A Practical Introduction to Index Numbers
A Practical Introduction to Index Numbers

Author: Jeff Ralph

Publisher: John Wiley & Sons

Published: 2015-08-17

Total Pages: 233

ISBN-13: 1118977815

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This book provides an introduction to index numbers for statisticians, economists and numerate members of the public. It covers the essential basics, mixing theoretical aspects with practical techniques to give a balanced and accessible introduction to the subject. The concepts are illustrated by exploring the construction and use of the Consumer Prices Index which is arguably the most important of all official statistics in the UK. The book also considers current issues and developments in the field including the use of large-scale price transaction data. A Practical Introduction to Index Numbers will be the ideal accompaniment for students taking the index number components of the Royal Statistical Society Ordinary and Higher Certificate exams; it provides suggested routes through the book for students, and sets of exercises with solutions.

Introduction to Indexing and Abstracting
Introduction to Indexing and Abstracting

Author: Donald B. Cleveland

Publisher: Englewood, Colo. : Libraries Unlimited

Published: 1990

Total Pages: 352

ISBN-13:

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Based on new research and years of practical experience, this guide presents the basic knowledge necessary to become a professional indexer. Synthesizing the thinking and experience of indexers and abstractors over the years, the book introduces readers to such fundamentals as the nature of information, the organization of information, vocabulary control, types of indexes and abstracts, evaluation of indexing, and the use of computers. A new chapter on indexing and the Internet has been added, as has a chapter that lists Web resources for indexers and abstractors. The work concludes with a discussion of the education, training, and job opportunities of the profession, as well as a look to the future. With its simple but thorough approach, this book provides readers with a broad overview of the professions, processes, and art of indexing and abstracting.

Introduction to Information Retrieval
Introduction to Information Retrieval

Author: Christopher D. Manning

Publisher: Cambridge University Press

Published: 2008-07-07

Total Pages:

ISBN-13: 1139472100

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Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

Beyond Book Indexing
Beyond Book Indexing

Author: Diane Brenner

Publisher: Information Today, Inc.

Published: 2000

Total Pages: 172

ISBN-13: 9781573870818

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How to get started in web indexing, embedded indexing, and other computer-based media.

Indexing and Retrieval of Non-Text Information
Indexing and Retrieval of Non-Text Information

Author: Diane Rasmussen Neal

Publisher: Walter de Gruyter

Published: 2012-10-30

Total Pages: 440

ISBN-13: 3110260581

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The scope of this volume will encompass a collection of research papers related to indexing and retrieval of online non-text information. In recent years, the Internet has seen an exponential increase in the number of documents placed online that are not in textual format. These documents appear in a variety of contexts, such as user-generated content sharing websites, social networking websites etc. and formats, including photographs, videos, recorded music, data visualizations etc. The prevalence of these contexts and data formats presents a particularly challenging task to information indexing and retrieval research due to many difficulties, such as assigning suitable semantic metadata, processing and extracting non-textual content automatically, and designing retrieval systems that "speak in the native language" of non-text documents.

An Introduction to Statistical Learning
An Introduction to Statistical Learning

Author: Gareth James

Publisher: Springer Nature

Published: 2023-08-01

Total Pages: 617

ISBN-13: 3031387473

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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Atiyah-Singer Index Theorem - An Introduction
Atiyah-Singer Index Theorem - An Introduction

Author: Amiya Mukherjee

Publisher: Hindustan Book Agency

Published: 2013-10-30

Total Pages: 0

ISBN-13: 9789380250540

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Offers a thorough introduction to the Atiyah-Singer index theorem for elliptic operators on compact manifolds without boundary. The main theme is the classical index theorem and some of its applications, but not the subsequent developments and simplifications of the theory. The book is designed for a complete proof of the K-theoretic index theorem and its representation in terms of cohomological characteristic classes.

Introduction to Data Science
Introduction to Data Science

Author: Rafael A. Irizarry

Publisher: CRC Press

Published: 2019-11-20

Total Pages: 836

ISBN-13: 1000708039

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Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.