This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises.

Second of a series of books, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects.

Since this text uses Orange for the application aspects, then its installation and widgets are briefly described. In any case this book should not be considered an Orange user manual, and if by chance you were wrong in choosing it because you expected something different, you are free to return it but probably it is not the case to give a negative rating.

Then it considers the concept of model, its life cycle and the relationship with measures and metrics. The measures of localization, dispersion, asymmetry, correlation, similarity, distance are then described. The test and score metrics used in machine learning, those relating to texts and documents, the association metrics between items in a shopping cart, the relationship between objects, similarity between sets and between graphs, similarity between time series are considered.

As a preliminary activity to the modeling phase, the Exploration Data Analysis is deepened in terms of questions, process, techniques and types of problems. For each type of problem, the recommended graphs, the methods of interpreting the results and their implementation in Orange are considered.

The data modeling phase is considered from the point of view of machine learning by deepening the types of machine learning, the types of models, the types of problems and the types of algorithms.

After considering the ideal characteristics of models and algorithms, a vocabulary of the types of models and algorithms is compiled and their use in Orange is considered through supervised and unsupervised learning projects.

The text is accompanied by supporting material and you can download the exercises and test data. IMPORTANT: The book uses Orange Data Mining for exercises but it is NOT an Orange User Manual.