Description
Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.
A hands-on textbook for courses on large-scale data analytics and designing machine learning solutions.
About the Author
Isaac Triguero is Distinguished Senior Researcher at the Department of Computer Science and Artificial Intelligence, University of Granada, and Associate Professor of Data Science at the School of Computer Science of the University of Nottingham. He won the 2019 School of Computer Science - University of Nottingham Award for Teaching. Mikel Galar is Associate Professor of Computer Science and Artificial Intelligence at the Department of Statistics, Computer Science and Mathematics, Public University of Navarre. He is a co-founder of Neuraptic AI and won the 2020 Excellence in Teaching Award of the Public University of Navarre.
Reviews
'With the growing ubiquity of large and complex datasets, MapReduce and Spark's dataflow programming models have become mission-critical skills for data scientists, data engineers, and ML engineers. Triguero and Galar leverage their extensive teaching experience on this topic to deliver this tour de force deep dive into both the technical concepts and programming knowhow needed for such modern large-scale data analytics. They interleave intuitive exposition of the concepts and examples from data engineering and classical ML pipelines with well-thought-out hands-on code and outputs. This book not only shows how all this knowledge is useful in practice today but also sets up the reader to be able to successfully 'generalize' to future workloads.' Arun Kumar, University of California, San Diego
Book Information
ISBN 9781009318259
Author Isaac Triguero
Format Paperback
Page Count 422
Imprint Cambridge University Press
Publisher Cambridge University Press
Weight(grams) 780g
Dimensions(mm) 245mm * 170mm * 20mm
A hands-on textbook for courses on large-scale data analytics and designing machine learning solutions.
About the Author
Isaac Triguero is Distinguished Senior Researcher at the Department of Computer Science and Artificial Intelligence, University of Granada, and Associate Professor of Data Science at the School of Computer Science of the University of Nottingham. He won the 2019 School of Computer Science - University of Nottingham Award for Teaching. Mikel Galar is Associate Professor of Computer Science and Artificial Intelligence at the Department of Statistics, Computer Science and Mathematics, Public University of Navarre. He is a co-founder of Neuraptic AI and won the 2020 Excellence in Teaching Award of the Public University of Navarre.
Reviews
'With the growing ubiquity of large and complex datasets, MapReduce and Spark's dataflow programming models have become mission-critical skills for data scientists, data engineers, and ML engineers. Triguero and Galar leverage their extensive teaching experience on this topic to deliver this tour de force deep dive into both the technical concepts and programming knowhow needed for such modern large-scale data analytics. They interleave intuitive exposition of the concepts and examples from data engineering and classical ML pipelines with well-thought-out hands-on code and outputs. This book not only shows how all this knowledge is useful in practice today but also sets up the reader to be able to successfully 'generalize' to future workloads.' Arun Kumar, University of California, San Diego
Book Information
ISBN 9781009318259
Author Isaac Triguero
Format Paperback
Page Count 422
Imprint Cambridge University Press
Publisher Cambridge University Press
Weight(grams) 780g
Dimensions(mm) 245mm * 170mm * 20mm
