From Data To Models And Back
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From Data to Models and Back
This book constitutes the refereed proceedings of the 9th International Symposium on From Data Models and Back, DataMod 2020, held virtually, in October 2020. The 11 full papers and 3 short papers presented in this book were selected from 19 submissions. The papers are grouped in these topical sections: machine learning; simulation-based approaches, and data mining and processing related approaches.
From Data to Models and Back
Author: Ricardo M. Czekster
language: en
Publisher: Springer Nature
Release Date: 2025-04-18
This book constitutes revised selected papers of the 12th International Symposium on From Data Models and Back, DataMod 2024, held in Aveiro, Portugal, during November 4–5, 2024. The 9 full papers included in this book were carefully reviewed and selected from 15 submissions. These papers present research results in the areas of knowledge management, data mining and machine learning, as well as application experiences, tools and promising preliminary ideas.
R for Data Science
Author: Hadley Wickham
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-12-12
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results