Data Science for AI
Build the statistical foundation for AI: data quality, regression, classification, time series, neural networks, and when to use classical ML vs deep learning vs foundation models.
"For this churn problem I'd use XGBoost, not GPT-4 — here's the confusion matrix and business cost analysis proving why"
4 Interactive Sessions
Short, interactive sessions — watch it work, steer it, then build it yourself. Go deeper anytime with the full code walkthrough.
- 1
See the data — and draw the line that predicts the next one
A model can be as simple as a line through your data: find the trend, then read the future off it.
- 2
Teach it to decide — classification
When the answer is a category, the model learns a boundary that separates the classes — and you grade it with precision and recall.
- 3
Find the hidden structure — without an answer key
With no labels, a model can still find patterns: group similar things (clustering) and flag the odd one out (anomaly detection in time).
- 4
Demystify neural nets — then pick the right brain for the job
A neural net builds its own features from raw data — powerful for media, overkill for a spreadsheet. The skill is matching the paradigm to the problem.
Production patterns you'll master
Synthetic data included
- Housing dataset (JSON)
- Churn data with labels
- Monthly sales (CSV)
- Neural network training logs
- Model cost comparisons
- Paradigm decision trees
What you walk away with
Shareable portfolio
A public URL showing your module timeline, patterns mastered, and completion status.
All the code
Download everything as a ZIP — pipelines, guardrails, deployment configs. Yours forever.
Module walkthrough
Each module documented with deliverables and the production pattern you implemented.
Ready to build your data science for ai?
First course free. $20 per course after that.