00000nam a22000004a 4500 UP-8027390931311614638 Buklod 20250609131908.0 m |o d | cr ||||||||||| 250228s2022 xxu r |||| u|eng d 2021054755 9781000579642 (epub) 9781003080978 (ebook) 9780367532178 (hardback) 9780367524685 (paperback) DLC DLC DLC rda eng eng QA 276.45 T56 2022 519.50285/5133 23/eng20220301 Timbers, Tiffany-Anne author. Data science an introduction Tiffany-Anne Timbers, Trevor Campbell and Melissa Lee. First edition. 2022. Boca Raton : CRC Press xxi, 420 pages 25 cm text rdacontent txt unmediated rdamedia n volume rdacarrier nc Statistics Includes bibliographical references and index. R and the tidyverse -- Reading in data locally and from the Web -- Cleaning and wrangling data -- Effective data visualization -- Classification I : training & predicting -- Classification II : evaluation & tuning -- Regression I : K-nearest neighbors -- Regression II : linear regression -- Clustering -- Statistical inference -- Combining code and text rwith Jupyter -- Collaboration with version control -- Setting up your computer. "Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models. Based on sound educational research and active learning principles, the book uses a modern approach to the R programming language and accompanying sheets for self-directed learning this book will leave students well-prepared for data science projects. Data Science: An Introduction focuses on workflows and communication strategies that are clear, reproducible, and shareable. Aimed at first year undergraduates with only minimal prior knowledge of mathematics and programming this book is suitable for students across many disciplines. All source code is available online as a GitHub repository, demonstrating the use of good reproducible and clear project workflows and is also accompanied by autograded Jupyter worksheets, providing the reader with guided interactive instruction"-- Provided by publisher. Description based on print version record and CIP data provided by publisher. Quantitative research Data processing Textbooks. Mathematical statistics Data processing Textbooks. R (Computer program language) Textbooks. Campbell, Trevor author. (Professor of statistics),. Lee, Melissa author. (Statistics educator),. Timbers, Tiffany-Anne. Print version: Data science Boca Raton : CRC Press, 2022 9780367532178 (DLC) 2021054754 First edition. FO UPD DSTC QA 276.45 T56 2022 Book