Write something
Pinned
7d •
I'm shutting down this community
I started this community over two years ago with a simple goal: to create a free place to learn, share knowledge, and help people get started with Python and data. For a long time, that worked well. Many of you showed up with exactly that mindset, and for that I am genuinely grateful. Unfortunately, the reality today is very different. Over time, the community has been taken over by spam, scams, and people trying to sell to or profit from others. At this point, most of the activity no longer has anything to do with learning or knowledge sharing. Keeping a free community like this healthy would require constant, active moderation. Since Skool does not have an API, that would mean someone on my team manually clicking through spam messages all day. That is not something I want anyone spending their time on. To be fully transparent, I have let this community run on autopilot for the past year. That is not fair to the people who are here with good intentions. Because of that, I have decided to shut down Data Alchemy. Going forward, all of our communities will be private and actively moderated. That is the only way to create a high quality, safe environment where people can actually learn and connect without dealing with scammers or noise. This does not mean the end of learning or staying in touch. I will continue to be active on YouTube, especially in the community feed and comment sections. Those are the best places to keep learning, ask questions, and stay connected without having to worry about spam or people trying to take advantage of you. And in case you have not seen it yet, I recently updated a full Python introduction course on YouTube. It covers everything I wanted to share in this free community, and more, in a single five hour video. Thank you to everyone who showed up, shared knowledge, asked good questions, and contributed in a positive way. I truly appreciate the time and energy you put into this community.
58
0
16h •
Data Drift & Why Models “Feel Wrong” Over Time
Ever noticed this? Your data model works well for a few months. Predictions look right. Insights feel sharp. Then slowly… things feel off. Nothing is technically broken. But decisions based on the data don’t hit like they used to. This usually isn’t a tooling issue. It’s data drift. Here’s what’s actually happening: • User behavior changes • Market conditions shift • Internal processes evolve • Edge cases become the norm But the model is still thinking in the old reality. Most teams only monitor: – accuracy – performance – latency Very few monitor relevance. Good data teams do one simple thing differently: They regularly ask “Does this data still represent how the business works today?” Practical examples: • Last quarter’s “high-value user” definition no longer applies • A metric that mattered before is now just noise • Old patterns are being over-trusted AI doesn’t fail loudly. It drifts quietly. Data Alchemy isn’t just about building models —it’s about knowing when reality has changed and your data hasn’t caught up yet. Sometimes the smartest move isn’t improving the model. It’s redefining what “signal” means right now.
0
0
2d •
Why “more data” is making teams slower, not smarter
One of the biggest misconceptions in data teams is this: “If we collect more data, clarity will follow.” In reality, the opposite often happens. More data usually means: • more alerts • more dashboards • more conflicting signals And less confidence in decisions. The strongest data teams I’ve seen do something counterintuitive: they intentionally limit what the system is allowed to observe. Here’s how they think about it: • Data exists to support decisions, not curiosity • Every metric must justify why it deserves attention • If a signal doesn’t change an action, it doesn’t belong in the system AI makes this even more important. When models ingest everything, they amplify noise just as efficiently as signal. Good data alchemy today isn’t about scale. It's about constraint. Less input. Clear intent. Faster judgment. That’s how data stops being overwhelming and starts becoming quietly powerful.
0
0
6d •
Why your data pipeline feels busy but still doesn’t help decisions
I see this a lot in teams working with data + AI. Pipelines are busy. Events flowing. Running analysis and nurturing. Dashboards updating. Yet when a real decision needs to be made, people still ask: “Can someone look into this?” That’s a signal something’s off. A busy pipeline doesn’t mean a useful pipeline. Here’s the common issue, in simple terms: Most pipelines are built to move data, not to support decisions. They focus on: • ingesting everything • transforming everything • storing everything But they forget to ask one basic question early: 👉 What decision is this data supposed to help us make? When that’s unclear, pipelines become noisy. A healthier pipeline looks like this: Decision first Example: “Do we intervene when user churn risk increases?” Minimal signals Only ingest data that actually affects that decision(not everything you can track). Clear thresholds At what point should the system alert, act, or stay quiet? Simple output Not a dashboard. A recommendation, alert, or action. This is where AI actually helps —by filtering noise, summarizing context, and pointing to what matters now. Busy pipelines move data fast. Good pipelines move understanding fast. Data alchemy isn’t about making pipelines bigger. It's about making them calmer, clearer, and decision-ready.
3d •
Happy holidays and a thank you.
Happy holidays and Merry Christmas to those that celebrate this. Sorry to hear that this community will be shut down, but I can completely understand the reason @Dave Ebbelaar . Thanks for bringing us together and being a great example to us all. Hope there will be a curated community where we can learn and grow together. Game on folks! 🤟🚀🤟
1-30 of 4,205
skool.com/data-alchemy
Your Community to Master the Fundamentals of Working with Data and AI — by Datalumina®
Leaderboard (30-day)
1
+63
2
+12
3
+12
4
+11
5
+11
Powered by