This data driven dealings development (DDDD) series aims at people who want to learn the concepts of statistical analysis, machine learning (ML), deep learning (DL), artificial intelligence (AI), statistical process control (SPC), data mining and data science (DS) with sales data in practice. It’s meant as a truly exhaustive explanation starting from scratch with easy to adapt data. We’ll conquer the concepts of data science, explore our data, reflect on data visualization and storytelling, predict future sales, mine market baskets and recommend products to customers. In the end we’ll build a data product in the shape of a complete ready to go sales chatbot, who will answer our customers’ frequently asked questions. All code is explained and provided in Jupyter Notebook.

Motivation:

When I was starting my Python data analysis journey I was missing books or tutorials which really cover all the subjects involved when trying to conduct a sales analysis successfully. Especially when you are a complete newbie running an analysis from A-Z without any (or not sufficient) pre-knowledge is difficult, because most books or tutorials only cover specific parts of the whole project. It is challenging to put all the pieces of the puzzle together. With this series I want you to have one guideline in hand which leads you all the way through your whole sales analysis project, from installing the necessary Python libraries, cleaning the data, effectively training machine learning models and deploying the results to your colleagues in an intelligible way.

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