Requirements: .ePUB, .PDF reader, 30.5 MB
Overview: Geospatial Data Science Essentials is your hands-on guide to mastering the science of geospatial analytics using Python. Designed for practitioners and enthusiasts alike, this book distills years of experience by wrapping up 101 key concepts from theory to implementation, ensuring you gain a practical understanding of the tools and methods that define the geospatial Data Science landscape today. Whether you are a seasoned data scientist, a GIS professional, a newcomer to spatial data, or simply a map lover, this book provides you solid foundation to level up your skills. The book is centered around practicalities, as you will explore real-world examples with compact code throughout ten topics and 101 sections. From understanding spatial data structures to leveraging advanced analytical techniques, from spatial networks to Machine Learning, this book equips you with a wide range of knowledge to navigate and succeed in the rapidly evolving field of geospatial Data Science. Creating a starting point for this book was truly a challenge, even after spending countless days and nights rewriting and revising section after section. So, cutting it short, my goal was simply to compile the essentials of geospatial Data Science in Python that I wish someone had handed me when I began working with spatial data. This book avoids overburdening details and theoretical depths — there are great books on that already. Instead, I wanted to focus on the most practical aspects and tons of Python coding on geospatial Data Science topics. My aim was to show you how to use Python for geospatial analytics, to provide a solid foundation, and to offer an overview of the different tools and methods of the current geospatial Data Science stack.
Genre: Non-Fiction > Tech & Devices
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