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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.
This edited book first consolidates the results of the EU-funded
EDISON project (Education for Data Intensive Science to Open New
science frontiers), which developed training material and
information to assist educators, trainers, employers, and research
infrastructure managers in identifying, recruiting and inspiring
the data science professionals of the future. It then deepens the
presentation of the information and knowledge gained to allow for
easier assimilation by the reader. The contributed chapters are
presented in sequence, each chapter picking up from the end point
of the previous one. After the initial book and project overview,
the chapters present the relevant data science competencies and
body of knowledge, the model curriculum required to teach the
required foundations, profiles of professionals in this domain, and
use cases and applications. The text is supported with appendices
on related process models. The book can be used to develop new
courses in data science, evaluate existing modules and courses,
draft job descriptions, and plan and design efficient
data-intensive research teams across scientific disciplines.
This edited book first consolidates the results of the EU-funded
EDISON project (Education for Data Intensive Science to Open New
science frontiers), which developed training material and
information to assist educators, trainers, employers, and research
infrastructure managers in identifying, recruiting and inspiring
the data science professionals of the future. It then deepens the
presentation of the information and knowledge gained to allow for
easier assimilation by the reader. The contributed chapters are
presented in sequence, each chapter picking up from the end point
of the previous one. After the initial book and project overview,
the chapters present the relevant data science competencies and
body of knowledge, the model curriculum required to teach the
required foundations, profiles of professionals in this domain, and
use cases and applications. The text is supported with appendices
on related process models. The book can be used to develop new
courses in data science, evaluate existing modules and courses,
draft job descriptions, and plan and design efficient
data-intensive research teams across scientific disciplines.
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