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This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry;
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In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R;
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business questions. Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps;
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demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more. Practical Data;
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Qualitative and Mixed Methods Data Analysis Using Dedoose (R): A Practical Approach for Research Across the Social Sciences provides both;
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collaborate on data science projects with GitHub Master R language fundamentals: syntax, programming concepts, and data structures Load, format;
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is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for;
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In this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them. All;
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In this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them;
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Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this;
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Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice;
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Use R for efficient data mining, text analytics and feature engineering Become a thorough data science professional with the help of hands-on;
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professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop (R) and Spark is your complete guide to;
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Data Science for Business with R focuses on the concepts foundational for students starting a business analytics or data science course;
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data sets with R so that students learn how to get started with R, along with computational examples in each section, and then students learn;
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. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with;
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This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior;
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database administrators with some or no experience in R but who are eager to easily deliver practical data science solutions in their day-to-day;
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by the R community * Become familiar with the lattice graphics package for high-level data visualization * Learn about bioinformatics;
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of situations with examples using the two most popular programming languages for data analysis-R and Python. Style and approach This step-by-step;
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Machine Learning with R, tidyverse, and mlr teaches readers how to gain valuable insights from their data using the powerful R programming;
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does a data analysis project in education look like? If you're just getting started with R in an education job, this is the book you'll;
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does a data analysis project in education look like? If you're just getting started with R in an education job, this is the book you'll;
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using statistics and the R programming language * Visualize and explore data, including working with large data sets found in big data;
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Book * Get a firm hold on the fundamentals of R through practical hands-on examples * Get started with good R programming fundamentals for;
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how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks;
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science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the;
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