Data Analytics: A Theoretical and Practical View from the EDISON ProjectКНИГИ » ОС И БД
Название: Data Analytics: A Theoretical and Practical View from the EDISON Project Автор: Juan J. Cuadrado-Gallego, Yuri Demchenko Издательство: Springer Год: 2023 Страниц: 486 Язык: английский Формат: pdf Размер: 10.1 MB
Building upon the knowledge introduced in The Data Science Framework, this book provides a comprehensive and detailed examination of each aspect of Data Analytics, both from a theoretical and practical standpoint. The book explains representative algorithms associated with different techniques, from their theoretical foundations to their implementation and use with software tools.
Designed as a textbook for a Data Analytics Fundamentals course, it is divided into seven chapters to correspond with 16 weeks of lessons, including both theoretical and practical exercises. Each chapter is dedicated to a lesson, allowing readers to dive deep into each topic with detailed explanations and examples. Readers will learn the theoretical concepts and then immediately apply them to practical exercises to reinforce their knowledge. And in the lab sessions, readers will learn the ins and outs of the R environment and Data Science methodology to solve exercises with the R language. With detailed solutions provided for all examples and exercises, readers can use this book to study and master data analytics on their own. Whether you're a student, professional, or simply curious about data analytics, this book is a must-have for anyone looking to expand their knowledge in this exciting field.
That first book was about the EDISON Data Science Framework (EDSF) developed by the EDISON project, whose definition of Data Science and Data Scientist as a profession that became widely accepted by the academic and professional communities. The book has been thought to help to start the learning of the techniques and algorithms used in data analytics and start dealing with their computational implementations. The book is intended to be used, both, as a text book to teach the concepts in courses about data analytics at graduate or postgraduate levels and to learn the data analytics knowledge by the practitioner readers by themselves. The book provides suggestions on how to use it for both purposes.
What is Data Science? All the content of this book has been created with the goal of providing the reader the foundational knowledge of Data Analytics, and the first notion that must be known is that Data Analytics is a part of Data Science. Consequently, to start the study of Data Analytics, we define what Data Science is. There are multiple definitions of the data science discipline and technology that stress/put in the centre one of the four flavours/goals of data analysis:
• Data Analytics is a process of inspecting, transforming, and modelling data with the goal of discovering trends, patterns, or relations that describe observable real-life phenomena and can be used for informed decision-making.
• Data Science involves the systematic study of the structure and behaviour of data to understand past and current occurrences and predict the future behaviour of those data. Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
What is Data Science in practice?
Data Science is a complex discipline that uses conceptual and mathematical abstractions and models, statistical methods, together with modern computational tools to obtain knowledge and derive insight from data to (uncover correlations and causations in business data) support decision making in scientific research and business activity.
If we must define Data Science in only one sentence: Science that studies how to obtain knowledge from Data.
This second book follows the series started in the first book, but its conception and development are totally different from the first one: if the first book is a theoretical book that was thought to present the framework of the whole data science discipline from an absolutely theoretical point of view, this second book does not present the whole data science discipline but only one of its six knowledge area groups, the data analytics knowledge area group, and presents it from an absolutely practical point of view. if in the first book there is neither solved nor even proposed any practical exercise, in this second book have been conceived and written with the practical exercise’s resolution as the main structural element of the book.
What is the reader to do? The answer is Exercises. Exercises, for the practical application of each of the theoretical concepts taught. The book will provide the reader with its complete detailed solution of all the exercises stated. However, the authors strongly ask the readers not to look at these solutions until they have solved the exercises themselves. If you look at the solutions before solving the exercise, it loses all its effectiveness as a learning element, and if this is done repeatedly, the book loses an important part of its value. However, it is important to make it very clear that this is not a “problems book”, since the theoretical concepts are exposed with length and depth.
This practical exercise must be solved on paper with the help of a hand calculator. During the laboratory session of the same week, you will learn how to solve with the use of the environment and the R language the same exercises that were solved in class in the theory session. Learning and deepening the knowledge of R will occur in parallel with that of Data Science.
With this structure, after starting with an introduction, the contents of the lesson are presented in a theoretical-practical manner; that is, after each theoretical concept is introduced, an exercise to apply that concept is presented and solved in detail without the help of any software tool. Then, the examples that have been previously solved with the R software tool. Finally, all the learning of the contents of all the lessons is reinforced with the resolution of a set of proposed exercises, in which solutions, with and without the use of software tools, are explained in depth.
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Data Analytics: A Theoretical and Practical View from the EDISON Project 
