{"title":"Rafael A Irizarry","description":null,"products":[{"product_id":"introduction-to-data-science-book-rafael-a-irizarry-9781032116556","title":"Introduction to Data Science","description":"Unlike the first edition, the new edition has been split into two books.  Thoroughly revised and updated, this is the first book of the second edition of Introduction to Data Science: Data Wrangling and Visualization with R. It introduces skills that can help you tackle real-world data analysis challenges. These include R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX\/Linux shell, version control with Git and GitHub, and reproducible document preparation with Quarto and knitr. The new edition includes additional material on data.table, locales, and accessing data through APIs. The book is divided into four parts: R, Data Visualization, Data Wrangling, and Productivity Tools. Each part has several chapters meant to be presented as one lecture and includes dozens of exercises. The second book will cover topics including probability, statistics and prediction algorithms with R.  Throughout the book, we use motivating case studies. In each case study, we try to realistically mimic a data scientist’s experience. For each of the skills covered, we start by asking specific questions and answer these through data analysis. Examples of the case studies included in the book are: US murder rates by state, self-reported student heights, trends in world health and economics, and the impact of vaccines on infectious disease rates.  This book is meant to be a textbook for a first course in Data Science. No previous knowledge of R is necessary, although some experience with programming may be helpful. To be a successful data analyst implementing these skills covered in this book requires understanding advanced statistical concepts, such as those covered the second book. If you read and understand all the chapters and complete all the exercises in this book, and understand statistical concepts, you will be well-positioned to perform basic data analysis tasks and you will be prepared to learn the more advanced concepts and skills needed to become an expert.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51017636708625,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":51017639166225,"sku":"NIN9781032116556","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":52111199994129,"sku":"NGR9781032116556","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52586853892369,"sku":"NLS9781032116556","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1032116552.jpg?v=1751237829"},{"product_id":"data-analysis-for-the-life-sciences-with-r-book-rafael-a-irizarry-9781498775670","title":"Data Analysis for the Life Sciences with R","description":"This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51734511649041,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ VERY_GOOD \/ SBYB","offer_id":51734512566545,"sku":"CIN1498775675VG","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ VERY_GOOD \/ INTERNAL","offer_id":52652409487633,"sku":"GOR008633864","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ GOOD \/ SBYB","offer_id":52981809021201,"sku":"CIN1498775675G","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9781498775670.jpg?v=1750699219"},{"product_id":"introduction-to-data-science-book-rafael-a-irizarry-9781032419879","title":"Introduction to Data Science","description":"Introduction to Data Science: Statistics and Prediction Algorithms Through Case Studies teaches data science as a way of thinking statistically, not just as a collection of computational tools. Building on the topics covered in Introduction to Data Science: Data Wrangling and Visualization with R, this book is designed for students with some programming experience and basic mathematical maturity, this book builds the foundations of probability, statistical inference, regression, high-dimensional data analysis, and machine learning through real data examples and reproducible R code. It is suitable for one-semester course in advanced data science.  The book shows how to reason about variability, uncertainty, prediction error, model assumptions, and validation. Through case studies involving polling, genetics, baseball, recommendation systems, image classification, and other modern datasets, readers learn how to connect probability models to data, summarize complex information, quantify uncertainty, fit and interpret models, evaluate prediction algorithms, and understand the statistical ideas behind machine learning. Each chapter is designed to support classroom teaching, self-study, and hands-on analysis, with exercises and companion web materials available through the book website.  Key Features:    Includes base R, data.table, and tidyverse code. Focuses on the statistical and probabilistic foundations of machine learning. Contains real-world case studies.  Rafael A. Irizarry is Professor and Chair of the Department of Data Science at Dana-Farber Cancer Institute and Professor of Applied Statistics at Harvard. 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