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Episode Summary In this episode, we dive into the transformative power of synthetic data and its ability to bypass privacy barriers while accelerating AI innovation. Learn how industries like healthcare, finance, and retail leverage synthetic data to fuel progress and discover actionable steps to implement this game-changing technology. Key Topics Covered What Is Synthetic Data?Definition and importance.How it solves privacy and data scarcity challenges.Top 5 Breakthroughs in Synthetic Data:SafeSynthDP: Differential privacy for secure synthetic data generation.GANs for Healthcare: Generating synthetic patient records.CaPS: Collaborative synthetic data sharing across organizations.Private Text Data: Privacy-safe NLP dataset generation.Vertical Federated Learning: Secure synthetic data creation for tabular datasets.Applications Across Industries:Healthcare: HIPAA-compliant AI for diagnostics.Finance: Risk modeling with synthetic transaction data.Retail: Personalization using synthetic customer profiles.Action Plan:Learn and apply differential privacy techniques.Experiment with large language models for synthetic data.Use federated learning for collaborative data sharing.Build synthetic datasets for complex, messy data.Market privacy-first solutions to build customer trust.Resources Mentioned Research Papers:SafeSynthDP: Privacy-Preserving Data GenerationGANs for Healthcare DataCaPS: Collaborative Synthetic Data PlatformPrivate Predictions for NLPVertical Federated Learning for Tabular DataTools and Frameworks:TensorFlow Privacy LibraryPyTorch GAN ZooFlower Framework for Federated LearningTakeaways Synthetic data is not just a workaround—it’s a key enabler of privacy-compliant AI innovation.Industries across the board are adopting synthetic data to overcome regulatory and privacy challenges.You can start leveraging synthetic data today with available tools and frameworks.Ready to explore the power of synthetic data? Dive into the resources mentioned and start experimenting with synthetic data generation to give your AI strategy a competitive edge. Subscribe to our podcast for more cutting-edge insights into the world of AI and data innovation.

Website: https://mukundansankar.substack.com/

Are you tired of applying to job after job on LinkedIn only to hear absolutely nothing? Today I share the secret LinkedIn hack-- learn how the top 1% find fresh data roles and network directly with hiring managers, how to skip the oversaturated jobs tab, and dive into the ultimate guide to finding untouched opportunities. Visit https://www.premiumdatajobs.com/ We know job hunting sucks 😫 Let us do it for you! 🤝 ⌚ TIMESTAMPS 00:00 - Introduction 01:46 - Think Like the Top 1% of Job Seekers 03:22 - Step-by-Step Guide to Accessing Hidden Jobs 07:04 - PremiumDataJobs.com 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Learning AI Tools in Tableau

As businesses increasingly rely on data to drive decisions, the role of advanced analytics and AI in enhancing data interpretation is becoming crucial. For professionals tasked with optimizing data analytics platforms like Tableau, staying ahead of the curve with the latest tools isn't just beneficial—it's essential. This insightful guide takes you through the integration of Tableau Pulse and Einstein Copilot, explaining their roles within the broader Tableau and Salesforce ecosystems. Author Ann Jackson, an esteemed analytics professional with a deep expertise in Tableau, offers a step-by-step exploration of these tools, backed by real-world use cases that demonstrate their impact across various industries. By the end of this book, you will: Understand the functionalities of Tableau Pulse and Einstein Copilot and how to use them Learn to deploy Tableau Pulse effectively, ensuring it aligns with your business objectives Navigate discussions on AI's role within Tableau, enhancing your strategic conversations Visualize how Tableau Pulse operates through detailed images and scenarios Utilize Einstein Copilot in Tableau Desktop/Prep to streamline and enhance data analysis

2025 promises to be another transformative year for data and AI. From groundbreaking advancements in reasoning models to the rise of new challengers in generative AI, the field shows no signs of slowing down. Last week Jonathan and Martijn scored their 2024 predictions, and scored highly, but what's in store for 2025?  Building on the insights from their 2024 predictions, we'll assess the future of generative AI, the evolving role of AI in education, the growing importance of synthetic data, and much more. In the episode, Richie, Jo, and Martijn discuss whether OpenAI and Google will maintain their dominance or face disruption from new players like Meta’s Llama and XAI’s Grok, the implications of recent breakthroughs in AI reasoning, the rise of short-form video generation AI in social media and advertising, the challenges Europe faces in keeping pace with the US and China in AI innovation and much more. Links Mentioned in the Show: Data & AI Trends & Predictions 2025Skill Track: AI Business FundamentalsRelated Episode: Reviewing Our Data Trends & Predictions of 2024 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn TheuwissenRewatch sessions from RADAR: Forward Edition 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

Summary In this episode of the Data Engineering Podcast Andrew Luo, CEO of OneSchema, talks about handling CSV data in business operations. Andrew shares his background in data engineering and CRM migration, which led to the creation of OneSchema, a platform designed to automate CSV imports and improve data validation processes. He discusses the challenges of working with CSVs, including inconsistent type representation, lack of schema information, and technical complexities, and explains how OneSchema addresses these issues using multiple CSV parsers and AI for data type inference and validation. Andrew highlights the business case for OneSchema, emphasizing efficiency gains for companies dealing with large volumes of CSV data, and shares plans to expand support for other data formats and integrate AI-driven transformation packs for specific industries.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Andrew Luo about how OneSchema addresses the headaches of dealing with CSV data for your businessInterview IntroductionHow did you get involved in the area of data management?Despite the years of evolution and improvement in data storage and interchange formats, CSVs are just as prevalent as ever. What are your opinions/theories on why they are so ubiquitous?What are some of the major sources of CSV data for teams that rely on them for business and analytical processes?The most obvious challenge with CSVs is their lack of type information, but they are notorious for having numerous other problems. What are some of the other major challenges involved with using CSVs for data interchange/ingestion?Can you describe what you are building at OneSchema and the story behind it?What are the core problems that you are solving, and for whom?Can you describe how you have architected your platform to be able to manage the variety, volume, and multi-tenancy of data that you process?How have the design and goals of the product changed since you first started working on it?What are some of the major performance issues that you have encountered while dealing with CSV data at scale?What are some of the most surprising things that you have learned about CSVs in the process of building OneSchema?What are the most interesting, innovative, or unexpected ways that you have seen OneSchema used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on OneSchema?When is OneSchema the wrong choice?What do you have planned for the future of OneSchema?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links OneSchemaEDI == Electronic Data InterchangeUTF-8 BOM (Byte Order Mark) CharactersSOAPCSV RFCIcebergSSIS == SQL Server Integration ServicesMS AccessDatafusionJSON SchemaSFTP == Secure File Transfer ProtocolThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Recent data support our long-standing view for resilient growth, elevated sticky inflation, and constrained central bank easing. The known unknowns around US policy reinforce these views but are also raising financial and macro risks that have both a directional and timing uncertainty.

Speakers:

Bruce Kasman

Joseph Kasman

This podcast was recorded on 10 January 2025.

This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2025 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

A real-world workflow for building apps with AI: - Solutions for handling larger codebases - Strategies for avoiding common AI tool pitfalls - Honest talk about where these tools fall short - Sneak peek into where AI agents are heading Terrible humor, for which I refuse to apologize Space for you to share your own experiences - if you've found better ways to use these tools, steal the show at the end!

Sajjid Chinoy joins Nora Szentivanyi to discuss the outlook for EM Asian economies in 2025. The tech cycle upswing, underpinned by AI-related demand, has been crucial to the region’s resilience in 2024. While these tech tailwinds are likely to sustain, the regional outlook for 2025 is heavily clouded by a US-China Trade War 2.0. In contrast to the last US-China trade war, the rest of the region is more vulnerable this time around because activity is still much below the pre-pandemic path and the shock itself is likely to be more acute (potentially larger increase in tariffs with the transshipment escape-valve closed). Moreover, the policy space to respond – especially on fiscal – is more constrained this time. So the collateral damage on the region, while differentiated across countries, is likely to be larger than commonly presumed.

This podcast was recorded on January 09, 2025.

This communication is provided for information purposes only.  Institutional clients can view the related report at https://www.jpmm.com/research/content/GPS-4866513-0 for more information; please visit www.jpmm.com/research/disclosures for important disclosures.

© 2025 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

2024 was another huge year for data and AI. Generative AI continued to shape the way we work and interact with technology, with companies of all sizes racing to integrate AI into their products. We saw strides in tools like AI-enhanced data science notebooks, rapid adoption of generative image AI, and a steady march toward video generation AI. At the same time, foundational skills like AI literacy and data governance gained traction as critical areas for individuals and organizations to master. This time last year, DataCamp Co-Founders Jonathan and Martijn made a series of predictions and data and AI for 2024, today, they join Richie to reflect on their 2024 predictions and share their vision for data and AI in 2025. In the episode, Richie, Jonathan, and Martijn review the mainstream adoption of generative AI and its journey toward daily use, the rise of AI literacy as a critical skill, the growing overlap between data science and software engineering with the emergence of AI engineers, evolving trends in programming languages, how generative AI has moved from prototype to production, the near-mainstreaming of video generation AI, why AI hype continues to thrive and much more. Links Mentioned in the Show: Data & AI Trends & Predictions 2025Skill Track: AI Business FundamentalsRelated Episode: Data Trends & Predictions 2024 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn TheuwissenRewatch sessions from RADAR: Forward Edition 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 Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style! In this episode, we explore: OpenAI’s O3: Features, O1 Comparison, Release Date & more.Advent of Code: How LLMs performed on the 2024 coding challenges.DeepSeek V3: A breakthrough AI model developed for a fraction of GPT-4’s cost, yet rivaling top benchmarks.Shadow Workspace: How Cursor compares to Copilot with features like integrated models, documentation, and search.Bolt.new: Why it’s poised to revolutionize web app development with prompt-driven innovation.O1 Preview’s Chess Hack: When smarter means “cheater” in a fascinating experiment against Stockfish.Pydantic AI: A new tool bringing structure and intelligence to Python’s AI workflows.RightTyper: A tool to infer and apply type hints for cleaner, more efficient Python code.Doom: The Gallery Experience: A whimsical take on art appreciation in a retro gaming environment.Suno V4: The next-gen music generator, featuring "Bart, the Data Dynamo."Ghostty Terminal: The terminal emulator developers are raving about.

In this episode, host Jason Foster sits down with Barry Panayi, Chief Data and Insight Officer at John Lewis Partnership to discuss the evolving role of the Chief Data Officer (CDO). Barry shares his journey from coding and analytics to leading data and insights at iconic brands like John Lewis and Waitrose. He offers a unique perspective on how CDOs can transition from technical experts to strategic business leaders. Barry's candid reflections and actionable advice make this episode essential listening for data professionals, aspiring CDOs, and anyone interested in the intersection of data, technology, and business leadership. Don't miss this engaging and insightful conversation!    *****      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.  

Presentation of work from the paper A New Model of Computational Genomics, focusing on applying machine learning to genetics and other programming techniques to manage the large-scale data involved in modern genetics. Includes discussion of whole-genome mtDNA sequences, proteins, mRNA, and ATP synthase to provide a foundation for understanding contemporary topics in genetics.

Send us a text The podcast welcomes Manav Gupta, VP and CTO of IBM Canada. As a frequent collaborator on client visits, we discuss various aspects of large language models. So all things AI models.  02:04 Jumping Right into AI!02:59 Meet Manav Gupta08:17 Let's Talk All Things Models27:20 How to Choose the Right Models31:48 Where are the Models Going???46:01 How to Learn AILinkedin: linkedin.com/in/mgupta76 Website: https://www.ibm.com/granite 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 is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technology, business innovation, and leadership ... while keeping it simple & fun.

makingdatasimplepodcast #AI #LargeLanguageModels #TechLeadership #ArtificialIntelligence #IBMCanada #ManavGupta #AIInnovation #TechPodcast #AIModels #LearnAI

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.

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:

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Conheça nossos comentaristas do Data Hackers News:

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Paulo Vasconcellos

⁠Matérias/assuntos comentados:

Meta vai encher Instagram e Facebook com bots de IA:

Novo modelo da OpenAI (o3) revolta pesquisadores por falta de transparência.

Demais canais do Data Hackers:

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Dan Crosby, CEO and Founder of Legend Energy Advisors, joins Kirk once again to discuss the challenges at the intersection of energy demands and the data center industry, the rise of cloud computing and AI, and the importance of national security in energy independence.

For more about us: https://linktr.ee/overwatchmissioncritical

podcast_episode
by Val Kroll , Julie Hoyer , Tim Wilson (Analytics Power Hour - Columbus (OH) , Barr Moses (Monte Carlo) , Moe Kiss (Canva) , Michael Helbling (Search Discovery)

Every year kicks off with an air of expectation. How much of our Professional Life in 2025 is going to look a lot like 2024? How much will look different, but we have a pretty good idea of what the difference will be? What will surprise us entirely—the unknown unknowns? By definition, that last one is unknowable. But we thought it would be fun to sit down with returning guest Barr Moses from Monte Carlo to see what we could nail down anyway. The result? A pretty wide-ranging discussion about data observability, data completeness vs. data connectedness, structured data vs. unstructured data, and where AI sits from an input and an output and a processing engine. And more. Moe and Tim even briefly saw eye to eye on a thing or two (although maybe that was just a hallucination). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.