Python Data Science Handbook

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

This self-contained handbook provides a carefully researched, compact source of key earth science information and data, logically sorted by subject matter, and then cross-referenced. Appealing to both

For readers who may not be statisticians or data analysts, Sarmento and Costa explain how to analyze data using the Python and R languages. They have particularly in mind, students and researchers

"This book investigates the incorporation of pattern management in business technologies as well as decision-making and prediction process. It brings together the common issues of data management and

"This book presents the concepts of big data, explores its analytics and technologies and their applications and develops an understanding of issues pertaining to the use of big data in multidisciplin

Finding a good data scientist has been likened to hunting for a unicorn. The required combination of software engineering skills, mathematical fluency, and business savvy are simply very hard to find

With a definition of geographic information science so broad as to include any aspect of the capture, storage, integration, management, retrieval, display, analysis, and modeling of spatial data and a

"This publication is a key resource on the latest advancements in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlig

Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targe