Every module. In order. Free.

Python, SQL, Git and statistics. The base every data role builds on.

1. 01

   Set Up Your Machine

   Terminal, Python, Git, Docker

   4h
2. 02

   Command Line

   Bash

   9h
3. 03

   Python

   Python 3, pytest

   20h
4. 04

   Git & GitHub

   Git, GitHub

   11h
5. 05

   Data in Python

   NumPy, pandas

   18h
6. 06

   SQL

   PostgreSQL

   18h
7. 07

   Statistics

   Python, NumPy, SciPy

   18h
8. 08

   AI Literacy

   LLMs, embeddings, Pydantic

   12h
9. 09

   Foundations Capstone

   Your first verified project

   25h

Build one data platform end to end: ingest, transform, schedule, deploy and monitor it.

1. 01

   Ingestion Pipeline

   APIs, files, databases

   22h
2. 02

   Warehouse Modeling

   Kimball, SCDs, cumulative tables

   22h
3. 03

   Transform with dbt

   dbt, PostgreSQL

   20h
4. 04

   Scheduling with Airflow

   Airflow

   22h
5. 05

   Cloud Data Lake

   S3, Parquet, Glue, Athena, Terraform

   26h
6. 06

   Big Data with Spark

   PySpark

   24h
7. 07

   Streaming with Kafka

   Kafka, Docker

   24h
8. 08

   Run and Monitor

   Terraform, Lambda, EventBridge, CloudWatch

   26h
9. 09

   Data Platform Capstone

   Your full settlement platform

   24h

Turn messy data and vague questions into analysis people trust and act on.

1. 01

   Exploratory Data Analysis

   pandas

   18h
2. 02

   Data Visualization

   Matplotlib, Seaborn

   18h
3. 03

   Statistics for Analysts

   SciPy, statsmodels

   20h
4. 04

   Dashboard Design

   Power BI, wireframes

   16h
5. 05

   Data Storytelling

   Memos, slides

   16h
6. 06

   Power BI

   Power Query, DAX

   28h
7. 07

   AI-Assisted Analysis

   LLMs for SQL and DAX

   14h
8. 08

   Analysis Capstone

   Your full decision product

   30h

Frame a problem, build and validate a model, then deploy it and watch it in production.

1. 01

   ML Fundamentals

   scikit-learn

   23h
2. 02

   How Models Learn

   Regression, trees, KNN, regularisation

   24h
3. 03

   Feature Engineering

   scikit-learn, pandas

   18h
4. 04

   Unsupervised Learning

   PCA, k-means, clustering

   18h
5. 05

   Model Evaluation

   scikit-learn, Fairlearn

   22h
6. 06

   A/B Testing

   Python, SciPy

   20h
7. 07

   Advanced Models

   XGBoost, SHAP

   24h
8. 08

   Model Deployment

   FastAPI, Docker, AWS Lambda

   27h
9. 09

   Data Science Capstone

   Your full credit-risk system

   28h

Take the model you built in Data Science to production: test it, ship it, monitor it, and improve it safely.

1. 01

   Tracked, Repeatable Training

   MLflow, Docker

   24h
2. 02

   CI/CD for ML

   GitHub Actions

   22h
3. 03

   Serving and Safe Releases

   Docker Compose, AWS Lambda

   29h
4. 04

   Monitoring and Incidents

   Evidently, Grafana

   26h
5. 05

   MLOps Capstone

   Your full ML platform

   30h

Build working AI products: LLM apps, retrieval (RAG), agents, and the systems that run them.

Coming soon. We're finishing the data and MLOps paths first.