicated string data type by default Inferred by default for string data (instead of object dtype) The str dtype can only hold strings (or missing values), in contrast to object dtype. (setitem with non string fails) The missing value sentinel is always NaN (np.nan) and follows the same missing value semantics as the other default dtypes. Copy-on-Write The result of any indexing operation (subsetting a DataFrame or Series in any way, i.e. including accessing a DataFrame column as a Series) or any method returning a new DataFrame or Series, always behaves as if it were a copy in terms of user API. As a consequence, if you want to modify an object (DataFrame or Series), the only way to do this is to directly modify that object itself. pd.col syntax can now be used in DataFrame.assign() and DataFrame.loc() You can now do this: df.assign(c = pd.col('a') + pd.col('b')) New Deprecation Policy Plus more - Michael #3: typos You've heard about codespell … what about typos? VSCode extension and OpenVSX extension. From Sky Kasko: Like codespell, typos checks for known misspellings instead of only allowing words from a dictionary. But typos has some extra features I really appreciate, like finding spelling mistakes inside snake_case or camelCase words. For example, if you have the line: *connecton_string = "sqlite:///my.db"* codespell won't find the misspelling, but typos will. It gave me the output: *error: `connecton` should be `connection`, `connector` ╭▸ ./main.py:1:1 │1 │ connecton_string = "sqlite:///my.db" ╰╴━━━━━━━━━* But the main advantage for me is that typos has an LSP that supports editor integrations like a VS Code extension. As far as I can tell, codespell doesn't support editor integration. (Note that the popular Code Spell Checker VS Code extension is an unrelated project that uses a traditional dictionary approach.) For more on the differences between codespell and typos, here's a comparison table I found in the typos repo: https://github.com/crate-ci/typos/blob/master/docs/comparison.md By the way, though it's not mentioned in the installation instructions, typos is published on PyPI and can be installed with uv tool install typos, for example. That said, I don't bother installing it, I just use the VS Code extension and run it as a pre-commit hook. (By the way, I'm using prek instead of pre-commit now; thanks for the tip on episode #448!) It looks like typos also publishes a GitHub action, though I haven't used it. Brian #4: A couple testing topics slowlify suggested by Brian Skinn Simulate slow, overloaded, or resource-constrained machines to reproduce CI failures and hunt flaky tests. Requires Linux with cgroups v2 Why your mock breaks later Ned Badthelder Ned's taught us before to “Mock where the object is used, not where it's defined.” To be more explicit, but probably more confusing to mock-newbies, “don't mock things that get imported, mock the object in the file it got imported to.” See? That's probably worse. Anyway, read Ned's post. If my project myproduct has user.py that uses the system builtin open() and we want to patch it: DONT DO THIS: @patch("builtins.open") This patches open() for the whole system DO THIS: @patch("myproduct.user.open") This patches open() for just the user.py file, which is what we want Apparently this issue is common and is mucking up using coverage.py Extras Brian: The Rise and Rise of FastAPI - mini documentary “Building on Lean” chapter of LeanTDD is out The next chapter I'm working on is “Finding Waste in TDD” Notes to delete before end of show: I'm not on track for an end of year completion of the first pass, so pushing goal to 1/31/26 As requested by a reader, I'm releasing both the full-so-far versions and most-recent-chapter Michael: My Vanishing Gradient's episode is out Django 6 is out Joke: tabloid - A minimal programming language inspired by clickbait headlines

In this episode, we look at the actuarial principles that make models safer: parallel modeling, small data with provenance, and real-time human supervision. To help us, long-time insurtech and startup advisor David Sandberg, FSA, MAAA, CERA, joins us to share more about his actuarial expertise in data management and AI. We also challenge the hype around AI by reframing it as a prediction machine and putting human judgment at the beginning, middle, and end. By the end, you might think about “human-in-the-loop” in a whole new way.• Actuarial valuation debates and why parallel models win• AI's real value: enhance and accelerate the growth of human capital• Transparency, accountability, and enforceable standards• Prediction versus decision and learning from actual-to-expected• Small data as interpretable, traceable fuel for insight• Drift, regime shifts, and limits of regression and LLMs• Mapping decisions, setting risk appetite, and enterprise risk management (ERM) for AI• Where humans belong: the beginning, middle, and end of the system• Agentic AI complexity versus validated end-to-end systems• Training judgment with tools that force critique and citationCultural references:Foundation, AppleTVThe Feeling of Power, Isaac AsimovPlayer Piano, Kurt VonnegutFor more information, see Actuarial and data science: Bridging the gap.What did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

This podcast is brought to you by Outcomes Rocket, your exclusive healthcare marketing agency. Learn how to accelerate your growth by going to⁠ outcomesrocket.com Thoughtful, problem-first innovation drives real clinical impact in healthcare. In this episode, Saul Marquez and Ed Gaudet from Censinet host Dr. Jason Hill, Innovation Officer at Ochsner Health, and David Leingang, Director of Innovation Data Science at Ochsner Health, to discuss how their team uses machine learning, workflow redesign, and data-driven insights to reduce physician message burden and improve patient routing. They share how analyzing 2.4 million inbox messages revealed that 4% were tied to weight-loss drugs, prompting the creation of a new weight-management digital medicine program instead of an AI tool. They explain how reorganizing message flows, adding e-visits, and using ML to uncover hidden system strain has improved efficiency, while predictive deterioration models saved lives but had to be retrained as outcomes changed. The conversation closes with an exploration of value-based care, problem-solving in AI, and the AHEAD Network's role in advancing healthcare innovation. Tune in and learn how practical AI, smarter workflows, and cross-industry collaboration are reshaping modern healthcare! Resources Connect with and follow Jason Hill on LinkedIn. Follow and connect with David Leingang on LinkedIn. Follow Ochsner Health on LinkedIn and explore their website!

What do you do when your day job feels empty — but you still need to show up, provide, and stay honest?In this episode of Shtark Tank, I sit down with my cousin and friend Yoni Schwartz — Head of Data Science at Exponential and Head Producer of Living L'chaim. Yoni shares how he went from a corporate role that felt like “a complete lack of purpose” to leading 10 shows that have inspired and helped countless people. He is also the host of Spirit of the Song podcast, make sure to check it out!We talk about meaning, ambition, family, and the real-life tradeoffs of building something big on top of a demanding day job.In this conversation we cover:What it feels like when work is steady… but meaninglessHow Yoni first joined Living L'chaim and how the role grew over timeThe ethics of balancing a primary job with major side projectsStartup life vs. corporate life — and what actually changed for himHow he manages two intense roles without a rigid systemThe idea of intentionality as a survival tool for busy peopleSetting boundaries after COVID blurred everythingEarly morning learning as a realistic anchor for fathers with young kidsThe impact Living L'chaim aims for — inspiration, mental health, and financial clarityCultivating a relationship with your RebbiKey takeaway:You don't need a perfect system to juggle a lot — but you do need honesty, priorities, and intentional choices you can live with.If this episode resonated, please take a moment to follow the show and leave a 5-star rating. It helps more Bnei Torah in the workforce find these conversations.Guest: Yoni SchwartzHost: Yaakov Wolff

Dr. Satyajit Wattamwar is a Data Science & Digital Expertise Leader with over 17 years of professional experience in enabling digital transformation across manufacturing, R&D, and innovation processes. He currently leads the development of in-house cloud AI platforms and data science technologies for cross-country, cross-functional business initiatives. His expertise spans predictive modeling, IoT analytics, advanced process control, and manufacturing analytics.On The Menu:Data scientist empowerment through AI-powered digital assistantsScaling digital solutions across 180+ global factoriesIndustry 4.0 ROI measurement strategies and KPIsDigital twin technology revolutionizing manufacturing operationsQuantum computing's potential impact on process optimizationExplainable AI methodologies for industrial safety applicationsSupply chain optimization using predictive data science

Analysis of a series of surveys over the span of six years has shown public confidence in the live export industry has grown. Data Science company Voconiq has carried out the research since 2019, and more than 80 percent of respondents in this year's survey agreed the industry plays an important economic role, up from 72 percent six years ago. Rural Editor Emily Minney spoke with Voconiq Chief Executive Kieran Moffat about his findings.See omnystudio.com/listener for privacy information.

Michael Crow is the president of Arizona State University, which U.S. News & World Report has called the most innovative school in the country for 11 years running. He tells Steve about why higher education needs to change, and how A.S.U. is leading the way. Plus: Steve has an announcement about the podcast. SOURCES:Michael Crow, president of Arizona State University. RESOURCES:The Fifth Wave: The Evolution of American Higher Education, by Michael Crow (2020)."College Admissions Shocker!," by Frank Bruni (New York Times, 2016).New American University.Dreamscape Learn.University Innovation Alliance.FYI.AI. EXTRAS:"Chemistry, Evolved," by People I (Mostly) Admire (2025)."America's Math Curriculum Doesn't Add Up," by People I (Mostly) Admire (2021).Data Science 4 Everyone. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

From studying Business Data Science to landing a role in investment banking at Centerview Partners, this is my honest story of how I discovered my path, the mistakes I made, and what I wish every student knew before starting their career. In this video, I share how I transitioned from college to corporate life — the lessons I learned outside the classroom, why real-world experience matters, and how small opportunities can lead to big growth. Whether you're a university student, career changer, or just curious about finance and personal growth, this episode will give you insight, motivation, and practical steps to help you find your direction.

AI is becoming ubiquitous in our lives. It shapes how we work, play, interact, create, and even manage our health—and this is only the beginning. To understand where we are and where we might go, we first need to understand how we got here. By tracing the evolving nature of machine intelligence, we can appreciate how today's AI differs from its past and how it is likely to evolve. With that in mind, we can begin to ask the big questions: When should we trust AI over human judgment? How should we govern its development? How will it change what it means to be human? And what roles will humans play in the future of work?To help us through this journey, I'm delighted to welcome back to TRIUM Connects Professor Vasant Dhar, the Robert A. Miller Professor at NYU's Stern School of Business and Professor of Data Science at NYU. Vasant is one of the world's leading thinkers on the impact of AI on society. He was present at the birth of AI and has been involved in every step of its evolution—both as an entrepreneur and as a scholar. He also hosts the acclaimed podcast Brave New World, which explores how machines are transforming humanity in the post-COVID era.In this episode, we discuss his newest book, Thinking With Machines: The Brave New World of AI. It's a remarkable hybrid: part autobiography, tracing how his professional life has intertwined with the development of AI; part user's guide, offering a lucid framework for deciding when to trust machines over human control; and part deep dive into the philosophical and policy implications of creating an alien intelligence.It was a real pleasure to welcome Vasant back onto the show. I learned a lot during our conversation, and I hope you will enjoy it as much as I did.CitationsDawid A, LeCun Y. Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence. arXiv. June 5, 2023.Dennett DC. Intentional systems. J Philos. 1971;68(4):87-106.Dhar V. Thinking With Machines: The Brave New World of AI. Galloway S, foreword. Hoboken, NJ: Wiley; 2025.Frank, R. H., & Cook, P. J. The winner-take-all society: Why the few at the top get so much more than the rest of us. Penguin Books; 1995.Ganguli D, Askell A, Henighan T, et al. Alignment faking in large language models. arXiv. December 20, 2024.Harari YN. Nexus: A Brief History of Information Networks from the Stone Age to AI. New York, NY: Random House; 2024.Kauffmann J, Dippel J, Ruff L, et al. Explainable AI reveals Clever Hans effects in unsupervised learning models. Nat Mach Intell. 2025;7:1–10.Pearl J, Mackenzie D. The Book of Why: The New Science of Cause and Effect. New York, NY: Basic Books; 2018.Pfungst O. Clever Hans (The Horse of Mr. Von Osten): A Contribution to Experimental Animal and Human Psychology. Rahn H, trans. New York: Henry Holt; 1911.Popper KR. The Logic of Scientific Discovery. London, UK: Hutchinson; 1959Suleyman M, Bhaskar M. The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma. New York, NY: Crown; 2023.Yudkowsky E, Soares N. If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All. New York, NY: Little, Brown and Company; 2025. Hosted on Acast. See acast.com/privacy for more information.

Chris Morgan, VP of Data Science at Lincoln Financial Group, joins me to unpack what a real data culture looks like inside a complex, highly regulated business that has policies on the books for decades. We talk about how to turn Gen AI buzz into real value, why governance and quality suddenly matter to everyone, and how to tackle data technical debt without stalling delivery.Chris shares concrete ways he finds champions in the business, balances centralized and federated models, and keeps stakeholders excited about the future while he quietly fixes the messy data foundation underneath it all.Key takeawaysData culture is less about dashboards and more about curiosity, repeatable processes, and raising the analytical watermark across the company, not just in the data team.The teams that will win with Gen AI are the ones that can safely connect proprietary data to these models, which demands strong governance, clear definitions, and shared standards.A blended model works best for scaling data work, where a central function sets guardrails and standards while domain teams stay close to the business and own local decisions.Paying down technical debt works when it is framed in business terms, tied to revenue and risk, and treated as a regular slice of capacity instead of a one time side project.Education is now part of the job for data leaders, from internal road shows on Gen AI to simple stories that explain why foundational data work matters before you can ship shiny tools.Timestamped highlights00:04 Setting the stage Chris explains his role at Lincoln Financial and how data science supports life and annuity products that can live for decades.03:33 The Cobb salad story A simple grocery store analogy that makes data standards and shared definitions instantly clear to non technical stakeholders.06:06 Finding the right champions Why Chris prefers curious partners who will invest time with the data team over senior leaders who just want results without changing behavior.08:33 Governance as Gen AI fuel How regulatory pressure and the need to trust what goes into models are pushing data governance and quality into the spotlight.11:11 A practical way to attack data technical debt How Chris decides what to fix first, and why he tries to reserve a steady slice of team time for cleanup so progress is visible and sustainable.17:44 Managing Gen AI expectations From road shows to constant communication, Chris shares how he keeps enthusiasm high while also being honest about the timeline and effort.One line that sums it up“These generative models are going to become a commodity and what will separate companies is who can take the most advantage of their proprietary data.”Practical playbookStart small with data culture by picking one engaged business partner, one problem, and one outcome you can measure clearly.Reserve a consistent portion of team capacity for technical debt, even if it is only a small percentage at first, and make the tradeoffs visible.Use stories, analogies, and simple rules of the road so stakeholders can understand how data systems work without becoming experts in the tech.Call to actionIf this conversation helped you think differently about data culture and Gen AI inside your company, follow the show and leave a rating so more engineering and data leaders can find it. To keep the discussion going, connect with me on LinkedIn and share how your team is tackling data culture and technical debt right now.

Can AI trade FX? Is the future 'agentic'? And are we seeing a bubble? Mark McDonald, Head of AI and Data Science, and Yuning Bai, Data Scientist, discuss recent developments in artificial intelligence.Click here for appropriate Disclosures, including analyst certifications, and Disclaimers that must be viewed with this podcast: https://www.research.hsbc.com/R/101/DkgSvdVStay connected and access free to view reports and videos from HSBC Global Investment Research follow us on LinkedIn https://www.linkedin.com/feed/hashtag/hsbcresearch/ or click here: https://www.gbm.hsbc.com/insights/global-research.

Can AI and human creativity truly coexist—or are we watching the beginning of the end for original artistry? In this episode of Today in Tech, host Keith Shaw dives deep into the future of visual content with Allesandra Sala, Shutterstock's Head of AI and Data Science. Together, they explore how generative AI is transforming the creative industry — from image perfection and stock photography disruption to copyright chaos, ethical dilemmas, and artistic identity. Discover: Why Shutterstock chose to embrace, not resist, generative AI How AI-generated content is both exciting and dangerously generic The ongoing legal battle over AI authorship and content ownership How artists can stay relevant (and possibly even thrive) with AI What ethical guardrails and transparency measures are needed now Whether a backlash to “too perfect” imagery is already underway Follow TECH(talk) for the latest tech news and discussion!

A lot of people building software today never took the traditional CS path. They arrived through curiosity, a job that needed automating, or a late-night itch to make something work. This week, David Kopec joins me to talk about rebuilding computer science for exactly those folks, the ones who learned to program first and are now ready to understand the deeper ideas that power the tools they use every day. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON NordStellar Talk Python Courses Links from the show David Kopec: davekopec.com Classic Computer Science Book: amazon.com Computer Science from Scratch Book: computersciencefromscratch.com Computer Science from Scratch at NoStartch (CSFS30 for 30% off): nostarch.com Watch this episode on YouTube: youtube.com Episode #529 deep-dive: talkpython.fm/529 Episode transcripts: talkpython.fm Theme Song: Developer Rap

This episode examines a new CNA model that can help government officials optimally deploy unmanned systems, and how it overlaps with our existing tools. Guest Biographies Arpita Verma is an expert in optimization, modeling, and simulation in CNA's Data Science for Production Program. John Crissman is an expert in artificial intelligence (AI), machine learning (ML) and natural language processing in CNA's Center for Data Management Analytics. Rebekah Yang is an expert in artificial intelligence and machine learning (AI/ML) for FAA NextGen and a specialist in data visualization and modeling in CNA's Center for Data Management Analytics. Further Reading UAS Cooperative Airspace Traffic Simulation (UCATS) First Responder Awareness Monitoring during Emergencies (FRAME) 

Kenneth Hochhauser is Partner and Head of Data and Analytics at RTL. His background includes roles as a retail executive at Macy's and GNC and as a small business and economic development officer for the City of New York. He has advised both tenants and landlords on site selection, trade area analysis, and retail strategy, including introducing Chipotle to the New York metro market and representing Duxiana nationally. His past assignments span major projects such as Brookfield Place, Trump Place, and Columbia University's Manhattanville and Morningside campuses.(02:39) - Ken's Journey(04:59) - Retail Market Trends(06:05) - Retail vs. Office Innovation(07:53) - Shopping Trends and Retail Insights(08:31) - Retail Challenges in Manhattan(10:05) - Retail's Historical Context and Future(12:14) - Tenant Preferences(17:33) - Experiential Retail & Unique Locations(20:56) - Non-Traditional Retail (23:21) - Feature: Blueprint - The Future of Real Estate - Register for 2026: The Premier Event for Industry Executives, Real Estate & Construction Tech Startups and VC's, at The Venetian, Las Vegas on September 22nd-24th, 2026. As a friend of Tangent, you can save $300 on your All-Access pass(28:11) - Retail Tech & Data Utilization(34:29) - Location Indicators & Retail Expansion(38:29) - Collaboration Superpower: an economist(40:08) - US Gov. Shutdown Impact

Join us for a special farewell episode of the Alter Everything Podcast as we celebrate the impactful journey of host Megan Bowers. In this episode, Megan reflects on her career in data analytics, her experiences at Alteryx, and the evolution of the podcast. Discover insights on building a personal brand, the importance of networking in the data industry, and the future of data science and AI. Hear memorable stories from past episodes, expert interviews, and practical advice for data professionals. Panelists: Michael Cusic, Sr. Learning Experience Designer @ Alteryx - @mikecusic, LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Alteryx Community Blog ContentEpisode 194: AI and Data PipelinesEpisode 134: Building Trust in AI with FiddlerEpisode 140: Using Alteryx to Understand Climate ChangeAlteryx ACE program 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.

Activities outside of data science can strengthen the very skills data scientists need for their careers in surprising ways. From improving stakeholder communication to learning how to work with resistance rather than against it, hobbies and interests often teach lessons that directly translate to professional effectiveness.In this Value Boost episode, Colin Priest joins Dr. Genevieve Hayes to explore how unexpected hobbies and activities can make you a more effective data scientist and enhance your career.You'll discover:How dancing skills translate into better stakeholder presentations [02:02]What swimming teaches about working with resistance [06:30]Why coaching swimmers improves communication with non-technical colleagues [08:10]The simple activity anyone can try to expand their data science thinking [11:03]Guest BioColin Priest is an actuary, data scientist and educator who has held several CEO and general management roles where he has championed data-driven initiatives. He now lectures at UNSW, where he specialises in adapting education for the age of AI.LinksConnect with Colin on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

We have been sold a story of complexity. Michael Kennedy (Talk Python) argues we can escape this by relentlessly focusing on the problem at hand, reducing costs by orders of magnitude in software, data, and AI.In this episode, Michael joins Hugo to dig into the practical side of running Python systems at scale. They connect these ideas to the data science workflow, exploring which software engineering practices allow AI teams to ship faster and with more confidence. They also detail how to deploy systems without unnecessary complexity and how Agentic AI is fundamentally reshaping development workflows.We talk through:- Escaping complexity hell to reduce costs and gain autonomy- The specific software practices, like the "Docker Barrier", that matter most for data scientists- How to replace complex cloud services with a simple, robust $30/month stack- The shift from writing code to "systems thinking" in the age of Agentic AI- How to manage the people-pleasing psychology of AI agents to prevent broken code- Why struggle is still essential for learning, even when AI can do the work for youLINKSTalk Python In Production, the Book! (https://talkpython.fm/books/python-in-production)Just Enough Python for Data Scientists Course (https://training.talkpython.fm/courses/just-enough-python-for-data-scientists)Agentic AI Programming for Python Course (https://training.talkpython.fm/courses/agentic-ai-programming-for-python)Talk Python To Me (https://talkpython.fm/) and a recent episode with Hugo as guest: Building Data Science with Foundation LLM Models (https://talkpython.fm/episodes/show/526/building-data-science-with-foundation-llm-models)Python Bytes podcast (https://pythonbytes.fm/)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Watch the podcast video on YouTube (https://youtube.com/live/jfSRxxO3aRo?feature=share)Join the final cohort of our Building AI Applications course starting Jan 12, 2026 (35% off for listeners) (https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav): https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

As part of the GIRO 2025 mini-series, we speak to Geraldine Finucane and Hazel Davis from the working party on Professionalism, Regulation and Ethics for Actuaries in Data Science. We discuss what effective governance can look like in practice and how to assess whether your firm's approach supports meaningful oversight.

Thanks to Prosus Group for collaborating on the Agents in Production Virtual Conference 2025.Abstract //The discussion centers on highly technical yet practical themes, such as the use of advanced post-training techniques like Direct Preference Optimization (DPO) and Parameter-Efficient Fine-Tuning (PEFT) to ensure LLMs maintain stability while specializing for e-commerce domains. We compare the implementation challenges of Computer-Using Agents in automating legacy enterprise systems versus the stability issues faced by conversational agents when inputs become unpredictable in production. We will analyze the role of cloud infrastructure in supporting the continuous, iterative training loops required by Reinforcement Learning-based agents for e-commerce!Bio // Paul van der Boor (Panel Host) //Paul van der Boor is a Senior Director of Data Science at Prosus and a member of its internal AI group.Arushi Jain (Panelist) // Arushi is a Senior Applied Scientist at Microsoft, working on LLM post-training for Computer-Using Agent (CUA) through Reinforcement Learning. She previously completed Microsoft's competitive 2-year AI Rotational Program (MAIDAP), building and shipping AI-powered features across four product teams.She holds a Master's in Machine Learning from the University of Michigan, Ann Arbor, and a Dual Degree in Economics from IIT Kanpur. At Michigan, she led the NLG efforts for the Alexa Prize Team, securing a $250K research grant to develop a personalized, active-listening socialbot. Her research spans collaborations with Rutgers School of Information, Virginia Tech's Economics Department, and UCLA's Center for Digital Behavior.Beyond her technical work, Arushi is a passionate advocate for gender equity in AI. She leads the Women in Data Science (WiDS) Cambridge community, scaling participation in her ML workshops from 25 women in 2020 to 100+ in 2025—empowering women and non-binary technologists through education and mentorship.Swati Bhatia //Passionate about building and investing in cutting-edge technology to drive positive impact.Currently shaping the future of AI/ML at Google Cloud.10+ years of global experience across the U.S, EMEA, and India in product, strategy & venture capital (Google, Uber, BCG, Morpheus Ventures).Audi Liu //I'm passionate about making AI more useful and safe.Why? Because AI will be ubiquitous in every workflow, powering our lives just like how electricity revolutionized our society - It's pivotal we get it right.At Inworld AI, we believe all future software will be powered by voice. As a Sr Product Manager at Inworld, I'm focused on building a real-time voice API that empowers developers to create engaging, human-like experiences. Inworld offers state-of-the-art voice AI at a radically accessible price - No. 1 on Hugging Face and Artificial Analysis, instant voice cloning, rich multilingual support, real-time streaming, and emotion plus non-verbal control, all for just $5 per million characters.Isabella Piratininga //Experienced Product Leader with over 10 years in the tech industry, shaping impactful solutions across micro-mobility, e-commerce, and leading organizations in the new economy, such as OLX, iFood, and now Nubank. I began my journey as a Product Owner during the early days of modern product management, contributing to pivotal moments like scaling startups, mergers of major tech companies, and driving innovation in digital banking.My passion lies in solving complex challenges through user-centered product strategies. I believe in creating products that serve as a bridge between user needs and business goals, fostering value and driving growth. At Nubank, I focus on redefining financial experiences and empowering users with accessible and innovative solutions.

In October 2025, Orbition Group hosted Driven by Data LIVE, where they welcomed 120 CDOs/Data Leaders to Tobacco Dock in London for a half day event, which included 3 panel discussions and some roundtable conversations.The third panel discussion saw Catherine King joined by;Paul Hollands, Chief Data & AI Officer, AXA,Johanna Hutchinson, Chief Data Officer, BAE SystemsNick Blewden, Director of Data, CoopSam Hancock, Global Head of Data Science, Octopus EnergyWhere they discussed, Leading with a Legacy of Impact, How Data can Change the World, which includes; How to distinguish between short-term value creation and long-term impact when shaping your data strategy.The decisions made that will outlive tenure and define data leadership legacy.The boldest thing a CDO should be doing today, but most aren't.The people-first principles that anchor leadership when making high-stakes data decisions.Experiencing the “this is why we do it” moment and the times when data work had a direct, unforgettable human impact.Thanks to our sponsor, Data Literacy Academy.Data Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data literacy first.At Data Literacy Academy, we don't just teach data skills. We empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. We're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that we offer. We embed a deep data culture shift through a transformative change management programme.We take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.

Topics covered in this episode: Advent of Code starts today Django 6 is coming Advanced, Overlooked Python Typing codespell 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: Advent of Code starts today A few changes, like 12 days this year, which honestly, I'm grateful for. See also: elf: Advent of Code CLI helper for Python Michael #2: Django 6 is coming Expected December 2025 Django 6.0 supports Python 3.12, 3.13, and 3.14 Built-in support for the Content Security Policy (CSP) standard is now available, making it easier to protect web applications against content injection attacks such as cross-site scripting (XSS). The Django Template Language now supports template partials, making it easier to encapsulate and reuse small named fragments within a template file. Django now includes a built-in Tasks framework for running code outside the HTTP request–response cycle. This enables offloading work, such as sending emails or processing data, to background workers. Email handling in Django now uses Python's modern email API, introduced in Python 3.6. This API, centered around the email.message.EmailMessage class Brian #3: Advanced, Overlooked Python Typing get_args, TypeGuard, TypeIs, and more goodies Michael #4: codespell Learned from this PR for the Talk Python book. Fix common misspellings in text files. It's designed primarily for checking misspelled words in source code (backslash escapes are skipped), but it can be used with other files as well. It does not check for word membership in a complete dictionary, but instead looks for a set of common misspellings. Therefore it should catch errors like "adn", but it will not catch "adnasdfasdf". It shouldn't generate false-positives when you use a niche term it doesn't know about. Extras Brian: Is mkdocs maintained? Hatch 1.16 Michael: Follow up on tach from Gerben Dekker: tach has been unmaintained for a bit but is not anymore. It was the main product from Gauge which is a Y combinator startup that pivoted to something unrelated and abandoned tach. However, https://github.com/DetachHead forked it but now got access to the main repo and has committed to maintaining it. ruff analyze graph is fully independent of tach - we actually started to look into alternatives for tach when it became unmaintained and then found ruff analyze graph. For our use case, with just a bit of manipulation on top of ruff analyze graph we replaced our use of deptry (which was slower - and I try to be careful depending on one-man projects). A Review of Michael Kennedy's book, “Talk Python in Production” - Thanks Doug Joke: NoaaS

Skills, tasks, jobs, activities. These terms get used interchangeably across HR and talent acquisition, but they mean fundamentally different things. Skills are attributes of people. Tasks are components of work. Jobs are bundles of activities.  Having clarity here matters more now than ever. As AI begins reshaping how work gets done, organisations need a precise understanding of their workforce at the task level. Without clear taxonomies, it becomes impossible to understand how to effectively implement AI for automation and augmentation. So how should companies be preparing to take the most advantage of the inevitable shifts AI will bring? My guest this week is Ben Zweig, CEO of Revelio Labs and author of the new book Job Architecture. In our conversation, he explains how to build effective taxonomies cheaply and scalably with LLMs and why this foundation is critical for navigating change. Ben also teaches Data Science and The Future of Work at NYU Stern and talks through an invaluable framework for assessing the likelihood of AI-driven job displacement. In the interview, we discuss: Why grouping people is the core of any HR analysis. What we get wrong about skills, jobs, tasks, and activities Why skills aren't the right unit of observation to analyse jobs AI automates tasks and activities, not jobs and skills. The vital importance of taxonomies Using LLMs to build taxonomies cost-effectively at scale. What are the advantages of doing this properly? The three forces that help measure the potential for AI-driven job displacement What does the future look like Follow this podcast on Apple Podcasts. Follow this podcast on Spotify.

In this episode, I'm talking with Vincent Warmerdam about treating LLMs as just another API in your Python app, with clear boundaries, small focused endpoints, and good monitoring. We'll dig into patterns for wrapping these calls, caching and inspecting responses, and deciding where an LLM API actually earns its keep in your architecture. Episode sponsors Seer: AI Debugging, Code TALKPYTHON NordStellar Talk Python Courses Links from the show Vincent on X: @fishnets88 Vincent on Mastodon: @koaning LLM Building Blocks for Python Co-urse: training.talkpython.fm Top Talk Python Episodes of 2024: talkpython.fm LLM Usage - Datasette: llm.datasette.io DiskCache - Disk Backed Cache (Documentation): grantjenks.com smartfunc - Turn docstrings into LLM-functions: github.com Ollama: ollama.com LM Studio - Local AI: lmstudio.ai marimo - A Next-Generation Python Notebook: marimo.io Pydantic: pydantic.dev Instructor - Complex Schemas & Validation (Python): python.useinstructor.com Diving into PydanticAI with marimo: youtube.com Cline - AI Coding Agent: cline.bot OpenRouter - The Unified Interface For LLMs: openrouter.ai Leafcloud: leaf.cloud OpenAI looks for its "Google Chrome" moment with new Atlas web browser: arstechnica.com Watch this episode on YouTube: youtube.com Episode #528 deep-dive: talkpython.fm/528 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Send Bidemi a Text Message!In this episode, host Bidemi Ologunde spoke with data scientist and AI/machine learning (ML) enthusiast Daria Dubovskaia in a wide-ranging conversation about cybersecurity, data analytics, and building robust ML systems in the real world. Daria shares her journey from studying rocket propulsion in Russia to moving to the United States, completing a Master's degree in Data Science at CUNY, and working at a healthcare startup in Tampa, Florida. Along the way, she talks about cleaning messy data, deploying production models in the cloud, protecting sensitive information, and communicating complex insights to non technical stakeholders. This episode is full of practical lessons for anyone interested in data-driven decision-making, career pivots into tech, and the growing intersection of machine learning and cybersecurity.Support the show

Annie Barrett, Vice President of Marketing for Adidas Originals, Basketball and Partnerships, and Sean Bruich, Senior Vice President of AI, Engineering and Data Science at Amgen, share insights on how iconic brand storytelling and rapidly evolving AI capabilities intersect to drive authenticity, collaboration, and transformative innovation across industries. Hosted on Acast. See acast.com/privacy for more information.

Eric Bradlow and Adi Wyner examine surprising NBA and NFL performance patterns while highlighting the innovative sports analytics research conducted by students—including advancements in expected-goals modeling, rugby decision analytics, and tennis serve evaluation—showcasing how data science and AI are reshaping modern sports analysis. Hosted on Acast. See acast.com/privacy for more information.

When most data scientists think about using LLMs and generative AI, the first thing that springs to mind is writing code faster. While that's certainly useful, if it's the only application you're exploring, you're missing some of the most powerful opportunities to enhance your effectiveness as a data scientist.In this episode, Colin Priest joins Dr. Genevieve Hayes to explore advanced LLM applications that go far beyond code generation, including techniques for processing unstructured data, improving stakeholder communication, and identifying blind spots in your analysis.You'll learn:How to use LLMs to extract structured insights from messy unstructured data [02:50]The role-playing technique that helps you practice difficult stakeholder conversations [14:12]Why using multiple LLMs helps reduce AI hallucinations [20:38]A step-by-step approach for integrating LLMs into your workflow safely [25:52]Guest BioColin Priest is an actuary, data scientist and educator who has held several CEO and general management roles where he has championed data-driven initiatives. He now lectures at UNSW, where he specialises in adapting education for the age of AI.LinksConnect with Colin on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Kevin Shtofman is the Global Head of Alliances and Corporate Development at Cherre, a real estate data platform powering over $3.3 trillion in AUM. With 20+ years of experience across real estate, finance, and consulting, Kevin leads global initiatives to integrate and contextualize data from systems, third parties, and JV partners, helping investors, operators, and asset managers make smarter decisions. At Cherre, he also oversees strategic partnerships, global expansion, and the innovation roadmap. Prior to joining Cherre, Kevin held leadership roles across the industry, including Chief Operating Officer at NavigatorCRE, and Global Real Estate Technology Strategy Lead at Deloitte, where he advised clients on emerging technologies like AI, automation, and blockchain. A recognized voice in real estate innovation, Kevin brings two decades of experience bridging data, operations, and technology across global real estate markets. Outside of work, Kevin is a golf enthusiast, occasional Ironman, and proud father of three daughters.(02:05) - Kevin's Background(05:19) - Challenges in Real Estate Data Management(06:52) - Cherre's Approach to Data Integration(13:48) - Evolution of Cherre's Platform(21:41) - Client Success Stories(24:58) - Future of Real Estate and AI(25:23) - Feature: Blueprint - The Future of Real Estate - Register for 2026: The Premier Event for Industry Executives, Real Estate & Construction Tech Startups and VC's, at The Venetian, Las Vegas on September 22nd-24th, 2026. As a friend of Tangent, you can save $300 on your All-Access pass(29:58) - Introducing Cherre AI Agent Marketplace(33:58) - AI Use Cases(40:06) - The Future of Real Estate Data(42:29) - Affordable Housing and Investment(47:37) - Collaboration Superpower: William Levitt (Wiki) & Larry Brown (Wiki)

Kelly discusses how ethical considerations influence machine learning, NLP, and data quality, and how organizations can integrate human-centered thinking into technical decision-making. They also share insights from their upcoming book, The Friendly Guide to Data Science, aimed at making the field accessible, ethical, and practical.Key Highlights:Ethical AI in Practice: How to incorporate ethics and human-centered principles into data science projects.Behavioral Economics & Decision-Making: How understanding human behavior informs AI and tech strategies.Making Data Science Accessible: Kelly's approach to mentoring, writing, and teaching the next generation of data scientists.

Topics covered in this episode: PEP 814 – Add frozendict built-in type From Material for MkDocs to Zensical Tach Some Python Speedups in 3.15 and 3.16 Extras Joke 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 #0: Black Friday is on at Talk Python What's on offer: An AI course mini bundle (22% off) 20% off our entire library via the Everything Bundle (what's that? ;) ) The new Talk Python in Production book (25% off) Brian: This is peer pressure in action 20% off The Complete pytest Course bundle (use code BLACKFRIDAY) through November or use save50 for 50% off, your choice. Python Testing with pytest, 2nd edition, eBook (50% off with code save50) also through November I would have picked 20%, but it's a PragProg wide thing Michael #1: PEP 814 – Add frozendict built-in type by Victor Stinner & Donghee Na A new public immutable type frozendict is added to the builtins module. We expect frozendict to be safe by design, as it prevents any unintended modifications. This addition benefits not only CPython's standard library, but also third-party maintainers who can take advantage of a reliable, immutable dictionary type. To add to existing frozen types in Python. Brian #2: From Material for MkDocs to Zensical Suggested by John Hagen A lot of people, me included, use Material for MkDocs as our MkDocs theme for both personal and professional projects, and in-house docs. This plugin for MkDocs is now in maintenance mode The development team is switching to working on Zensical, a static site generator to overcome some technical limitations with MkDocs. There's a series of posts about the transition and reasoning Transforming Material for MkDocs Zensical – A modern static site generator built by the creators of Material for MkDocs Material for MkDocs Insiders – Now free for everyone Goodbye, GitHub Discussions Material for MkDocs still around, but in maintenance mode all insider features now available to everyone Zensical is / will be compatible with Material for Mkdocs, can natively read mkdocs.yml, to assist with the transition Open Source, MIT license funded by an offering for professional users: Zensical Spark Michael #3: Tach Keep the streak: pip deps with uv + tach From Gerben Decker We needed some more control over linting our dependency structure, both internal and external. We use tach (which you covered before IIRC), but also some home built linting rules for our specific structure. These are extremely easy to build using an underused feature of ruff: "uv run ruff analyze graph --python python_exe_path .". Example from an app I'm working on (shhhhh not yet announced!) Brian #4: Some Python Speedups in 3.15 and 3.16 A Plan for 5-10%* Faster Free-Threaded JIT by Python 3.16 5% faster by 3.15 and 10% faster by 3.16 Decompression is up to 30% faster in CPython 3.15 Extras Brian: LeanTDD book issue tracker Michael: No. 4 for dependencies: Inverted dep trees from Bob Belderbos Joke: git pull inception

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.

LinkedIn has become a powerful career tool for data scientists willing to invest the time. Regular posting can lead to unexpected work opportunities, reconnections with former colleagues, and valuable networking with professionals worldwide. But making the leap from occasional posting to consistent content creation can feel overwhelming.In this Value Boost episode, Sarah Burnett joins Dr. Genevieve Hayes to share practical LinkedIn strategies that can transform your data science career.In this episode, you'll discover:How Sarah went from posting twice a year to daily LinkedIn content [01:25]The biggest benefits of consistent LinkedIn posting for data science careers [03:15]How to manage the challenge of daily content creation without burnout [04:31]The one LinkedIn strategy every data scientist should start using tomorrow [08:47]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

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!

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."

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

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