Requiring no prior programming experience and packed with tons of practical examples, step-by-step exercises, and sample code, this friendly and accessible guide shows you how to know your way around lists, data frames, and other R data structures, while learning to interact with other programs, such as Microsoft Excel. You'll learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and so much more.
R is an open source statistical environment and programming language that has become very popular in varied fields for the management and analysis of data. R provides a wide array of statistical and graphical techniques, and has become the standard among statisticians for software development and data analysis.
This will help yo Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers.
This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations.
It not only shows how to use Rs powerful graphic tools to construct t Introduction to R for Quantitative Finance. R is a statistical computing language that's ideal for answering quantitative finance questions. This book gives you both theory and practice, all in clear language with stacks of real-world examples. It is divided into six parts and each part is further divided into different chapters. The first two parts provide basic knowledge explaining core concepts and setting up different tools such as Installing RStudio and Python distribution.
All of these parts are divided into a total of 23 chapters that discuss different aspects related to machine learning one by one. Machine Learning For Dummies is a help to guide anyone gearing up to learn machine-learning in an effective way. The most important aspect of this book is its simple and easy-to-understand language to explain different concepts in a systematic manner from very basics to an advanced level step by step.
Download this book and make yourself learn Machine learning in a simple yet effective way. All the knowledge shared in this book would help any beginner to learn from this book easily and develop a firm understanding of its working. After learning from this book, you will be able to apply your knowledge of machine learning to real-life problems. The authors of this book, John Mueller and Lucca Massaron wrote it specifically for beginners with little to no prior experience and take a start from scratch.
Requiring no prior programming experience and packed with tons of practical examples, step-by-step exercises, and sample code, this friendly and accessible guide shows you how to know your way around lists, data frames, and other R data structures, while learning to interact with other programs, such as Microsoft Excel. You'll learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and so much more. R is an open source statistical environment and programming language that has become very popular in varied fields for the management and analysis of data.
R provides a wide array of statistical and graphical techniques, and has become the standard among statisticians for software development and data analysis. R For Dummies, 2nd Edition takes the intimidation out of working with R and arms you with the knowledge and know-how to master the programming language of choice among statisticians and data analysts worldwide.
0コメント