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This book organizes in one place the mathematics, probability, statistics and machine learning information that is required for a practitioner of cybersecurity analytics, as well as the basics of cybersecurity needed for a practitioner.
Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models and more.
With a quick start guide to epidemiological data visualization and analysis in R, this book spans the gulf between academia and practices providing many lively, instructive data analysis examples using the most up-to-date data, such as the newly discovered COVID-19.
This book provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary.
Massive Graph Analytics provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. The book will be beneficial to students, researchers and practitioners, in academia, national laboratories, and industry in massive scale graph analytics.
Highlights a combination of two aspects: technical concreteness and holistic thinking. Authors discuss what principles are used to invent these techniques, what assumptions are made, how mathematics is used to articulate these assumptions, and how these formulations generalize a range of real-world applications into generic and abstract forms.
"A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source"--
Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable reference for researchers involved with model selection, variable selection, machine learning, and risk management.
The book begins by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning during the rest of the book.
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