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Enterprise Risk Management

From Incentives to Controls

A fully revised second edition focused on the best practices of enterprise risk management Since the first edition of Enterprise Risk Management: From Incentives to Controls was published a decade ago, much has changed in the worlds of business and finance. That's why James Lam has returned with a new edition of this essential guide. Written to reflect today's dynamic market conditions, the Second Edition of Enterprise Risk Management: From Incentives to Controls clearly puts this discipline in perspective. Engaging and informative, it skillfully examines both the art as well as the science of effective enterprise risk management practices. Along the way, it addresses the key concepts, processes, and tools underlying risk management, and lays out clear strategies to manage what is often a highly complex issue. Offers in-depth insights, practical advice, and real-world case studies that explore the various aspects of ERM Based on risk management expert James Lam's thirty years of experience in this field Discusses how a company should strive for balance between risk and return Failure to properly manage risk continues to plague corporations around the world. Don't let it hurt your organization. Pick up the Second Edition of Enterprise Risk Management: From Incentives to Controls and learn how to meet the enterprise-wide risk management challenge head on, and succeed.

In this book, Mr. Lam explains how an over-reliance on quantitative risk measurement has directly contributed to some of the high-profile risk management failures of recent years.

Big Data For Dummies

Find the right big data solution for your business ororganization Big data management is one of the major challenges facingbusiness, industry, and not-for-profit organizations. Data setssuch as customer transactions for a mega-retailer, weather patternsmonitored by meteorologists, or social network activity can quicklyoutpace the capacity of traditional data management tools. If youneed to develop or manage big data solutions, you'll appreciate howthese four experts define, explain, and guide you through this newand often confusing concept. You'll learn what it is, why itmatters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importanceto businesses, not-for-profit organizations, government, and ITprofessionals Authors are experts in information management, big data, and avariety of solutions Explains big data in detail and discusses how to select andimplement a solution, security concerns to consider, data storageand presentation issues, analytics, and much more Provides essential information in a no-nonsense,easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helpsyou take charge of big data solutions for your organization.

Implement discover how to implement your big data solution with an eye to operationalizing and protecting your data What it means see the importance of big data to your organization and how it's used to solve problems Open the book and find ...

Data Mining for Business Analytics

Concepts, Techniques and Applications in Python

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

This is the sixth version of this successful text, and the first using Python.

Data Mining Techniques

For Marketing, Sales, and Customer Relationship Management

Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information.

Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information.

Discovering Knowledge in Data

An Introduction to Data Mining

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book

This book provides the tools needed to thrive in today’s big data world.

Data Mining for Business Analytics

Concepts, Techniques, and Applications with XLMiner

An applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on social network analysis and text mining A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.com Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. Praise for the Second Edition "…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."– Research Magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." – ComputingReviews.com "Excellent choice for business analysts...The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years.

The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization "…extremely well organized, clearly ...

Data Mining and Business Analytics with R

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: • A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools • Illustrations of how to use the outlined concepts in real-world situations • Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials • Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional ...

Statistik für die Praxis

Vom Problem zur Methode

Ein Statistik-Profi hat "zur Feder" gegriffen, damit Praktiker statistische Daten endlich nutzbringend anwenden können: damit sie ohne mühevolle Berechnungen und falsche Ergebnisse die richtigen Entscheidungen treffen. Statistik ist für die meisten ein notwendiges Übel. Vielen ist sie zu abstrakt, es fehlt der konkrete Bezug zur Praxis. An den Hoch- und Fachschulen wurde häufig nicht vermittelt, wie statistische Methoden später im Beruf eingesetzt werden können. Aus wertvollen Daten werden oft nur einfachste Kennzahlen wie der arithmetische Mittelwert oder der Median gebildet - die dann auch noch falsch interpretiert werden. Dabei ist es kein Hexenwerk, mit vergleichsweise geringem Aufwand wertvolle Schlüsse aus vorliegendem Datenmaterial zu ziehen. Thomas Elser führt mit Hilfe vieler lebendiger Beispiele in die statistische Denkweise ein. Praktiker lernen ohne große Mühe, wozu Statistik in der Praxis wirklich taugt. Der Autor zeigt, welches praktische Problem auf welche statistische Methode "passt". Entscheidungen erhalten so eine solide Grundlage. Auf der beiliegenden CD-ROM werden im Excel-Format Aufgaben mit Lösungen (Formeln, Rechenschemata, Grafiken) präsentiert. Außerdem enthält die CD-ROM Beispiele aller im Buch behandelten statistischen Methoden: Die vom Leser eingegebenen Daten werden sofort in statistische Kennzahlen und die entsprechenden Grafiken umgesetzt.

Thomas Elser führt mit Hilfe vieler lebendiger Beispiele in die statistische Denkweise ein. Praktiker lernen ohne große Mühe, wozu Statistik in der Praxis wirklich taugt.

The Handbook of Technical Analysis + Test Bank

The Practitioner's Comprehensive Guide to Technical Analysis

A self study exam preparatory guide for financial technical analysis certifications Written by the course director and owner of www.tradermasterclass.com, a leading source of live and online courses in trading, technical analysis, and money management, A Handbook of Technical Analysis: The Practitioner's Comprehensive Guide to Technical Analysis is the first financial technical analysis examination preparatory book in the market. It is appropriate for students taking IFTA CFTe Level I and II (US), STA Diploma (UK), Dip TA (Aus), and MTA CMT Level I, II, and III exams in financial technical analysis, as well as for students in undergraduate, graduate, or MBA courses. The book is also an excellent resource for serious traders and technical analysts, and includes a chapter dedicated to advanced money management techniques. This chapter helps complete a student's education and also provides indispensable knowledge for FOREX, bond, stock, futures, CFD, and option traders. Learn the definitions, concepts, application, integration, and execution of technical-based trading tools and approaches Integrate innovative techniques for pinpointing and handling market reversals Understand trading mechanisms and advanced money management techniques Examine the weaknesses of popular technical approaches and find more effective solutions The book allows readers to test their current knowledge and then check their learning with end-of-chapter test questions that span essays, multiple choice, and chart-based annotation exercises. This handbook is an essential resource for students, instructors, and practitioners in the field. Alongside the handbook, the author will also publish two full exam preparatory workbooks and a bonus online Q&A Test bank built around the most popular professional examinations in financial technical analysis.

This handbook is an essential resource for students, instructors, and practitioners in the field.

Bank Asset and Liability Management

Strategy, Trading, Analysis

Banks are a vital part of the global economy, and the essence of banking is asset-liability management (ALM). This book is a comprehensive treatment of an important financial market discipline. A reference text for all those involved in banking and the debt capital markets, it describes the techniques, products and art of ALM. Subjects covered include bank capital, money market trading, risk management, regulatory capital and yield curve analysis. Highlights of the book include detailed coverage of: Liquidity, gap and funding risk management Hedging using interest-rate derivatives and credit derivatives Impact of Basel II Securitisation and balance sheet management Structured finance products including asset-backed commercial paper, mortgage-backed securities, collateralised debt obligations and structured investment vehicles, and their role in ALM Treasury operations and group transfer pricing. Concepts and techniques are illustrated with case studies and worked examples. Written in accessible style, this book is essential reading for market practitioners, bank regulators, and graduate students in banking and finance. Companion website features online access to software on applications described in the book, including a yield curve model, cubic spline spreadsheet calculator and CDO waterfall model.

Concepts and techniques are illustrated with case studies and worked examples. Written in accessible style, this book is essential reading for market practitioners, bank regulators and graduate students in banking and finance.