Since 1994, Nancy Mulvany's Indexing Books has been the gold standard for thousands of professional indexers, editors, and authors. This long-awaited second edition, expanded and completely updated, will be equally revered. Like its predecessor, this edition of Indexing Books offers comprehensive, reliable treatment of indexing principles and practices relevant to authors and indexers alike. In addition to practical advice, the book presents a big-picture perspective on the nature and purpose of indexes and their role in published works. New to this edition are discussions of "information overload" and the role of the index, open-system versus closed-system indexing, electronic submission and display of indexes, and trends in software development, among other topics. Mulvany is equally comfortable focusing on the nuts and bolts of indexing—how to determine what is indexable, how to decide the depth of an index, and how to work with publisher instructions—and broadly surveying important sources of indexing guidelines such as The Chicago Manual of Style, Sun Microsystems, Oxford University Press, NISO TR03, and ISO 999. Authors will appreciate Mulvany's in-depth consideration of the costs and benefits of preparing one's own index versus hiring a professional, while professional indexers will value Mulvany's insights into computer-aided indexing. Helpful appendixes include resources for indexers, a worksheet for general index specifications, and a bibliography of sources to consult for further information on a range of topics. Indexing Books is both a practical guide and a manifesto about the vital role of the human-crafted index in the Information Age. As the standard indexing reference, it belongs on the shelves of everyone involved in writing and publishing nonfiction books.

This book is essential in understanding, investing and risk managing the holy grail of investments - structured products. The book begins by introducing structured products by way of a basic guide so that readers will be able to understand a payoff graphic, read a termsheet or assess a payoff formula, before moving on to the key asset classes and their peculiarities. Readers will then move on to the more advanced subjects such as structured products construction and behaviour during their lifetime. It also explains how to avoid important pitfalls in products across all asset classes, pitfalls that have led to huge losses over recent years, including detailed coverage of counterparty risk, the fall of Lehman Brothers and other key aspects of the financial crisis related to structured products. The second part of the book presents an original approach to implementing structured products in a portfolio. Key features include: A comprehensive list of factors an investor needs to take into consideration before investing. This makes it a great help to any buyer of structured products; Unbiased advice on product investments across several asset classes: equities, fixed income, foreign exchange and commodities; Guidance on how to implement structured products in a portfolio context; A comprehensive questionnaire that will help investors to define their own investment preferences, allowing for a greater precision when facing investment decisions; An original approach determining the typical distribution of returns for major product types, essential for product classification and optimal portfolio implementation purposes; Written in a fresh, clear and understandable style, with many figures illustrating the products and very little mathematics. This book will enable you to better comprehend the use of structured products in everyday banking, quickly analyzing a product, assessing which of your clients it suits, and recognizing its major pitfalls. You will be able to see the added value versus the cost of a product and if the payoff is compatible with the market expectations.

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results