A collection of resources on the path to becoming the elusive unicorn data scientist. Filter results using the drop-down pages menu above. Get in touch at [email protected] - Follow on twitter @datascienceuni - Listen to podcast at datacafe.uk
“I feel there is one element of leadership that has been ignored for too long. A very important element: great leaders lead by not doing much.
Great leaders are masterful because they don't get in the way. They don't interfere. When you step away from your ego and you release your competitive streak, you become nicer, better, and more effective. When you don't force your ideas on everyone else, when you don't force a direction, magical things happen.
Your team comes together. They come up with amazing ideas. They set the direction and they are inspired to follow it. They try harder. They do more. And this can only happen when there is no interference and disruption from an egotistical leader who loves hearing his own voice. It seems like a counterintuitive concept for leadership, l know. But I can tell you it works. It's worked for me for more than ten years now…”
AI is collapsing the distance between ideas and working products, and that changes what leadership looks like.
To delve into this I used ChatGPT & Replit to spin-up a functioning physiotherapy tracker app in under an hour.
But the leadership insights matter more than the code.
- True innovation balances vision with velocity: test the pain points quickly, but never lose sight of the big idea.
- AI increasingly makes scope creep cheaper, so good leadership is needed to maintain focus.
- The future of tech leadership is turning AI from a tool into a teammate, and reshaping how we build together.
I shared my full walkthrough and leadership takeaways here: https://medium.com/@jbyrnephd/building-faster-leading-smarter-a-tech-leaders-experiment-with-ai-driven-app-development-518c47b765c2
A hands-on build with ChatGPT and Replit, and what it reveals about leading tech teams today.
What do you think, are we ready to treat AI as a teammate?
I tested ChatGPT’s new Agentic AI to find out: can GPT-5 access and optimise my calendar for me?
When OpenAI announced last month that ChatGPT could now act as an Agentic AI, I wanted to see what that actually feels like in practice. Wit
My key insight:
• For individuals, Agentic AI will soon be the personal assistant we never had; becoming a commodity in everyday tools that supercharges productivity.
• For enterprises, the real edge will come from tailoring such agents with proprietary data and workflows across pillars like Supply Chain, R&D, Marketing, and Central Functions. Those who combine high-quality data, cross-system workflows, and AI-skilled people, with appropriate governance, will unlock the ground-breaking opportunities.
I tested ChatGPT’s new Agentic AI to find out: can GPT-5 access and optimise my calendar for me?
When OpenAI announced last month that ChatGPT could now act as an Agentic AI, I wanted to see what that actually feels like in practice. Wit
My key insight:
• For individuals, Agentic AI will soon be the personal assistant we never had; becoming a commodity in everyday tools that supercharges productivity.
• For enterprises, the real edge will come from tailoring such agents with proprietary data and workflows across pillars like Supply Chain, R&D, Marketing, and Central Functions. Those who combine high-quality data, cross-system workflows, and AI-skilled people, with appropriate governance, will unlock the ground-breaking opportunities.
Shipping GenAI isn't just models, it's 𝐫𝐞𝐩𝐞𝐚𝐭𝐚𝐛𝐥𝐞 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 with 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 𝐩𝐚𝐭𝐡𝐬.
So 𝘧𝘳𝘰𝘮 𝘵𝘶𝘵𝘰𝘳𝘪𝘢𝘭 𝘵𝘰 𝘵𝘦𝘢𝘮 𝘱𝘭𝘢𝘺𝘣𝘰𝘰𝘬: I've distilled Databricks' unstructured data pipeline for RAG into a 𝒎𝒊𝒏𝒊𝒎𝒂𝒍, 𝒓𝒖𝒏𝒏𝒂𝒃𝒍𝒆 𝒘𝒂𝒍𝒌𝒕𝒉𝒓𝒐𝒖𝒈𝒉 your teams can drop into a UC-governed project and start measuring.
Highlights:
- widgets and a _bootstrap setup (that software developers might hate but data scientists will appreciate).
- gotchas: path configs and UC table naming.
- levers to tune: chunk size/overlap, model swaps.
Leader's angle: 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐬𝐞 𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐬, reduce setup drag, and enable fast iteration and comparison of results across projects and teams.
This is a walkthrough of the Databricks tutorials for setting up an unstructured data pipeline for RAG (retrieval augmented generation)…
