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This short book has been designed and written for covering Tutorial R for Data Science topics. These lectures are included in Data management and warehousing course held at Master of Science in Statistical and actuarial sciences at Università Cattolica del Sacro Cuore.
The main goal is to provide Students with an idea of how the process that interests Data Science projects in the industry can be exploited. In this field, projects characteristics and processes are several, therefore the focus here is on a particular case. Exploratory Data Science Data Science projects conducted on a laboratory environment in a Big Data framework are considered. However, many elements are in common with all of the possible Data Science applications. This particular case is structured for providing some elements aimed at contributing to the creation of the forma mentis necessary to understand how to act in this context.
The book is structured as a storyline. The former part (Chapter 1 and 2), is mainly focused on introducing Big Data context and the tool used, i.e. tidy verse package of R software. Subsequently, (Chapters 3, 4 and 5) presents functions for loading, wrangling and visualizing data. Such functions are firstly presented with dummy examples, therefore are applied on a simulated credit card data. This data set is ideally constructed, loaded, wrangled and visualized within this text, reproducing an explorative Data Science application.
The main goal is to provide Students with an idea of how the process that interests Data Science projects in the industry can be exploited. In this field, projects characteristics and processes are several, therefore the focus here is on a particular case. Exploratory Data Science Data Science projects conducted on a laboratory environment in a Big Data framework are considered. However, many elements are in common with all of the possible Data Science applications. This particular case is structured for providing some elements aimed at contributing to the creation of the forma mentis necessary to understand how to act in this context.
The book is structured as a storyline. The former part (Chapter 1 and 2), is mainly focused on introducing Big Data context and the tool used, i.e. tidy verse package of R software. Subsequently, (Chapters 3, 4 and 5) presents functions for loading, wrangling and visualizing data. Such functions are firstly presented with dummy examples, therefore are applied on a simulated credit card data. This data set is ideally constructed, loaded, wrangled and visualized within this text, reproducing an explorative Data Science application.