Title
Data Analysis With R: Load, Wrangle, And Analyze Your Data Using The World'S Most Powerful Statistical Programming Language
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Key Features Load, Manipulate And Analyze Data From Different Sources Gain A Deeper Understanding Of Fundamentals Of Applied Statistics A Practical Guide To Performing Data Analysis In Practice Book Descriptionfrequently The Tool Of Choice For Academics, R Has Spread Deep Into The Private Sector And Can Be Found In The Production Pipelines At Some Of The Most Advanced And Successful Enterprises. The Power And Domainspecificity Of R Allows The User To Express Complex Analytics Easily, Quickly, And Succinctly. With Over 7,000 User Contributed Packages, It'S Easy To Find Support For The Latest And Greatest Algorithms And Techniques.Starting With The Basics Of R And Statistical Reasoning, Data Analysis With R Dives Into Advanced Predictive Analytics, Showing How To Apply Those Techniques To Realworld Data Though With Realworld Examples.Packed With Engaging Problems And Exercises, This Book Begins With A Review Of R And Its Syntax. From There, Get To Grips With The Fundamentals Of Applied Statistics And Build On This Knowledge To Perform Sophisticated And Powerful Analytics. Solve The Difficulties Relating To Performing Data Analysis In Practice And Find Solutions To Working With Messy Data , Large Data, Communicating Results, And Facilitating Reproducibility.This Book Is Engineered To Be An Invaluable Resource Through Many Stages Of Anyone'S Career As A Data Analyst.What You Will Learn Navigate The R Environment Describe And Visualize The Behavior Of Data And Relationships Between Data Gain A Thorough Understanding Of Statistical Reasoning And Sampling Employ Hypothesis Tests To Draw Inferences From Your Data Learn Bayesian Methods For Estimating Parameters Perform Regression To Predict Continuous Variables Apply Powerful Classification Methods To Predict Categorical Data Handle Missing Data Gracefully Using Multiple Imputation Identify And Manage Problematic Data Points Employ Parallelization And Rcpp To Scale Your Analyses To Larger Data Put Best Practices Into Effect To Make Your Job Easier And Facilitate Reproducibility Table Of Contents Refresher The Shape Of Data Describing Relationships Probability Using Data To Reason About The World Testing Hypotheses Bayesian Methods Predicting Continuous Variables Predicting Categorical Variables Sources Of Data Dealing With Messy Data Dealing With Large Data Reproducibility And Best Practices
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- Q: How many pages does this book have? A: This book has three hundred sixty-three pages. It provides a comprehensive guide to data analysis using R.
- Q: What is the binding type of the book? A: The book is available in paperback binding. This makes it easy to handle and read.
- Q: What are the dimensions of the book? A: The book measures seven point five two inches in length, zero point eight seven inches in width, and nine point two five inches in height. These dimensions make it portable and easy to store.
- Q: Who is the author of this book? A: The author of this book is Tony Fischetti. He is known for his expertise in data analysis and programming.
- Q: What is the main focus of this book? A: The book focuses on data analysis using the R programming language. It covers topics from basic to advanced analytics.
- Q: What skill level is this book suitable for? A: The book is suitable for both beginners and experienced data analysts. It starts with fundamentals and progresses to advanced techniques.
- Q: How can I apply the knowledge from this book? A: You can apply the knowledge by practicing with real-world data examples provided in the book. It includes exercises to enhance your analytical skills.
- Q: Is this book good for learning statistical reasoning? A: Yes, this book teaches statistical reasoning and sampling methods. It helps readers understand how to draw inferences from data.
- Q: Can I use this book for learning R programming? A: Yes, this book serves as a practical guide for learning R programming. It includes syntax and application of R in data analysis.
- Q: How should I store this book? A: Store the book in a cool, dry place away from direct sunlight. This will help maintain its condition over time.
- Q: What if the book arrives damaged? A: If the book arrives damaged, you can return it for a replacement or refund. Check the return policy of the seller for specific instructions.
- Q: Are there any exercises included in this book? A: Yes, the book includes engaging problems and exercises. These help reinforce the concepts learned throughout the chapters.
- Q: Does this book cover handling missing data? A: Yes, it provides guidance on handling missing data using multiple imputation techniques. This is crucial for accurate analysis.
- Q: Is this book suitable for someone with no background in data analysis? A: Yes, it is suitable for beginners. The book starts with basic concepts and gradually introduces advanced topics.
- Q: What methodologies are discussed in the book? A: The book discusses methodologies like regression, Bayesian methods, and hypothesis testing. These are essential for data analysis.