Ironhack Data Analytics Bootcamp

Documentation and exercises from the Ironhack Data Analytics bootcamp (July 2026).

The bootcamp is taught in English, so I keep all my notes in English too.

Structure

• prework/ — exercises before the bootcamp starts: data types, descriptive statistics, pseudocode, data structures (lists, dictionaries), conditionals and functions in Python.

Each week has its own folder with a README.md summary and day1–day5 subfolders for each day's exercises. Expand a week to jump straight to its days:

Week 1 — Introduction to Python

Week folder

• Day 1 — Terminal, Git & GitHub, data structures
• Day 2 — Flow control & functions
• Day 3 — More on functions & comprehensions
• Day 4 — Lambda, map/reduce/filter & error handling
• Day 5 — Object-Oriented Programming & Python challenge

Week 2 — Data Wrangling & Retrieval

Week folder

• Day 1 — Data wrangling, GDPR & intro to Pandas
• Day 2 — Data cleaning & manipulation
• Day 3 — Structuring, combining & aggregating data
• Day 4 — Web scraping
• Day 5 — Quest deliverable & Data Wrangling challenge

Week 3 — SQL

Week folder

• Day 1 — Databases & data modelling
• Day 2 — Basic SQL queries & aggregation
• Day 3 — SQL joins & subqueries
• Day 4 — Temporary tables, views, CTEs & window functions
• Day 5 — Connecting SQL & Python, SQL challenge

Week 4 — First Project

Week folder

• Day 1 — Project kickoff, Git collaboration & Agile
• Day 2 — Project work
• Day 3 — Project work
• Day 4 — Project work
• Day 5 — Presentation & deliverable

Week 5 — EDA and Inferential Stats

Week folder

• Day 1 — EDA & univariate analysis
• Day 2 — EDA bivariate analysis
• Day 3 — Probability & hypothesis testing
• Day 4 — Correlation, normality & Tableau
• Day 5 — Advanced Tableau, storytelling & Vanguard project

Week 6 — Second Project

Week folder

• Day 1 — Project kickoff, Git & environments
• Day 2 — Project work
• Day 3 — Project work
• Day 4 — Project work
• Day 5 — Presentation & deliverable

Week 7 — Machine Learning

Week folder

• Day 1 — Intro to Machine Learning
• Day 2 — Feature engineering
• Day 3 — Supervised learning & ensemble methods
• Day 4 — Hyperparameter tuning
• Day 5 — Class imbalance

Week 8 — APIs, LLMs, Cloud Computing, BigQuery & Final Project

Week folder

• Day 1 — Week 7 project presentation & deliverable
• Day 2 — APIs
• Day 3 — Large Language Models (LLMs)
• Day 4 — Cloud computing & BigQuery
• Day 5 — Final project kickoff

Week 9 — Final Project

Week folder

• Day 1 — Final project work
• Day 2 — Final project work
• Day 3 — Final project work
• Day 4 — Final project work
• Day 5 — Presentation & deliverable

Week 10 — Extra Week 1: Unsupervised Learning & Streamlit

Weeks 10 and 11 are extra content — not part of the official Spain program's curriculum.

Week folder

• Day 1 — Web scraping recap & Gnod project
• Day 2 — Unsupervised learning methods
• Day 3 — Unsupervised learning metrics
• Day 4 — Streamlit
• Day 5 — PCA & week deliverable

Week 11 — Extra Week 2: NumPy, PyTorch & Deep Learning

Week folder

• Day 1 — Google Cloud Platform
• Day 2 — NumPy
• Day 3 — PyTorch tensors & neural networks
• Day 4 — Build an LLM
• Day 5 — Neural network example

As the bootcamp progresses, each week will be filled in with its projects and exercises.

Labs

Solved Lab | ... notebooks live in this repo, inside their corresponding day folder — see the day-by-day breakdown above.

Each lab is also kept as a separate fork of a data-bootcamp-v4 template repo, only because Ironhack's Student Portal requires it — it checks for an open pull request against the upstream repo to mark a lab as done, not the code itself. So the fork + PR is submission plumbing; this repo is the actual copy I read back.

Workflow for each lab:

1. Fork the lab's template repo (e.g. data-bootcamp-v4/lab-python-functions) to my account.
2. Clone the fork locally.
3. Solve the exercise, commit, push to origin (my fork).
4. Open a pull request from my fork back to the upstream data-bootcamp-v4 repo — easy to miss, some lab READMEs only mention git push and stop there.
5. Copy the solved notebook into this repo, in the matching day folder.
6. Sync/check in the Student Portal.

Lab	Solved here	Submission PR
Data Structures	week01/day1	lab-python-data-structures
Flow Control	week01/day2	lab-python-flow-control
Functions	week01/day2	lab-python-functions
List, Dict, Set Comprehension	week01/day3	lab-python-list-dict-set-comprehension
Error Handling	week01/day4	lab-python-error-handling
Pandas	week02/day1	lab-dw-pandas
Data Cleaning and Formatting	week02/day2	lab-dw-data-cleaning-and-formatting
Data Structuring and Combining	week02/day3	lab-dw-data-structuring-and-combining
Data Aggregation and Filtering	week02/day3	lab-dw-data-aggregation-and-filtering
Web Scraping	week02/day4	lab-web-scraping
MySQL Database Creation	week03/day1	lab-sql-mysql-db-creation
SQL Basic Queries	week03/day2	lab-sql-basic-queries
SQL Data Aggregation and Transformation	week03/day2	lab-sql-aggregation-and-transformation
SQL Joins	week03/day3	lab-sql-joins
SQL Subqueries	week03/day3	lab-sql-subqueries
SQL Temporary Tables, Views and CTEs	week03/day4	lab-sql-temp-tables-views-ctes
SQL Window Functions	week03/day4	lab-sql-window-functions

Bonus content

Optional extra modules outside the 9 core weeks.

Flask & Django

Folder — model deployment with Flask & Django (RNCP): intro to model deployment, deploying a data science project with Django, and Flask for data science deployment.

AI Fundamentals

Folder — introduction to AI, ChatGPT & MS Copilot, AI ethics, AI security, field-specific AI applications, the societal impact of AI and the future of AI.

About the bootcamp

Ironhack Data Analytics Bootcamp — an intensive full-time 9-week program with live classes taught in English. Start: July 2026.