In this Supercomputing edition of Data Unchained, host Molly Presley is joined live from the St. Louis Convention Center by Ari Berman, former Founder and CEO of Fireteam and current member of the Starfish team. The conversation explores the growing convergence of high performance computing, AI, and large scale data management, with a focus on unstructured data visibility, global file systems, and shared data stewardship across science, life sciences, and enterprise environments. Ari and Molly discuss why knowing what data you have is foundational to innovation, how organizations can reduce silos, and how platforms like Starfish and Hammerspace work together to enable discovery, collaboration, and smarter use of data at scale. Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US Hosted on Acast. See acast.com/privacy for more information.

In this episode of Data Unchained, host Molly Presley is joined by Carl Watts of the Library of Congress for a deep dive into what it takes to manage and preserve one of the largest and most complex data environments in the world. Carl shares firsthand insight into overseeing more than 150 petabytes of historical data, navigating large scale tape migrations, and confronting the governance, copyright, and operational challenges that come with applying AI to national archives. The conversation explores whether artificial intelligence can responsibly unlock siloed collections across text, audio, video, and web archives, and what it truly costs to move, protect, and future proof America’s digital memory at petabyte scale. Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US Hosted on Acast. See acast.com/privacy for more information.

Before StatQuest became the go-to learning companion for millions of AI and ML practitioners… Before the “BAM! Double BAM! Triple BAM!” became a teaching tool that many learners adore...

There was just one guy in a genetics lab, trying desperately to explain his data analysis to coworkers so they didn't think he was working magic.

In this deeply personal and inspiring episode, Joshua Starmer (CEO & Founder | StatQuest) shares the real story behind his rise — a journey shaped by strategy, struggle, blunt feedback, and a relentless desire to make complicated ideas simple.

What you’ll discover: 🔹How Josh went from helping colleagues in a genetics lab to becoming a renowned educator, treasuring his first 9 views and 2 subscribers as a big win. 🔹How early feedback Josh received as a kid became a quiet spark — motivating him to improve how he explained things and ultimately shaping the teaching style millions now rely on. 🔹How his method for breaking down complex topics with unique tools like his iconic BAM! help make learning lighter and less intimidating. 🔹His thoughts on AI tutors, avatars, and interactive learning and how ethics, bias, and hallucinations relate to next-gen learning.

This is more than a conversation about statistics, data science, AI, education, or YouTube. It’s the story of a researcher who never imagined starting a learning platform, yet became one of the most trusted teachers in statistics and machine learning—turning frustration into clarity, confusion into curiosity, and small beginnings into a massive global impact.

📌 If you’ve ever struggled with PCA, logistic regression, K-means clustering, neural networks, or any tricky stats and ML concepts… chances are StatQuest made it click. Now, hear from the creator himself about what goes on behind the scenes. Now you’ll finally understand how he made it click.

🔹A must-listen for: AI/ML learners, data scientists, educators, content creators, self-taught enthusiasts, and anyone who’s faced the fear of “I’m not good at explaining things.”Prepare to walk away inspired — and with a renewed belief that clarity is a superpower anyone can learn.

It's Friday! Matt Housley and I catch up to discuss the aftermath of AWS re:Invent and why the industry’s obsession with AI Agents might be premature. We also dive deep into the hardware wars between Google and NVIDIA , the "brain-damaged" nature of current LLMs , and the growing "enshittification" of the internet and platforms like LinkedIn. Plus, I reveals some details about my upcoming "Mixed Model Arts" project.

Most organisations don't struggle with change because of strategy or technology, they struggle because change is fundamentally human. In this episode of Hub & Spoken, Jason Foster, CEO & Founder of Cynozure, speaks with Sunil Kumar, Chief Transformation Officer, to explore why transformation so often stalls and what leaders can do to make it stick. Drawing on more than 26 years working across airlines, telecoms, finance and FMCG, Sunil explains why context, such as geopolitics, customer behaviour, industry shifts and internal culture, is the deciding factor in how change lands. When leaders ignore that context, resistance and fatigue follow. Jason and Sunil discuss the human realities behind change, including: Why people naturally resist it How values and beliefs influence adoption Why narrative and excitement matter more than familiar project metrics Sunil also shares his practical "push, pull, connect" model for building momentum and why adoption, not go-live, should be the true measure of success. 🎧 Listen to the full episode now Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023 and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024. Cynozure is a certified B Corporation. 

In this Supercomputing 25 edition of Data Unchained, host Molly Presley talks with Hunter Hagewood of Vanderbilt University’s ACCRE research computing center about the urgent storage and performance challenges that pushed Vanderbilt to rethink its entire HPC strategy. Hunter explains how procurement delays, faculty chargeback pressures, millions of small medical imaging files, manual GPU data staging, and rapidly growing research datasets created a breaking point that traditional appliance based storage could not solve. He shares how Hammer Space helped eliminate the 20 percent of workloads that were bottlenecking the cluster, enabled modular growth at scale, improved GPU performance for AI driven workflows, and positioned Vanderbilt to save nearly two million dollars over four years while supporting future research expansion. Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US Hosted on Acast. See acast.com/privacy for more information.

Brought to You By: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Linear ⁠ — ⁠ The system for modern product development. — Michelle Lim joined Warp as engineer number one and is now building her own startup, Flint. She brings a strong product-first mindset shaped by her time at Facebook, Slack, Robinhood, and Warp. Michelle shares why she chose Warp over safer offers, how she evaluates early-stage opportunities, and what she believes distinguishes great founding engineers. Together, we cover how product-first engineers create value, why negotiating equity at early-stage startups requires a different approach, and why asking founders for references is a smart move. Michelle also shares lessons from building consumer and infrastructure products, how she thinks about tech stack choices, and how engineers can increase their impact by taking on work outside their job descriptions. If you want to understand what founders look for in early engineers or how to grow into a founding-engineer role, this episode is full of practical advice backed by real examples — Timestamps (00:00) Intro (01:32) How Michelle got into software engineering  (03:30) Michelle’s internships  (06:19) Learnings from Slack  (08:48) Product learnings at Robinhood (12:47) Joining Warp as engineer #1 (22:01) Negotiating equity (26:04) Asking founders for references (27:36) The top reference questions to ask (32:53) The evolution of Warp’s tech stack  (35:38) Product-first engineering vs. code-first (38:27) Hiring product-first engineers  (41:49) Different types of founding engineers  (44:42) How Flint uses AI tools  (45:31) Avoiding getting burned in founder exits (49:26) Hiring top talent (50:15) An overview of Flint (56:08) Advice for aspiring founding engineers (1:01:05) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Thriving as a founding engineer: lessons from the trenches • From software engineer to AI engineer • AI Engineering in the real world • The AI Engineering stack — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

In this episode, I sit down with Mark Freeman and Chad Sanderson (Gable.ai) to discuss the release of their new O’Reilly book, Data Contracts: Developing Production-Grade Pipelines at Scale. They dive deep into the chaotic journey of writing a 350-page book while simultaneously building a venture-backed startup. The conversation takes a sharp turn into the evolution of Data Contracts. While the concept started with data engineers, Mark and Chad explain why they pivoted their focus to software engineers. They argue that software engineers are facing a "Data Lake Moment, "prioritizing speed over craftsmanship, resulting in massive technical debt and integration failures.

Gable: https://www.gable.ai/

A carreira de AI Engineer se consolidou como uma das mais disputadas do mercado de tecnologia. Mas afinal, o que realmente é esperado desse profissional na prática? Neste episódio do Data Hackers, discutimos em profundidade o caminho para se tornar um AI Engineer, analisando as principais habilidades técnicas, as diferenças em relação a outros cargos da área de dados e engenharia, a formação acadêmica versus experiência prática, a rotina nas empresas e o impacto da IA Generativa, RAG e AI Agents no dia a dia da função. Para enriquecer o debate, utilizamos dados da pesquisa State of Data Brazil como base para entender o cenário atual do mercado brasileiro, identificar tendências de demanda por habilidades, perfis profissionais mais buscados e os principais desafios enfrentados por quem deseja ingressar ou evoluir nessa carreira. Se você quer migrar para IA, se preparar para oportunidades reais ou entender se esse é o próximo passo profissional em dados, este episódio é para você. Não se esqueça de preencher a pesquisa State of Data Brazil: https://www.stateofdata.com.br/

Nossa Bancada Data Hackers: Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart.Gabriel Lages — Co-founder da Data Hacker e Diretor de Dados & AI da Hotmart

Send us a text Hit replay on this high-energy conversation with Ben Lowe, Founder and CEO of Lighthouse Technology, as he breaks down what it really takes to build something from scratch in a fast-changing AI world. From finding the right co‑founder and stepping into the CEO role, to navigating the toughest challenges of entrepreneurship, Ben shares practical lessons and hard‑won insights for builders at every stage. Ben also lifts the curtain on Lighthouse Technology’s mission at the intersection of cloud modernisation, cost optimisation, and Agentic AI, and how enterprises can turn AI hype into outcomes with the right GTM motion and architecture. If you care about innovation, AI for the enterprise, or your own startup journey, this replay is packed with career gold, strategic perspective, and a candid look at what’s next. 01:34 Finding a Entrepreneurial Partner 04:43 The Creation of Lighthouse 08:10 Becoming the CEO 09:34 The Biggest Challenge of Entrepreneurship 11:30 Lighthouse Technology 19:07 Agentic AI 20:16 The Lighthouse Trajectory 22:12 The GTM 31:39 Reseller versus Innovator 36:53 AI for the Enterprise 41:35 The 2-Min Pitch 43:30 What's True?LinkedIn: https://www.linkedin.com/in/lowebenjamin/?originalSubdomain=uk Website: https://lighthousetechnology.ai/

Entrepreneurship #AgenticAI #TechLeadership #Innovation #BusinessGrowth #CareerAdvice #AIForEnterprise #StartupJourney #MakingDataSimple #LighthouseTechnology #PodcastReplay

Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

How does a one-millimetre worm help win four Nobel Prizes? In this episode, we explore how C. elegans became one of the most influential organisms in modern biology — not because of its size, but because of its community.

Researchers, beginning with Sydney Brenner’s vision, built an ecosystem of radical openness: shared strains, shared annotations, shared tools, shared knowledge. This culture powered breakthroughs in apoptosis, GFP, RNA interference, and microRNAs, each recognised with a Nobel Prize.

We discuss how the CGC, WormBase, WormAtlas, open imaging libraries, and collaborative genetics transformed a tiny worm into a global scientific powerhouse. It’s the story of a field that chose to share — and in doing so, changed biology.

Key themes: • The collaborative backbone behind worm research • Why sharing strains and data accelerated Nobel-winning discoveries • How open tools shaped genetics, neuroscience, and ageing research • The social and scientific architecture of a uniquely supportive community • Why C. elegans is still leading modern multi-omics and connectomics

Based on the research article:🎧 Subscribe to the WOrM Podcast “From nematode to Nobel: How community-shared resources fueled the rise of Caenorhabditis elegans as a research organism” Victor R. Ambros, Martin Chalfie, Aric L. Daul, Andrew Z. Fire, David H. Hall, H. Robert Horvitz, Craig C. Mello, Gary Ruvkun, Nathan E. Schroeder, Paul W. Sternberg & Ann E. Rougvie. PNAS (2025) 🔗 https://doi.org/10.1073/pnas.2522808122

🎧 Subscribe to the WOrM Podcast Whole-organism stories from molecules to behaviour.

This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch.

📩 More info: 🔗 ⁠⁠www.veerenchauhan.com⁠⁠ 📧 [email protected]

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! I unpacked the 7 biggest myths circulating in the data career space and reveal what actually matters when trying to land your first role. 💌 Join 30k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com//interviewsimulator ⌚ TIMESTAMPS 00:00 – Myth #1: “Data Analyst is the only title” 02:00 – Myth #2: “You have zero data experience” 04:23– Myth #3: “You need to learn ALL the data skills” 06:03 – Myth #4: “You need a degree or certificate” 07:40 – Myth #5: “Data jobs are easy to land” 09:14– Myth #6: “Most data jobs are remote” 11:08 – Myth #7: “AI is going to take your data job” 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website

--- Mayor Renard Johnson’s participation in the Bloomberg Harvard City Leadership Initiative offers an example of the power of data storytelling.

--- Today, Bloomberg Center for Government Excellence Executive Director Oliver Wise talks to Mayor Renard Johnson of El Paso, Texas, a participant in the Bloomberg Harvard City Leadership Initiative. The city finds itself at the center of two national conversations: immigration and AI infrastructure. The mayor is using data to broaden prevailing narratives about El Paso and elevate the social and economic opportunity the metropolitan region has to offer.

--- Learn more about GovEx! --- Fill out our listener survey

The AI landscape is evolving at breakneck speed, with new capabilities emerging quarterly that redefine what's possible. For professionals across industries, this creates a constant need to reassess workflows and skills. How do you stay relevant when the technology keeps leapfrogging itself? What happens to traditional roles when AI can increasingly handle complex tasks that once required specialized expertise? With product-market fit becoming a moving target and new positions like forward-deployed engineers emerging, understanding how to navigate this shifting terrain is crucial. The winners won't just be those who adopt AI—but those who can continuously adapt as it evolves. Tomasz Tunguz is a General Partner at Theory Ventures, a $235m early-stage venture capital firm. He blogs at tomtunguz.com & co-authored Winning with Data. He has worked or works with Looker, Kustomer, Monte Carlo, Dremio, Omni, Hex, Spot, Arbitrum, Sui & many others. He was previously the product manager for Google's social media monetization team, including the Google-MySpace partnership, and managed the launches of AdSense into six new markets in Europe and Asia. Before Google, Tunguz developed systems for the Department of Homeland Security at Appian Corporation. In the episode, Richie and Tom explore the rapid investment in AI, the evolution of AI models like Gemini 3, the role of AI agents in productivity, the shifting job market, the impact of AI on customer success and product management, and much more. Links Mentioned in the Show: Theory VenturesConnect with TomTom’s BlogGavin Baker on MediumAI-Native Course: Intro to AI for WorkRelated Episode: Data & AI Trends in 2024, with Tom Tunguz, General Partner at Theory VenturesRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Send us a text We go inside Mediahuis to see how a small GenAI team is transforming newsroom workflows without losing editorial judgment. From RAG search to headline suggestions and text‑to‑video assists, this episode shares what works, what doesn’t, and how adoption spreads across brands. You’ll hear about: Ten priority use cases shipped across the groupHeadline and summary suggestions that boost clarity and speedRAG‑powered search turning archives into instant contextText‑to‑video tools that free up local video teamsThe hurdles of adoption, quality, and scaling prototypes into productionTheir playbook blends engineering discipline with editorial empathy: use rules where you can, prompt carefully when you must, and always keep journalists in the loop. We also cover policies, guardrails, AI literacy, and how to survive model churn with reusable templates and grounded tests. The result: a practical path to AI in media — protecting judgment, raising quality, and scaling tools without losing each brand’s voice. 🎧 If this sparks ideas for your newsroom or product team, follow the show, share with a colleague, and leave a quick review with your favorite takeaway.

In this talk, Xia He-Bleinagel, Head of Data & Cloud at NOW GmbH, shares her remarkable journey from studying automotive engineering across Europe to leading modern data, cloud, and engineering teams in Germany. We dive into her transition from hands-on engineering to leadership, how she balanced family with career growth, and what it really takes to succeed in today’s cloud, data, and AI job market.

TIMECODES: 00:00 Studying Automotive Engineering Across Europe 08:15 How Andrew Ng Sparked a Machine Learning Journey 11:45 Import–Export Work as an Unexpected Career Boos t17:05 Balancing Family Life with Data Engineering Studies 20:50 From Data Engineer to Head of Data & Cloud 27:46 Building Data Teams & Tackling Tech Debt 30:56 Learning Leadership Through Coaching & Observation 34:17 Management vs. IC: Finding Your Best Fit 38:52 Boosting Developer Productivity with AI Tools 42:47 Succeeding in Germany’s Competitive Data Job Market 46:03 Fast-Track Your Cloud & Data Career 50:03 Mentorship & Supporting Working Moms in Tech 53:03 Cultural & Economic Factors Shaping Women’s Careers 57:13 Top Networking Groups for Women in Data 1:00:13 Turning Domain Expertise into a Data Career Advantage

Connect with Xia- Linkedin - https://www.linkedin.com/in/xia-he-bleinagel-51773585/ - Github - https://github.com/Data-Think-2021 - Website - https://datathinker.de/

Connect with DataTalks.Club: - Join the community - https://datatalks.club/slack.html - Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ - Check other upcoming events - https://lu.ma/dtc-events - GitHub: https://github.com/DataTalksClub - LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

In this talk, Anusha Akkina, co-founder of Auralytix, shares her journey from working as a Chartered Accountant and Auditor at Deloitte to building an AI-powered finance intelligence platform designed to augment, not replace, human decision-making. Together with host Alexey from DataTalks.Club, she explores how AI is transforming finance operations beyond spreadsheets—from tackling ERP limitations to creating real-time insights that drive strategic business outcomes.

TIMECODES: 00:00 Building trust in AI finance and introducing Auralytix 02:22 From accounting roots to auditing at Deloitte and Paraxel 08:20 Moving to Germany and pivoting into corporate finance 11:50 The data struggle in strategic finance and the need for change 13:23 How Auralytix was born: bridging AI and financial compliance 17:15 Why ERP systems fail finance teams and how spreadsheets fill the gap 24:31 The real cost of ERP rigidity and lessons from failed transformations 29:10 The hidden risks of spreadsheet dependency and knowledge loss 37:30 Experimenting with ChatGPT and coding the first AI finance prototype 43:34 Identifying finance’s biggest pain points through user research 47:24 Empowering finance teams with AI-driven, real-time decision insights 50:59 Developing an entrepreneurial mindset through strategy and learning 54:31 Essential resources and finding the right AI co-founder

Connect with Anusha - Linkedin - https://www.linkedin.com/in/anusha-akkina-acma-cgma-56154547/ - Website - https://aurelytix.com/

Connect with DataTalks.Club: - Join the community - https://datatalks.club/slack.html - Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ - Check other upcoming events - https://lu.ma/dtc-events - GitHub: https://github.com/DataTalksClub - LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

At Qdrant Conference, builders, researchers, and industry practitioners shared how vector search, retrieval infrastructure, and LLM-driven workflows are evolving across developer tooling, AI platforms, analytics teams, and modern search research.

Andrey Vasnetsov (Qdrant) explained how Qdrant was born from the need to combine database-style querying with vector similarity search—something he first built during the COVID lockdowns. He highlighted how vector search has shifted from an ML specialty to a standard developer tool and why hosting an in-person conference matters for gathering honest, real-time feedback from the growing community.

Slava Dubrov (HubSpot) described how his team uses Qdrant to power AI Signals, a platform for embeddings, similarity search, and contextual recommendations that support HubSpot’s AI agents. He shared practical use cases like look-alike company search, reflected on evaluating agentic frameworks, and offered career advice for engineers moving toward technical leadership.

Marina Ariamnova (SumUp) presented her internally built LLM analytics assistant that turns natural-language questions into SQL, executes queries, and returns clean summaries—cutting request times from days to minutes. She discussed balancing analytics and engineering work, learning through real projects, and how LLM tools help analysts scale routine workflows without replacing human expertise.

Evgeniya (Jenny) Sukhodolskaya (Qdrant) discussed the multi-disciplinary nature of DevRel and her focus on retrieval research. She shared her work on sparse neural retrieval, relevance feedback, and hybrid search models that blend lexical precision with semantic understanding—contributing methods like Mini-COIL and shaping Qdrant’s search quality roadmap through end-to-end experimentation and community education.

Speakers

Andrey Vasnetsov Co-founder & CTO of Qdrant, leading the engineering and platform vision behind a developer-focused vector database and vector-native infrastructure. Connect: https://www.linkedin.com/in/andrey-vasnetsov-75268897/

Slava Dubrov Technical Lead at HubSpot working on AI Signals—embedding models, similarity search, and context systems for AI agents. Connect: https://www.linkedin.com/in/slavadubrov/

Marina Ariamnova Data Lead at SumUp, managing analytics and financial data workflows while prototyping LLM tools that automate routine analysis. Connect: https://www.linkedin.com/in/marina-ariamnova/

Evgeniya (Jenny) Sukhodolskaya Developer Relations Engineer at Qdrant specializing in retrieval research, sparse neural methods, and educational ML content. Connect: https://www.linkedin.com/in/evgeniya-sukhodolskaya/

Como é construir a próxima geração de sistemas inteligentes em uma das maiores operações de beleza do mundo — conectando engenharia de dados, IA generativa e agentes de IA em um ecossistema que já nasce preparado para o futuro? Neste episódio, conversamos com Felipe Gusmão Contratres, Marcel “Xiquin” Cecchin e Yasmim Vasconcelos, do Grupo Boticário, sobre como a evolução da engenharia de dados está permitindo a criação de AI Agents que vão muito além da análise. A conversa parte de uma pergunta essencial:como empresas que já dominam a IA generativa estão dando o próximo passo, habilitando agentes inteligentes a partir de uma nova infraestrutura de dados ? Falamos sobre a transformação da engenharia de dados, que deixou de focar apenas em relatórios e passou a operar como a fundação para sistemas avançados. Exploramos também como tecnologias que estão sendo utilizadas dentro do Grupo Boticário para habilitar produtos reais baseados em IA.

Nossa Bancada Data Hackers: Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart.Gabriel Lages — Co-founder da Data Hacker e Diretor de Dados & AI da Hotmart

Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conheça nossos comentaristas do Data Hackers News: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠Preencha a pesquisa State of Data Brazil: https://www.stateofdata.com.br/ Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠