processingwranglingand visualizing data the world around us has changed tremendously since computers and the internet became mainstream with the ubiquitous mobile phones and now internet enabled devicesthe line between the digital and physical worlds is more blurred than it ever was at the heart of all this is data data is at the center of everything around usbe it financesupply chainsmedical sciencespace explorationcommunicationand what not it is not surprising that we have generated of the world' data in just the last few years and this is just the beginning rightlydata is being termed as the oil of the st century the last couple of introduced the concepts of machine learning and the python ecosystem to get started this introduces the core entity upon which the machine learning world relies to show its magic and wonders everything digital has data at its core in some form or the other data is generated at various rates by numerous sources across the globe in numerous formats before we dive into the specifics of machine learningwe will spend some time and effort understanding this central entity called data it is important that we understand various aspects of it and get equipped with different techniques to handle it based on requirements in this we will cover the journey data takes through typical machine learning related use case where it goes from its initial raw form to form where it can be used by machine learning algorithmsmodels to work upon we cover various data formatsprocessing and wrangling techniques to get the data into form where it can be utilized by machine learning algorithms for analysis we also learn about different visualization techniques to better understand the data at hand together these techniques will help us be prepared for the problems to be solved in the coming as well as in real-world scenarios introduced the crisp-dm methodology it is one of the standard workflows followed by data science teams across the world in the coming sections of this we will concentrate on the following sub-sections of this methodologydata collectionto understand different data retrieval mechanisms for different data types data descriptionto understand various attributes and properties of the data collected data wranglingto prepare data for consumption in the modeling steps data visualizationto visualize different attributes for sharing resultsbetter understandingand so on the code samplesjupyter notebooksand sample datasets for this are available in the github repository for this book at under the directory/folder for (cdipanjan sarkarraghav bali and tushar sharma sarkar et al practical machine learning with python