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Access Data Analysis Cookbook

Access Data Analysis Cookbook

by Ken Bluttman
368 Pages · 2007 · 11.3 MB · 2,275 Downloads · New!
" You miss 100% of the shots you don’t take. ” ― Wayne Gretzky
Algorithms from and for Nature and Life
by Andrea Cerioli
547 Pages · 2013 · 8.6 MB · 1,134 Downloads · New!
This volume provides approaches and solutions to challenges occurring at the interface of research fields such as, e.g., data analysis, data mining and knowledge discovery, computer science, operations research, and statistics. In addition to theory-oriented contributions various application areas are included. Moreover, traditional classification research directions concerning network data, graphs, and social relationships as well as statistical musicology describe examples for current interest fields tackled by the authors. The book comprises a total of 55 selected papers presented at the Joint Conference of the German Classification Society (GfKl), the German Association for Pattern Recognition (DAGM), and the Symposium of the International Federation of Classification Societies (IFCS) in 2011.​
A Manager’s Guide to Data Warehousing
by Laura Reeves
480 Pages · 2009 · 2.7 MB · 3,892 Downloads · New!
Aimed at helping business and IT managers clearly communicate with each other, this helpful book addresses concerns straight-on and provides practical methods to building a collaborative data warehouse. You’ll get clear explanations of the goals and objectives of each stage of the data warehouse lifecycle while learning the roles that both business managers and technicians play at each stage. Discussions of the most critical decision points for success at each phase of the data warehouse lifecycle help you understand ways in which both business and IT management can make decisions that best meet unified objectives.
Analyzing the Analyzers
by Harlan Harris
35 Pages · 2013 · 2.3 MB · 1,428 Downloads · New!
There has been intense excitement in recent years around activities labeled “data science,” “big data,” and “analytics.” However, the lack of clarity around these terms and, particularly, around the skill sets and capabilities of their practitioners has led to inefficient communication between “data scientists” and the organizations requiring their services. This lack of clarity has frequently led to missed opportunities. To address this issue, we surveyed several hundred practitioners via the Web to explore the varieties of skills, experiences, and viewpoints in the emerging data science community.
A Practical Guide to Data Mining for Business and Industry
by Andrea Ahlemeyer-Stubbe
324 Pages · 2014 · 16.1 MB · 1,721 Downloads · New!
Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.
Big Data Analytics Using Splunk
by Peter Zadrozny
376 Pages · 2013 · 16.9 MB · 3,711 Downloads · New!
Big Data Analytics Using Splunk is a hands-on book showing how to process and derive business value from big data in real time. Examples in the book draw from social media sources such as Twitter (tweets) and Foursquare (check-ins). You also learn to draw from machine data, enabling you to analyze, say, web server log files and patterns of user access in real time, as the access is occurring. Gone are the days when you need be caught out by shifting public opinion or sudden changes in customer behavior. Splunk’s easy to use engine helps you recognize and react in real time, as events are occurring.
Big Visual Data Analysis
by C.-C. Jay Kuo
132 Pages · 2016 · 10.43 MB · 1,595 Downloads · New!
This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks.