This is a two-parts hands-on training where I walk the audience through the basics coding skills required for Data Science from a Software Engineering perspective. I start playing with Python and its programming paradigm. Then I introduce a few data manipulation tools such as NumPy, Pandas, and Matplotib. After that, we compare the most used visualization tools - Matplotlib, Seaborn, and Plotly - to complete the first part. For the second part, I use a massive dataset to extract and identify knowledge with the already given toolset based on the KDD methodology. In the end, we run a machine learning algorithm with the founded features to exemplify its application.