Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and more » shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics.
This book will discuss and explore the following through examples and case studies:
* An introduction to R: data management and R functions
* The architecture, framework, and life cycle of a business analytics project
* Descriptive analytics using R: descriptive statistics and data cleaning
* Data mining: classification, association rules, and clustering
Predictive analytics: simple regression, multiple regression, and logistic regression
This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book.
What You Will Learn
• Write R programs to handle data
• Build analytical models and draw useful inferences from them
• Discover the basic concepts of data mining and machine learning
• Carry out predictive modeling
• Define a business issue as an analytical problem
Who This Book Is For
Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals. « less
Advanced Statistical Programming for Data Science, Analysis and Finance
Master functions and discover how to write functional programs in R. In this concise book, you'll make your functions pure by avoiding side-effects; you’ll write functions that manipulate other functions, and you’ll construct complex functions using simpler functions as building blocks.
In Functional more » Programming in R, you’ll see how we can replace loops, which can have side-effects, with recursive functions that can more easily avoid them. In addition, the book covers why you shouldn't use recursion when loops are more efficient and how you can get the best of both worlds.
Functional programming is a style of programming, like object-oriented programming, but one that focuses on data transformations and calculations rather than objects and state. Where in object-oriented programming you model your programs by describing which states an object can be in and how methods will reveal or modify that state, in functional programming you model programs by describing how functions translate input data to output data. Functions themselves are considered to be data you can manipulate and much of the strength of functional programming comes from manipulating functions; that is, building more complex functions by combining simpler functions.
What You'll Learn * Write functions in R including infix operators and replacement functions
* Create higher order functions
* Pass functions to other functions and start using functions as data you can manipulate
* Use Filer, Map and Reduce functions to express the intent behind code clearly and safely
* Build new functions from existing functions without necessarily writing any new functions, using point-free programming
* Create functions that carry data along with them
Who This Book Is For
Those with at least some experience with programming in R. « less
* Understand the basics of R and how they can be applied in various Quantitative Finance scenarios
* Learn various algorithmic trading techniques and ways to optimize them using the tools available in R.
* Contain different methods to manage risk and explore trading using Machine Learning.
BOOK more » DESCRIPTION
The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language.
You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate.
We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging.
By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R.
WHAT YOU WILL LEARN
* Get to know the basics of R and how to use it in the field of Quantitative Finance
* Understand data processing and model building using R
* Explore different types of analytical techniques such as statistical « less
Data Programming and the Cloud
Program for data analysis using R and learn practical skills to make your work more efficient. This book covers how to automate running code and the creation of reports to share your results, as well as writing functions and packages. Advanced R is not designed to teach advanced R programming nor to more » teach the theory behind statistical procedures. Rather, it is designed to be a practical guide moving beyond merely using R to programming in R to automate tasks.
This book will show you how to manipulate data in modern R structures and includes connecting R to data bases such as SQLite, PostgeSQL, and MongoDB. The book closes with a hands-on section to get R running in the cloud. Each chapter also includes a detailed bibliography with references to research articles and other resources that cover relevant conceptual and theoretical topics.
What You Will Learn
* Write and document R functions
* Make an R package and share it via GitHub or privately
* Add tests to R code to insure it works as intended
* Build packages automatically with GitHub
* Use R to talk directly to databases and do complex data management
* Run R in the Amazon cloud
* Generate presentation-ready tables and reports using R
Who This Book Is For
Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to take their R coding and programming to the next level. « less
Notes on R: A Programming Environment for Data Analysis and Graphics. Version 3.3.1 (2016-06-21)
This introduction to R is derived from an original set of notes describing the S and S-Plus environments written in 1990–2 by Bill Venables and David M. Smith when at the University of Adelaide. We have made a number of small changes to reflect differences between the R and S programs, more » and expanded some of the material.
We would like to extend warm thanks to Bill Venables (and David Smith) for granting permission to distribute this modified version of the notes in this way, and for being a supporter of R from way back.
Comments and corrections are always welcome. Please address email correspondence to R-core@R-project.org. « less
Quantitative Research and Platform Development
This book explains the broad topic of automated trading, starting with its mathematics and moving to its computation and execution. Readers will gain a unique insight into the mechanics and computational considerations taken in building a backtester, strategy optimizer, and fully functional trading platform.
Automated more » Trading with R provides automated traders with all the tools they need to trade algorithmically with their existing brokerage, from data management, to strategy optimization, to order execution, using free and publically available data. If your brokerage’s API is supported, the source code is plug-and-play.
The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage. The book’s three objectives are:
* To provide a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail traders.
* To offer an understanding the internal mechanisms of an automated trading system.
* To standardize discussion and notation of real-world strategy optimization problems.
What you’ll learn
* Programming an automated strategy in R gives the trader access to R and its package library for optimizing strategies, generating real-time trading decisions, and minimizing computation time.
* How to best simulate strategy performance in their specific use case to derive accurate performance estimates.
* Important machine-learning criteria for statistical validity in the context of time-series.
* An understanding of critical real-world variables pertaining to portfolio management and performance assessment, including latency, drawdowns, varying trade size, portfolio growth, and penalization of unused capital.
Who This Book Is For
This book is for traders/practitioners at the retail or small fund level with at least an undergraduate background in finance or computer science. Graduate level finance or data science students. « less
Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, more » the choice of data, the choice of tools, and the choice of algorithms.
Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing.
The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings. « less
A Practical Guide to Smarter Programming
Become a more productive programmer with Efficient R Programming. Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace give practical advice on a range of topics—from optimizing set-up of RStudio to leveraging C++—that make this book a valuable asset for both more » experienced and novice programmers. It’s suitable for academics, business users, and programmers from a wide range of backgrounds.
* Get practical, tried-and-true advice from longtime R instructors
* Dive into a wide range of topics, including RStudio set-up and leveraging C++, suitable for all skill levels
* Gain insight into RStudio’s functionality to boost code-writing productivity
* Learn the necessary skills for team-based R programming work
* Save time, and energy, debugging code and searching online forums « less
Up-to-Date Guidance from One of the Foremost Members of the R Core Team
Written by John M. Chambers, the leading developer of the original S software, Extending R covers key concepts and techniques in R to support analysis and research projects. It presents the core ideas of R, provides programming more » guidance for projects of all scales, and introduces new, valuable techniques that extend R.
The book first describes the fundamental characteristics and background of R, giving readers a foundation for the remainder of the text. It next discusses topics relevant to programming with R, including the apparatus that supports extensions. The book then extends R’s data structures through object-oriented programming, which is the key technique for coping with complexity. The book also incorporates a new structure for interfaces applicable to a variety of languages.
A reflection of what R is today, this guide explains how to design and organize extensions to R by correctly using objects, functions, and interfaces. It enables current and future users to add their own contributions and packages to R. « less
Increase speed and performance of your applications with efficient data structures and algorithms
ABOUT THIS BOOK
* See how to use data structures such as arrays, stacks, trees, lists, and graphs through real-world examples
* Find out about important and advanced data structures such as searching and sorting algorithms
* Understand important concepts such as big-o notation, dynamic programming, more » and functional data structured
WHO THIS BOOK IS FOR
This book is for R developers who want to use data structures efficiently. Basic knowledge of R is expected.
WHAT YOU WILL LEARN
* Understand the rationality behind data structures and algorithms
* Understand computation evaluation of a program featuring asymptotic and empirical algorithm analysis
* Get to know the fundamentals of arrays and linked-based data structures
* Analyze types of sorting algorithms
* Search algorithms along with hashing
* Understand linear and tree-based indexing
* Be able to implement a graph including topological sort, shortest path problem, and Prim’s algorithm
* Understand dynamic programming (Knapsack) and randomized algorithms
In this book, we cover not only classical data structures, but also functional data structures.
We begin by answering the fundamental question: why data structures? We then move on to cover the relationship between data structures and algorithms, followed by an analysis and evaluation of algorithms. We introduce the fundamentals of data structures, such as lists, stacks, queues, and dictionaries, using real-world examples. We also cover topics such as indexing, sorting, and searching in depth.
Later on, you will be exposed to advanced topics such as graph data structures, dynamic programming, and randomized algorithms. You will come to appreciate the intricacies of high performance and scalable programming using R. We also cover special R data structures such as vectors, data frames, and atomic vectors.
With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. We will also explore the application of binary search and will go in depth into sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort.
STYLE AND APPROACH
This easy-to-read book with its fast-paced nature will improve the productivity of an R programmer and improve the performance of R applications. It is packed with real-world examples. « less