Data Science & AI
From counting words to conversing with machines: the full path from statistics and Python craft through classical machine learning to Transformers, LLM systems and production ML — every concept grounded in the same small demo corpus you can inspect.
This catalogue is one long walk in four domains, each standing on the last. Foundations supplies the statistics, mathematics, computing and Python that everything later quietly assumes. Machine Learning covers the classical toolkit and the neural-network story. AI Systems opens the modern stack — Transformers, LLMs, retrieval and agents — and Production turns working models into services and decisions people can rely on.
You do not have to walk it in order. If your profession is language rather than code — teaching, translating, interpreting — the guided start picks a no-maths route through the same material. If you are an engineer, pick any domain below and drill in: every landing page introduces its block before the modules begin, and every computation runs on the same small demo corpus you can inspect by hand.
Foundations
P1 · Statistics & Probability
describe data, reason under uncertainty, test hypotheses
15 live modules · 3 tracks
P2 · Mathematics for ML
linear algebra, calculus, optimization
12 live modules · 2 tracks
P3 · Computer Science & Computer Architecture
algorithms & data structures, how the machine executes
3 live modules · 1 track
P4 · Python & DS Engineering Craft
idioms → vectorization → pandas/polars → scipy; testing, packaging
2 live modules · 1 track
Machine Learning
P5 · Traditional Machine Learning
supervised + unsupervised end to end
14 live modules · 3 tracks
P6 · Deep Learning & Neural Networks
from the neuron to modern architectures
14 live modules · 2 tracks
P7 · Time Series & Forecasting
decomposition, classical + ML forecasting, anomaly detection
9 live modules · 2 tracks
P8 · Causal Inference & Experimentation
A/B at scale, uplift, quasi-experiments
8 live modules · 2 tracks
P9 · Reinforcement Learning
bandits → MDPs → policy methods; bridges to RLHF
7 live modules · 2 tracks
AI Systems
Production & Professional Practice
P12 · Data Engineering
pipelines, data quality, SQL/polars at scale
9 live modules · 2 tracks
P13 · Production ML & MLOps
taking models to production: deployment, monitoring, LLMOps
7 live modules · 4 tracks
P14 · Data Visualization & Communication
chart craft, dashboards, stakeholder storytelling
9 live modules · 2 tracks
P15 · Responsible AI — Ethics, Safety & Governance
fairness, privacy, governance, EU AI Act, LLM safety
8 live modules · 2 tracks
P16 · ML Leadership & Product
scoping ML projects, make-vs-buy, leading DS teams
4 live modules · 1 track