{"product_id":"beginning-data-science-in-r-4-data-analysis-visualization-and-modelling-for-the-data-scientist-paperback","title":"Beginning Data Science in R 4: Data Analysis, Visualization, and Modelling for the Data Scientist - Paperback","description":"\u003cp\u003eby \u003cb\u003eThomas Mailund\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eDiscover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. \u003cbr\u003e\u003ci\u003eBeginning Data Science in R 4, Second Edition\u003c\/i\u003e details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. \u003cbr\u003eModern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well.\u003cbr\u003eSource code is available at github.com\/Apress\/beg-data-science-r4. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003ePerform data science and analytics using statistics and the R programming language\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eVisualize and explore data, including working with large data sets found in big data\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eBuild an R package\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eTest and check your code\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003ePractice version control\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eProfile and optimize your code\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003e\u003cbr\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cbr\u003e\u003c\/b\u003eThose with some data science or analytics background, but not necessarily experience with the R programming language.\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003eDiscover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.\u003cbr\u003e\u003ci\u003eBeginning Data Science in R 4, Second Edition\u003c\/i\u003e details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. \u003cbr\u003eThis book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. \u003cbr\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ePerform data science and analytics using statistics and the R programming language\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eVisualize and explore data, including working with large data sets found in big data\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eBuild an R package\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eTest and check your code\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003ePractice version control\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eProfile and optimize your code\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003eThomas Mailund is an associate professor in bioinformatics at Aarhus University, Denmark. His background is in math and computer science but for the last decade his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.\u003cbr\u003e\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 511\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.09 x 10 x 7 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 24, 2022\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":47447910219954,"sku":"9781484281543","price":59.38,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0770\/3891\/1666\/files\/60cab399fedaca959b95aec74ca3a3c0.webp?v=1778747357","url":"https:\/\/box.dadyminds.org\/products\/beginning-data-science-in-r-4-data-analysis-visualization-and-modelling-for-the-data-scientist-paperback","provider":"DADYMINDS BOX","version":"1.0","type":"link"}