This repository contains a series of interactive notebooks focusing on data mining and data analysis, specially designed as part of the Master's in Data Science program. These exercises will guide you through various aspects of data mining, including linear regression, time series analysis, PCA (Principal Component Analysis), and clustering techniques.

Contents Jupyter Notebooks: You will find a collection of Jupyter notebooks covering a wide range of topics in data mining, from data exploration and preprocessing to the application of advanced algorithms for regression, time series analysis, dimensionality reduction using PCA, and clustering techniques.

Datasets: The notebooks include links to both public and private datasets used in the exercises. Specific datasets for the Master's program are available.

Detailed Instructions: The notebook contains detailed instructions and explanatory comments to guide you through the exercises. Code examples are provided, and the logic behind each step is explained.

Requirements To make the most of these exercises, students are expected to have a solid foundation in Python programming, statistical and mathematical concepts, and prior knowledge of Python libraries for data science, such as NumPy, pandas, scikit-learn, and visualization libraries.

Usage Clone or download this repository to your local machine.

Open the Jupyter notebooks in a development environment of your choice (e.g., Jupyter Notebook or JupyterLab).

Follow the detailed instructions in each notebook to complete the exercises and explore data mining, including linear regression, time series analysis, PCA, and clustering techniques.

Enjoy learning and applying these fundamental concepts of Data Science!

Contributions If you are a student in the Master's in Data Science program and wish to contribute to this repository by adding additional exercises or improving documentation, you are encouraged to do so. Please ensure that you follow the contribution guidelines specified in this repository.