Extreme Dax
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Extreme DAX
Author: Michiel Rozema
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-01-20
Discover the true power of DAX and build advanced DAX solutions for practical business scenarios Key FeaturesSolve complex business problems within Microsoft BI tools including Power BI, SQL Server, and ExcelDevelop a conceptual understanding of critical business data modeling principlesLearn the subtleties of Power BI data visualizations, evaluation context, context transition, and filteringBook Description This book helps business analysts generate powerful and sophisticated analyses from their data using DAX and get the most out of Microsoft Business Intelligence tools. Extreme DAX will first teach you the principles of business intelligence, good model design, and how DAX fits into it all. Then, you'll launch into detailed examples of DAX in real-world business scenarios such as inventory calculations, forecasting, intercompany business, and data security. At each step, senior DAX experts will walk you through the subtleties involved in working with Power BI models and common mistakes to look out for as you build advanced data aggregations. You'll deepen your understanding of DAX functions, filters, and measures, and how and when they can be used to derive effective insights. You'll also be provided with PBIX files for each chapter, so that you can follow along and explore in your own time. What you will learnUnderstand data modeling concepts and structures before you start working with DAXGrasp how relationships in Power BI models are different from those in RDBMSesSecure aggregation levels, attributes, and hierarchies using PATH functions and row-level securityGet to grips with the crucial concept of contextApply advanced context and filtering functions including TREATAS, GENERATE, and SUMMARIZEExplore dynamically changing visualizations with helper tables and dynamic labels and axesWork with week-based calendars and understand standard time-intelligenceEvaluate investments intelligently with the XNPV and XIRR financial DAX functionsWho this book is for Extreme DAX is written for analysts with a working knowledge of DAX in Power BI or other Microsoft analytics tools. It will help you upgrade your knowledge and work with analytical models more effectively, so you'll need practical experience with DAX before you can get started.
Extreme Value Theory for Time Series
This book deals with extreme value theory for univariate and multivariate time series models characterized by power-law tails. These include the classical ARMA models with heavy-tailed noise and financial econometrics models such as the GARCH and stochastic volatility models. Rigorous descriptions of power-law tails are provided through the concept of regular variation. Several chapters are devoted to the exploration of regularly varying structures. The remaining chapters focus on the impact of heavy tails on time series, including the study of extremal cluster phenomena through point process techniques. A major part of the book investigates how extremal dependence alters the limit structure of sample means, maxima, order statistics, sample autocorrelations. This text illuminates the theory through hundreds of examples and as many graphs showcasing its applications to real-life financial and simulated data. The book can serve as a text for PhD and Master courses on applied probability, extreme value theory, and time series analysis. It is a unique reference source for the heavy-tail modeler. Its reference quality is enhanced by an exhaustive bibliography, annotated by notes and comments making the book broadly and easily accessible.
Advanced Data Mining and Applications
Author: Xue Li
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-07-26
Here are the proceedings of the 2nd International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China, August 2006. The book presents 41 revised full papers and 74 revised short papers together with 4 invited papers. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, and more.