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Overview

The bootcamp includes 111+ Jupyter notebooks organized across 8 modules (A1-A8). Each notebook is designed as an interactive learning experience combining theory, code examples, and hands-on exercises.
All notebooks are located in the source/ directory, organized by module folders: 000_A1 through 007_A8.

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Notebooks by Module

Focus: Foundations of data science, Python basics, and development environment setupTopics Covered:
  • Data science workflow and methodology
  • Setting up Python and Jupyter environments
  • Introduction to computational thinking
  • Overview of the bootcamp structure
Notebook Count: Introductory materials
Focus: Core Python programming conceptsTopics Covered:
  • Variables, data types, and operators
  • Control flow (if/else, loops)
  • Functions and modules
  • File I/O operations
Notebook Count: Foundation exercises
Focus: Working with structured data using NumPy arrays and Pandas DataFramesKey Notebooks:
  • 002_A3/L1_analisis_caso/numpy.ipynb - NumPy fundamentals
  • 002_A3/L2_analisis_caso/analisis_caso.ipynb - Data analysis case study
  • 002_A3/L5_analisis_caso/L5_analisis_caso.ipynb - Advanced data wrangling
  • 002_A3/PROYECTO/datos.ipynb - Module project
Topics Covered:
  • NumPy arrays and vectorized operations
  • Pandas DataFrames and Series
  • Data cleaning and transformation
  • Merging, joining, and concatenating datasets
  • Working with CSV and Excel files
Notebook Count: 4 core notebooks + Bootcamp-main lessons (Modulo3: 35+ notebooks)
Focus: Statistical analysis and data visualization techniquesKey Notebooks:
  • 003_A4/PROYECTO/comercio_ya.ipynb - E-commerce analysis project
Additional Resources:
  • Bootcamp-main/Modulo4/ contains 20+ notebooks covering:
    • Univariate and multivariate analysis
    • EDA best practices
    • Matplotlib and Seaborn visualizations
    • Statistical summaries and distributions
Topics Covered:
  • Descriptive statistics
  • Data distributions and outliers
  • Correlation analysis
  • Creating effective visualizations with Matplotlib and Seaborn
Notebook Count: 1 main project + 20+ supporting notebooks
Focus: Statistical foundations for data scienceKey Notebooks:
  • 004_A5/PROYECTO/simulacion.ipynb - Monte Carlo simulations
  • 004_A5/EJERCICIOS/L3_DataSnack_distribuciones.ipynb - Probability distributions
  • 004_A5/EJERCICIOS/DataNova.ipynb - Statistical analysis exercises
  • 004_A5/EJERCICIOS/Leccion3/Index_M5_AE3.ipynb - Hypothesis testing
Additional Resources:
  • Bootcamp-main/Modulo5/ contains 5+ notebooks on statistical inference
Topics Covered:
  • Probability theory fundamentals
  • Common distributions (Normal, Binomial, Poisson)
  • Hypothesis testing
  • Confidence intervals
  • Statistical inference
Notebook Count: 4 core notebooks + 5+ supporting notebooks
Focus: Supervised learning algorithms and model evaluationKey Notebooks:
  • 005_A6/PROYECTO/proyecto_modulo6_gasto_clientes_completo.ipynb - Customer spending prediction
  • 005_A6/EJERCICIOS/L3_preprocesamiento_escalamiento.ipynb - Data preprocessing
  • 005_A6/EJERCICIOS/L4_regresion_lineal_ejercicio.ipynb - Linear regression
  • 005_A6/EJERCICIOS/L5_clasificacion_knn_ejercicio.ipynb - KNN classification
  • 005_A6/EJERCICIOS/L6_metricas_regresion_clasificacion.ipynb - Model metrics
  • 005_A6/EJERCICIOS/L7_optimizacion_modelo_clasificacion.ipynb - Model optimization
  • 005_A6/EJERCICIOS/L8_boosting_gradient_boosting_ejercicio.ipynb - Ensemble methods
Additional Resources:
  • Bootcamp-main/Modulo6/ contains 19+ notebooks covering:
    • Train/test splits and cross-validation
    • Feature scaling and engineering
    • Regression and classification algorithms
    • Decision trees and ensemble methods
Topics Covered:
  • Supervised learning workflow
  • Linear and logistic regression
  • K-Nearest Neighbors (KNN)
  • Decision trees and random forests
  • Model evaluation metrics
  • Hyperparameter tuning
  • Gradient boosting
Notebook Count: 11 core notebooks + 19+ supporting notebooks
Focus: Unsupervised learning and advanced ML techniquesKey Notebooks: Located in 006_A7/PROYECTO/ and 006_A7/EJERCICIOS/Topics Covered:
  • Clustering algorithms (K-Means, hierarchical)
  • Dimensionality reduction (PCA, t-SNE)
  • Anomaly detection
  • Feature engineering techniques
  • Model pipelines
Notebook Count: 10 notebooks
Focus: Introduction to neural networks and deep learning frameworksKey Notebooks:
  • 007_A8/PROYECTO/proyecto_mod8_keras.ipynb - Fashion MNIST with Keras/TensorFlow
  • 007_A8/PROYECTO/proyecto_mod8_pytorch.ipynb - Fashion MNIST with PyTorch
  • 007_A8/EJERCICIOS/perceptron_AND_paso_a_paso.ipynb - Perceptron fundamentals
Topics Covered:
  • Neural network architecture
  • Activation functions and backpropagation
  • Building models with Keras/TensorFlow
  • Building models with PyTorch
  • Image classification with CNNs
  • Training, validation, and testing
  • Model optimization and regularization
Notebook Count: 4 notebooks

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How to Navigate the Notebooks

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Directory Structure

source/
├── 000_A1/          # Module 1: Introduction
├── 001_A2/          # Module 2: Python Fundamentals
├── 002_A3/          # Module 3: NumPy & Pandas
│   ├── PROYECTO/    # Module project
│   ├── EJERCICIOS/  # Exercises
│   └── L*_analisis_caso/  # Lesson case studies
├── 003_A4/          # Module 4: EDA & Visualization
├── 004_A5/          # Module 5: Probability & Statistics
├── 005_A6/          # Module 6: Machine Learning
├── 006_A7/          # Module 7: Advanced ML
├── 007_A8/          # Module 8: Deep Learning
└── Bootcamp-main/   # Additional reference notebooks
    ├── Modulo3/
    ├── Modulo4/
    ├── Modulo5/
    └── Modulo6/

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Notebook Types

Project Notebooks

Located in PROYECTO/ folders. These are comprehensive projects that integrate concepts from the entire module.

Exercise Notebooks

Located in EJERCICIOS/ folders. Focused practice on specific topics with guided exercises.

Lesson Notebooks

Named with patterns like Index_M*_AE*.ipynb or L*_analisis_caso.ipynb. Step-by-step lessons with examples.

Live Coding

Named LiveCoding*.ipynb. Real-time coding demonstrations with instructor notes.

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Using Jupyter Notebooks Effectively

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Essential Shortcuts

  • A - Insert cell above
  • B - Insert cell below
  • D, D - Delete cell
  • M - Convert cell to Markdown
  • Y - Convert cell to Code
  • Shift + Up/Down - Select multiple cells
  • Shift + Enter - Run cell and move to next
  • Ctrl + Enter - Run cell and stay
  • Alt + Enter - Run cell and insert below
  • Tab - Code completion
  • Shift + Tab - Show function documentation

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Best Practices

1

Run Cells Sequentially

Always run cells in order from top to bottom, especially when learning. Later cells often depend on variables defined in earlier cells.
2

Restart Kernel Regularly

Use Kernel > Restart & Run All to ensure your notebook runs cleanly from start to finish. This catches hidden dependencies.
3

Document Your Work

Add Markdown cells to explain your thought process, findings, and conclusions. This helps with learning and future reference.
4

Save Often

Use Ctrl + S frequently. Jupyter auto-saves, but manual saves ensure you don’t lose work.
5

Clear Output Before Sharing

Use Cell > All Output > Clear before committing notebooks to version control to reduce file size.

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Troubleshooting Common Issues

Module Not Found Error: Make sure you’ve installed all required packages. See the Tools & Libraries page for installation instructions.
Kernel Died: This usually means your code consumed too much memory. Try:
  • Restart the kernel
  • Process data in smaller chunks
  • Close other applications
Slow Performance: Large datasets can slow down Jupyter. Consider:
  • Using df.sample() to work with a subset
  • Closing unused notebooks
  • Restarting the kernel to free memory

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Tips for Learning

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Active Learning Approach

  1. Read First: Go through the entire notebook to understand the flow
  2. Type, Don’t Copy: Retype code examples rather than copy-pasting
  3. Experiment: Modify parameters and see what changes
  4. Break Things: Try to make code fail, then fix it - you’ll learn faster
  5. Add Comments: Explain code in your own words

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Project-Based Learning

Start with Exercises

Begin with EJERCICIOS notebooks to practice individual concepts before tackling full projects.

Complete All Projects

The PROYECTO notebooks in each module integrate all concepts and prepare you for real-world work.

Review Case Studies

The analisis_caso notebooks show how to apply techniques to realistic business scenarios.

Compare Approaches

In Module A8, compare the Keras and PyTorch implementations to understand different frameworks.

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Additional Resources

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Reference Notebooks

The Bootcamp-main/ directory contains additional reference materials:
  • Modulo3: 35+ notebooks on Pandas operations
  • Modulo4: 20+ notebooks on visualization techniques
  • Modulo5: 5+ notebooks on statistical methods
  • Modulo6: 19+ notebooks on machine learning algorithms

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Automatic Pipeline Notebooks

The automatico/ directory contains:
  • modulos_3_a_7_plan_estudio.ipynb - Study plan overview
  • auto_pipeline_csv_paso_a_paso.ipynb - Automated data pipeline example
Having trouble with a specific notebook? Check the glossary for term definitions or the tools page for setup help.

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Next Steps

Datasets Reference

Learn about the datasets used in these notebooks

Tools & Libraries

Set up your development environment

Glossary

Look up data science terms and concepts

Contact Manager Project

Start with the first module project

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