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Bøker i Chapman & Hall/CRC Data Science Series-serien

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  • - A Practical Introduction with Applications in R
    av Brandon M. (University of Cincinnati Greenwell
    1 208,-

    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.

  • - A Practical Approach for Predictive Models
    av Max Kuhn, USA.) Johnson & Kjell (North Carolina State University
    645 - 890,-

  • av Tracey Dowdeswell & Nachshon (Sean) Goltz
    626 - 2 025,-

  • av Jerry Davis
    1 083,-

    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.

  • av Peter Prevos
    626 - 2 010,-

  • av Kailash Awati & Alexander (University Technology of Sydney Scriven
    626 - 1 620,-

  • av Diego Miranda-Saavedra
    607,-

  • av Przemyslaw Biecek & Tomasz (Hasselt University Burzykowski
    656 - 1 560,-

  • - An Introduction
    av Melissa Lee, Tiffany-Anne (University of British Columbia) Timbers & Trevor Campbell
    650 - 1 988,-

  • av Daniel T. (School of Public Policy and Urban Affairs O'Brien
    705 - 1 941,-

  • av Tiffany Timbers
    783 - 1 895,-

    Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows.Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects.The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia's DSCI100: Introduction to Data Science course.

  • av Lily Wang
    1 083,-

    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.

  • Spar 10%
    - Math + R + Data
    av Norman Matloff
    760 - 2 465,-

  • av Julia Silge & Emil Hvitfeldt
    713 - 2 092,-

  • av David A. Bader
    1 928,-

    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.

  • - Code and Context for Data Science in Government
    av Ken Steif
    683 - 1 632,-

  • av Harry G. Perros
    683 - 1 745,-

  • - A Small Data Approach
    av Shuai Huang
    1 095,-

    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.

  • - Learn R and Python in Parallel
    av Nailong Zhang
    705 - 1 845,-

    "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"--

  • av David J. Marchette & Rakesh M. Verma
    652 - 1 632,-

    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.

  • - With Applications in R
    av Italy) Zuccolotto, Paola (University of Brescia, Italy) Manisera & m.fl.
    701 - 2 056,-

  • av Jianqing (Princeton University Fan
    1 440,-

    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.

  • av Greg Wilson, Heidelberg, Maya Gans, m.fl.
    705 - 2 052,-

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