🎙️ Future of Data and AI Podcast: Episode 08 with Emil EifremBefore graph databases were everywhere…Before knowledge graphs became essential to AI…Before LLMs, embeddings, and RAG entered the conversation…There was one simple, stubborn idea: data makes more sense when you understand the relationships.In this engaging and insightful episode, Emil Eifrem (Co-Founder & CEO | Neo4j) shares the story behind building Neo4j — the graph database platform that quietly reshaped how the world models and reasons over data. Emil takes us through the early days when graph databases felt risky, misunderstood, and years ahead of the market, and explains why connected data mirrors human thinking better than tables, rows, and columns ever could.What you’ll discover:🔹Why relationships became the foundation of Neo4j — and how treating them as first-class citizens changed everything.🔹How property graph models preserve context, making complex systems easier to reason about and analyze.🔹Unexpected ways graph databases are powering fraud detection, recommendations, cybersecurity, and enterprise knowledge.🔹Why knowledge graphs quietly became essential to modern AI — long before most people noticed.🔹How graphs and LLMs work together to ground AI systems in structure, meaning, and explainability.🔹Insights on building deep infrastructure technology with patience, conviction, and long-term vision.This is more than a conversation about AI, graphs, or databases. It’s a look at how intelligence — human or artificial — depends on connections, context, and understanding.📌 If you’ve ever wondered:- Why AI sometimes feels confident but wrong- How machines can reason instead of just predict- Why some technologies take years before the world catches up…this episode will change how you think about data, AI, and connected systems.🔹A must-listen for: data scientists, AI practitioners, knowledge graph enthusiasts, graph database users, enterprise architects, AI researchers, and anyone curious about how connected data powers modern AI.Prepare to walk away inspired.Keywords: Emil Eifrem, Neo4j, graph databases, knowledge graphs, property graph model, AI reasoning, connected data, enterprise AI, graph analytics, fraud detection AI, AI infrastructure, LLM applications, RAG, embeddings, AI explainability, building data platforms, AI strategy, human-like reasoning, data science insights, graph AI, Neo4j interview, AI podcast, Future of Data and AI PodcastFor more episodes: https://www.youtube.com/playlist?list=PL8eNk_zTBST_jMlmiokwBVfS_BqbAt0z2For highlights, check out: https://www.youtube.com/playlist?list=PL8eNk_zTBST-YYNgPcw3rO9Tvn7fEjR4AVisit our podcast page for more info: https://datasciencedojo.com/podcast/

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.

🎙️ Future of Data and AI Podcast: Episode 06 with Robin Sutara

What do Apache, Excel, Microsoft, and Databricks have in common? Robin Sutara!

From being a technician for Apache helicopters to leading global data strategy at Microsoft and now Databricks, Robin Sutara’s journey is anything but ordinary. In this episode, she shares how enterprises are adopting AI in practical, secure, and responsible ways—without getting lost in the hype.

We dive into how Databricks is evolving beyond the Lakehouse to power the next wave of enterprise AI—supporting custom models, Retrieval-Augmented Generation (RAG), and compound AI systems that balance innovation with governance, transparency, and risk management. Robin also breaks down the real challenges to AI adoption—not technical, but cultural.

She explains why companies must invest in change management, empower non-technical teams, and embrace diverse perspectives to make AI truly work at scale. Her take on job evolution, bias in AI, and the human side of automation is both refreshing and deeply relevant. A sharp, insightful conversation for anyone building or scaling AI inside the enterprise—especially in regulated industries where trust and explainability matter as much as innovation.