Data Neighbor Podcast

Data Neighbor Podcast
  • 5,0 (6)
  • TECHNOLOGIES
  • CHAQUE SEMAINE
Data Neighbor Podcast

Welcome to the Data Neighbor Podcast with Hai, Sravya, and Shane! We’re your friendly guides to the ever-evolving world of data. Whether you’re an aspiring data scientist, a data professional looking to grow your career, or just curious about how data shapes the world, you’re in the right place. Our mission? To help you break in or thrive in the field of data. We dive into: - Personal career journeys and how luck, opportunity, and grit play a role - How to break into the data field even with a non-traditional background - Industry insights through engaging conversations and expert interviews

  1. -19 H

    Ep20: How to Build an App in Minutes Using AI (Replit, Cursor, Bolt, V0, Lovable)

    AI is transforming how products are built—and with new AI prototyping tools like Replit, Cursor, V0, Bolt, and Lovable, it’s never been easier to go from idea to working app in minutes. In this episode of the Data Neighbor Podcast, we’re joined by Colin Matthews, founder of Tech for Product, to explore how product managers, data scientists, and engineers can build faster using AI.Colin teaches two of the top AI and technical fluency courses on Maven, writes the Tech for Product Substack, and helps builders leverage tools like Replit and GPT to build and test real products—fast.📚 Courses from Colin:AI Prototyping for Product Managers: https://maven.com/tech-for-product/ai-prototyping-for-product-managersTechnical Foundations for Product Managers: https://maven.com/tech-for-product/tech-fundamentals💼 Follow Colin Matthews:LinkedIn: https://www.linkedin.com/in/colinmatthews-pm/Substack: https://blog.techforproduct.com/Website: https://techforproduct.com/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Hai Guan: https://linkedin.openinapp.co/4qi1rSravya Madipalli: https://linkedin.openinapp.co/9be8cShane Butler: https://linkedin.openinapp.co/b02feIn this episode, we cover:- How to prototype real apps with AI (Replit, Cursor, Lovable, Bolt, V0)- How AI is changing the role of product managers and engineers- Replit demo: How we built a working data analysis app with zero code- AI vs traditional product development: What’s faster?- When these tools are actually production-ready- How product teams can use AI to validate ideas early- AI tools for data science, analytics, and product strategy- Best practices for debugging and building with AI agents- The real role of product-market fit in AI-first prototyping- How to avoid common mistakes when using AI tools- Colin’s advice for staying up to date with the fast-moving AI stackIf you work in AI, product, data science, machine learning, or tech strategy—or you’re a founder trying to get your idea off the ground—this episode is a goldmine of practical insights. From MVPs to experimentation platforms, we explore how AI is changing who gets to build, how fast they can do it, and what the future of product development looks like. #AI #AIPrototyping #ProductManagement #ProductDevelopment #DataScience #MachineLearning #AIApps #Replit #Cursor #V0 #Bolt #Lovable #AIForStartups #NoCodeAI #GPTApps #TechForProduct #MavenCourses #AICodingTools #BuildWithAI #AIProductStrategy #AIforPMs #RapidPrototyping #TechnicalFluency #LLMTools #AIStack #AIEngineering #DataTools #AIUX #Streamlit #OpenAI #AIAppDemo

    59 min

  2. -6 J

    Ep19: 4 Data-Driven Techniques to Secure Your Next Job in a Tough Market

    Are you struggling to land a data science role in today’s competitive market? In this episode, we sit down with Karun Thankachan, Senior Data Scientist at Walmart and former Amazon Applied Scientist, to break down the science of job searching - including actionable strategies to optimize your job applications, referrals, and LinkedIn outreach. Oh and we covered how recommendation systems are built and what all go into them in practice.What You’ll Learn in This Episode:- The metrics-driven approach to job searching: How many applications, referrals, and LinkedIn connections should you aim for?- How AI is changing hiring for both recruiters and candidates- The importance of networking and why it’s the most underrated skill in data science- A deep dive into recommender systems—how AI powers Netflix, Amazon, and Walmart recommendations- How LLMs (Large Language Models) are revolutionizing recommendation enginesIf you’re an aspiring data scientist, a mid-career professional looking for new opportunities, or just interested in how AI is reshaping hiring and recommender systems, this episode is packed with insights you don’t want to miss! Connect with Karun, Hai, Sravya, and Shane:Karun: https://www.linkedin.com/in/karunt/Hai: https://linkedin.openinapp.co/4qi1rSravya: https://linkedin.openinapp.co/9be8cShane: https://linkedin.openinapp.co/b02fe#DataScience #JobSearch #AIHiring #RecommenderSystems #MachineLearning #NetworkingTips #CareerGrowth #AI #Walmart #Amazon #DataNeighborPodcast #TechHiring

    1 h

  3. 16 MARS

    Ep18: Open-Source LLMs vs. ChatGPT: Which One Should You Use?

    AI is evolving faster than ever—and open-source AI models are catching up to proprietary models at an incredible pace. In this episode of the Data Neighbor Podcast, we sit down with Maarten Grootendorst, co-author of Hands-On Large Language Models with Jay Alammar, DeepLearning.AI instructor, and creator of BERTopic and KeyBERT, to break down the real differences between open-source and closed-source AI models.We’ll discuss how LLMs (Large Language Models) evolved from bag-of-words and Word2Vec to modern transformer-based models like BERT, GPT-4, DeepSeek, LLaMA 2, and Mixtral. More importantly, we explore when open-source AI models might actually be better than proprietary models from OpenAI, Google DeepMind, and Anthropic. Hands-On Large Language Models (Maarten’s Book): https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961DeepLearning.AI Course: How Transformer LLMs Work: https://www.deeplearning.ai/short-courses/how-transformer-llms-work/Maarten’s AI Newsletter: https://newsletter.maartengrootendorst.com/ Connect with us! Maarten Grootendorst: https://www.linkedin.com/in/mgrootendorst/Hai Guan: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya Madipalli: https://www.linkedin.com/in/sravyamadipalli/Shane Butler: https://www.linkedin.com/in/shaneausleybutler/

    1 h

  4. 10 MARS

    Ep17: How to Use AI and Build Data Science Teams

    In this episode of the Data Neighbor Podcast, we sit down with Justin Chen, Senior Director of Growth Data Science and former Head of Data Science & Engineering at Coinbase, to dive deep into how to build scalable, high-impact data teams. We get into the mind of a data leader who has built numerous reputable data organizations to understand principles, lessons, and challenges every company faces. Justin shares invaluable insights on:- Go slow to go fast – Why early-stage speed can create long-term inefficiencies.- The evolution of data science – How different orgs (growth, core, platform) function within companies.- The myth of the unicorn data scientist – Why hiring for specialization is key.- AI's impact on data science – How automation and AI tools are shaping the future of analytics.- Getting a seat at the table – How data professionals can move from support roles to strategic leadership.Justin also shares his firsthand experience of building an AI-powered data copilot at Coinbase to streamline analytics workflows, offering a sneak peek into how AI will shape the next generation of data teams. Whether you’re a data scientist, engineer, or aspiring leader, this conversation is packed with practical advice and industry wisdom you won’t want to miss! Connect with Justin: https://www.linkedin.com/in/mingc/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Hai: https://linkedin.openinapp.co/4qi1rSravya: https://linkedin.openinapp.co/9be8cShane: https://linkedin.openinapp.co/b02fe #DataScience #AI #aiengineering #TechLeadership #MachineLearning #GrowthDataScience #CareerAdvice #Analytics #Hiring #DataEngineering #DataDriven #DataTeams #TechPodcast #aiagents

    54 min

  5. 4 MARS

    Ep16: AI is Breaking the Internet - for Better or Worse

    AI is changing everything—including how we moderate content online. In this episode of the Data Neighbor Podcast, we sit down with Sugin Lou, a Staff Data Scientist at Cash App and former Nextdoor AI trust & safety expert, to discuss the challenges of AI content moderation, misinformation, and trust & safety in the era of LLMs.If you care about AI trust, AI policy, risk of AI, and AI governance, this episode is for you. More about this episode:What is content moderation? How does AI impact trust & safety? From Facebook moderation to moderation bots and comment moderation, companies rely on AI-powered moderation tools to detect AI-generated content, deepfakes, misinformation, and harmful speech. But is AI moderation really working, or is it just scaling misinformation at an unprecedented rate?AI Misinformation & Risk Management:With AI-generated content, fake AI identities, and deepfakes spreading faster than ever, AI-powered disinformation is becoming a serious issue. We explore how AI risk management, AI governance, and AI regulation are trying to catch up before AI trust is lost forever.Trust & Safety in AI:How do platforms like Facebook, YouTube, and Nextdoor determine what content gets removed? How does the moderation process work? And what are the hidden risks of AI trust & safety failures?Evaluating AI Models for Trust & Safety:How do companies evaluate LLMs and ensure AI-generated content isn’t spreading misinformation? We discuss the latest in AI safety, LLM evaluation, and how companies like OpenAI, Google, and Anthropic are handling AI fraud, AI accountability, and AI disinformation.Key Topics Covered:-What is content moderation? AI’s role in trust & safety-AI moderation bots vs. human moderation-Facebook moderation & the future of AI content filtering-The hidden risks of AI-generated content & deepfakes-How AI is breaking the internet—for better or worse-AI misinformation detection & AI disinformation at scale-AI fraud, risk assessment, and AI accountability-How AI safety teams are responding to AI threats-With AI moderation tools, chat moderation, and content filtering AI, tech companies are trying to prevent AI-powered misinformation while balancing --AI ethics, AI regulation, and free speech. But can AI content moderation actually keep up?Connect with us!Sugin Lou: https://www.linkedin.com/in/sugin-lou/ Hai Guan: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya Madipalli: https://www.linkedin.com/in/sravyamadipalli/Shane Butler: https://www.linkedin.com/in/shaneausleybutler/ #AI #ArtificialIntelligence #MachineLearning #AIContentModeration #FacebookModeration #ModerationBot #AITrust #AIAccountability #AIMisinformation #Deepfake #AIRegulation #TrustAndSafety #GenerativeAI #LLMEvaluation #AITrustAndSafety #AICompanions #AIEthics #AIModerationTools #WhatIsContentModeration

    58 min

  6. 25 FÉVR.

    Ep15: How Top Companies Use Data Science to Build Winning Products

    In this episode of the Data Neighbor Podcast, discover how data science shapes both winning and losing productr. We explore key insights from staff data science expert Anagh Pal with experience at Amazon, Twitter, and Nextdoor. Learn how cross-functional collaboration, data-driven experimentation, and hypothesis testing drive product success and avoid common pitfalls. Whether you're a data scientist, product manager, or just curious about how tech products evolve, this episode reveals essential strategies and lessons.Understanding product analytics and product development is crucial for anyone in data science, product management, or product design. In this episode, we dive into the product roadmap process and how data scientists and product managers collaborate to drive innovation. Learn how data analytics roadmaps influence decision-making, A/B testing, and experimentation to build products that users love. Whether you're wondering "What is data science?", planning your data science roadmap, or looking to advance in product management, this episode provides insights into real-world applications at top tech companies. Discover the key metrics, frameworks, and cross-functional strategies used by leading product managers and data scientists to launch, refine, and scale successful tech products.Connect with Hai, Sravya, Shane, and Anagh (let us know YouTube sent you!):Hai Guan: https://linkedin.openinapp.co/4qi1rSravya Madipalli: https://linkedin.openinapp.co/9be8cShane Butler: https://linkedin.openinapp.co/b02feAnagh Pal: https://www.linkedin.com/in/anaghpal/Learn about:-The role of data science in product success and failure-How data collaboration with PMs and engineers shapes outcomes-Data experimentation, A/B testing, and analyzing key metrics-Earning trust as a data scientist in cross-functional teams-Insights from careers at Amazon, Twitter, and Nextdoor-The future of data science and product development in 2025 #datascience #productdevelopment #abtesting #collaboration #techroadmap2025 #dataanalystroadmap #productroadmap #datastrategies #datascientistrole #bigtech

    29 min

  7. 18 FÉVR.

    Ep14: How DeepSeek Beat OpenAI - The 3 Breakthroughs No One Saw Coming

    AI is evolving faster than ever, and DeepSeek has sent shockwaves through the industry. In this episode of the Data Neighbor Podcast, we sit down with Dr. Raffaele Ciriello, a scholar in information systems at the University of Sydney, to unpack the impact of DeepSeek and its implications for the future of AI innovation, sustainability, and ethics.DeepSeek R1 and DeepSeek V3 have positioned themselves as major competitors to OpenAI's GPT-4, Google's Gemini, and Meta's LLaMA models—but what makes them different? In this episode, we discuss how DeepSeek AI is redefining the large language model (LLM) space, how its open-source AI model challenges proprietary AI, and whether it could be the best free AI alternative. We also explore the financial impact on Nvidia stock, as DeepSeek’s approach proves that cutting-edge AI doesn’t require billions in GPU investment. Plus, we break down how DeepSeek's mixture-of-experts (MoE) architecture is reshaping AI efficiency, its potential risks in AI companionship and generative AI, and what this means for the future of AI regulation and governance.🔗 Links Mentioned:Connect with Raffaele: https://www.linkedin.com/in/raffaele-ciriello/Raffaele’s Google Scholar: https://scholar.google.ch/citations?user=BIJHqJYAAAAJ&hl=enRaffaele’s ResearchGate: https://www.researchgate.net/profile/Raffaele-CirielloLatest article from Raffaele on OpenAI's Deep Research capability: https://theconversation.com/openais-new-deep-research-agent-is-still-just-a-fallible-tool-not-a-human-level-expert-249496Raffaele's article on Compassionate AI Design, Governance, and Use: https://www.techrxiv.org/users/886325/articles/1264666-compassionate-ai-design-governance-and-use🔍 Key Topics Covered:🔥 The rise of DeepSeek and why it’s a game-changer💰 How DeepSeek shattered AI’s cost barriers🆚 Open Source vs. Proprietary AI: What’s at stake?⚡ The environmental impact of AI & the Jevons Paradox🚀 AI regulation, safety, and the road aheadWe also dive into AI companionship, the ethical risks of unchecked AI, and how open-source AI could empower more responsible governance. Don’t miss this eye-opening discussion!Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Hai: https://linkedin.openinapp.co/4qi1rSravya: https://linkedin.openinapp.co/9be8cShane: https://linkedin.openinapp.co/b02feIf you need personalized advice for your data science career roadmap, send us a message on LinkedIn or post a comment below!

    45 min

  8. 12 FÉVR.

    Ep13: How to Master AI for Data Science (Before It’s Too Late)

    AI is creating a massive career arbitrage moment—but are you taking advantage of it? In this episode, we sit down with Yuzheng, an AI course instructor, ex-Meta, Amazon, Tencent, and a leading voice in the GenAI space. Yuzheng breaks down how the next 10-18 months will redefine careers and why those who embrace AI now will skyrocket ahead.🔹 AI isn’t replacing jobs—it’s amplifying potential.🔹 The skills that matter in the GenAI world (and what’s becoming obsolete).🔹 How AI-powered tools can make you a force multiplier at work.🔹 The mindset shift needed to stay ahead of the AI curve.Whether you're a data scientist, analyst, machine learning engineer, or just AI-curious, this episode will give you a practical roadmap to stay ahead before this window of opportunity closes.🔗 Links Mentioned in the Episode👉 Yu Zheng's AI Course on Maven: https://maven.com/kedaibiao/genai👉 Cursor: https://www.cursor.com/Connect with Hai, Sravya, and Shane (let us know which platform sent you!): Hai: https://linkedin.openinapp.co/4qi1rSravya: https://linkedin.openinapp.co/9be8cShane: https://linkedin.openinapp.co/b02feIf you need personalized advice for your data science career roadmap, send us a message on LinkedIn or post a comment below!

    56 min
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Notes et avis

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À propos

Welcome to the Data Neighbor Podcast with Hai, Sravya, and Shane! We’re your friendly guides to the ever-evolving world of data. Whether you’re an aspiring data scientist, a data professional looking to grow your career, or just curious about how data shapes the world, you’re in the right place. Our mission? To help you break in or thrive in the field of data. We dive into: - Personal career journeys and how luck, opportunity, and grit play a role - How to break into the data field even with a non-traditional background - Industry insights through engaging conversations and expert interviews

Informations

  • Création
    Data Neighbor Podcast
  • Années d’activité
    2024 - 2025
  • Épisodes
    20
  • Classification
    Tous publics
  • Copyright
    © Data Neighbor Podcast
  • Site web de l’émission
    Data Neighbor Podcast

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