BusinessIntelligence

This repository contains the code for the Business Intelligence module of the APSV ('Aplicaciones y Servicios') subject of the 'Máster Universitario en Ingeniería de Telecomunicación' of the 'Universidad Politécnica de Madrid'. It is also used for ISTE ('Ingeniería de Servicios Telemáticos') subject of the 'Máster Universitario en Ingeniería de Redes y Servicios Telemáticos' of the same university.

The repository contains the following folders:

• data: contains the data used for the exercises.
• notebooks: contains the Jupyter notebooks that will be used on the labs.

There are 3 notebooks:

• 1. Introduction to Python.ipynb: contains the code for the first lab. On it we will learn the basics of Python language and how to use Pandas and Matplotlib libraries.
• 2. Data exploration and visualization.ipynb: contains the code for the second lab. On it we will learn how to use the Pandas library to perform data analysis.
• 3. Introduction to Machine Learning.ipynb: contains the code for the third lab. On it we will learn the basics of machine learning using the Scikit-learn library.

How to use the notebooks

The recommended way to use the notebooks is to use Google Colab platform. To do so, follow these steps:

1. Go to https://colab.research.google.com/
2. Click on the GitHub tab.
3. Paste the URL of this repository: https://github.com/APSV-UPM/BusinessIntelligence
4. Click on the search icon.
5. Click on the notebook you want to open.

After that, you will be able to run the code on the notebooks. You can also modify the code and save the changes on your computer or on your Google Drive. If you want to continue working on the notebooks later, you will need to open the saved copy instead of the original one from GitHub.

How to use the notebooks (alternative)

⚠️ WARNING: This alternative will not be supported by the teachers. Use it at your own risk.

If you want to use the notebooks locally, in Anaconda or any other solution, you will need to install Python 3.8 or higher and the following libraries:

• pandas
• matplotlib
• scikit-learn
• seaborn
• numpy

Also, you might need to manually download the data from the data folder and modify the cells that read the data to point to the correct location.