We hear a lot about data preparation and clean data before doing a Data Analysis or Data Science job, even an AI or Machine Learning job. And that around 90% of an analyst's work is cleaning and transforming data. In this session, we will focus on a practical use case from a real life scenario, where we will have a problem and how we will tackle it from step 1 until the data is clean and ready for data analysis. By step 1 we mean understanding the problem, understanding on how to get the data, then assuming we have the data or generate it (as a bonus for testing), and then work on it and clean / transform it to be ready for Data Analysis. Data Analysis itself is outside the scope of this session.
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