Books: 46

Analysis

CoverTitleYear
Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary more » manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks. Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics. Key features: * Provides a complete discussion of both the hardware and software used to organize big data * Describes a wide range of useful applications for managing big data and resultant data sets * Maintains a firm focus on massive data and large networks * Unveils innovative techniques to help readers handle big data Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT – The Health and Life Sciences University, Austria, and the Universität der Bundeswehr München. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory. Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine. Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universität München. His research interests are in operations research, systems biology, graph theory and discrete optimization. Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology. « less
2016
Up-to-Date Guidance from One of the Foremost Members of the R Core Team Written by John M. Chambers, the leading developer of the original S software, Extending R covers key concepts and techniques in R to support analysis and research projects. It presents the core ideas of R, provides programming more » guidance for projects of all scales, and introduces new, valuable techniques that extend R. The book first describes the fundamental characteristics and background of R, giving readers a foundation for the remainder of the text. It next discusses topics relevant to programming with R, including the apparatus that supports extensions. The book then extends R’s data structures through object-oriented programming, which is the key technique for coping with complexity. The book also incorporates a new structure for interfaces applicable to a variety of languages. A reflection of what R is today, this guide explains how to design and organize extensions to R by correctly using objects, functions, and interfaces. It enables current and future users to add their own contributions and packages to R. « less
2016
An Object Oriented Approach Using C++
Use powerful C# algorithms and Object Oriented Programming (OOP) to aid in hedge fund decision making Hedge fund managers cannot afford to ignore their risk/return profiles, and taking advantage of new technologies is an excellent way to minimize risk and capitalize on various investment styles. As Hedge more » Fund Analysis and Modeling Using C# demonstrates, the C# programming language is perfectly suited to hedge fund analysis. This book serves as a complete course in hedge fund modeling and provides a primer on C# and Object Oritented Programming (OOP) that will allow you to manage risk easily and make the most of key statistics. Covering both basic and risk-adjusted performance measures, Hedge Fund Analysis and Modeling Using C# moves from simple to sophisticated analysis techniques, using worked examples to show you exactly how to manage return in an era of volatility and financial risk. You'll have access to: * Complete guidance on using C# and Objected Oriented Programming (OOP) for analysis using non-normal returns data and other key statistics * Bonus content on a companion website containing C# programs, algorithms, and data available for download * Real world modeling exercises that demonstrate the identification of risk and return factors * Complete guidance for optimizing hedge fund decisions using quantitative strategies This is the only book on the market that guides you through using C# to model hedge fund risks and returns. Along with its companion titles on Excel/VBA analysis and MATLAB analysis, Hedge Fund Analysis and Modeling Using C# contributes important guidance for hedge fund managers who want to take advantage of technological platforms for optimal fund performance. « less
2016
Project success through realistic requirements
* Learn how to create good requirements when designing hardware and software systems. While this book emphasizes writing traditional “shall” statements, it also provides guidance on use case design and creating user stories in support of agile methodologies. The book surveys modeling techniques and various more » tools that support requirements collection and analysis. You’ll learn to manage requirements, including discussions of document types and digital approaches using spreadsheets, generic databases, and dedicated requirements tools. Good, clear examples are presented, many related to real-world work the author has done during his career. Requirements Writing for System Engineeringantages of different requirements approaches and implement them correctly as your needs evolve. Unlike most requirements books,Requirements Writing for System Engineering teaches writing both hardware and software requirements because many projects include both areas. To exemplify this approach, two example projects are developed throughout the book, one focusing on hardware and the other on software. This book * Presents many techniques for capturing requirements. * Demonstrates gap analysis to find missing requirements. * Shows how to address both software and hardware, as most projects involve both. * Provides extensive examples of “shall” statements, user stories, and use cases. * Explains how to supplement or replace traditional requirement statements with user stories and use cases that work well in agile development environments What You Will Learn * Understand the 14 techniques for capturing all requirements. * Address software and hardware needs; because most projects involve both. * Ensure all statements meet the 16 attributes of a good requirement. * Differentiate the 19 different functional types of requirement, and the 31 non-functional types. * Write requirements properly based on extensive examples of good ‘shall’ statements, user stories, and use cases. * Employ modeling techniques to mitigate the imprecision of words. Audience Writing Requirements teaches you to write requirements the correct way. It is targeted at the requirements engineer who wants to improve and master his craft. This is also an excellent book from which to teach requirements engineering at the university level. Government organizations at all levels, from Federal to local levels, can use this book to ensure they begin all development projects correctly. As well, contractor companies supporting government development are also excellent audiences for this book. « less
2016
Explained Using R
Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating more » model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R. « less
2015
An Introduction with Statistical Applications
Thoroughly updated, Probability: An Introduction with Statistical Applications, Second Edition features a comprehensive exploration of statistical data analysis as an application of probability. The new edition provides an introduction to statistics with accessible coverage of reliability, acceptance more » sampling, confidence intervals, hypothesis testing, and simple linear regression. Encouraging readers to develop a deeper intuitive understanding of probability, the author presents illustrative geometrical presentations and arguments without the need for rigorous mathematical proofs. « less
2014
Exploratory Data Analysis
If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case more » study throughout this thoroughly revised book, you'll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You'll explore distributions, rules of probability, visualization, and many other tools and concepts. « less
2014
Practical Examples
IBM Cognos 10 is the next generation of the leading performance management, analysis, and reporting standard for mid- to large-sized companies. One of the most exciting and useful aspects of IBM Cognos software is its powerful custom report creation capabilities. After learning the basics, report authors more » in the enterprise need to apply the technology to reports in their actual, complex work environment. This book provides that advanced know how. Using practical examples based on years of teaching experiences as IBM Cognos instructors, the authors provide you with examples of typical advanced reporting designs and complex queries in reports. The reporting solutions in this book can be directly used in a variety of real-world scenarios to provide answers to your business problems today. The complexity of the queries and the application of design principles go well beyond basic course content or introductory books. IBM Cognos 10 Report Studio: Practical Examples will help you find the answers to specific questions based on your data and your business model. It will use a combination tutorial and cookbook approach to show real-world IBM Cognos 10 Report Studio solutions. If you are still using IBM Cognos 8 BI Report Studio, many of the examples have been tested against this platform as well. The final chapter has been dedicated to showing those features that are unique to the latest version of this powerful reporting solution. « less
2011
A Tutorial with R and BUGS
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students more » or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. -Accessible, including the basics of essential concepts of probability and random sampling -Examples with R programming language and BUGS software -Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). -Coverage of experiment planning -R and BUGS computer programming code on website -Exercises have explicit purposes and guidelines for accomplishment « less
2010
250+ READY-TO-USE, POWERFUL DMX QUERIES Transform data mining model information into actionable business intelligence using the Data Mining Extensions (DMX) language. Practical DMX Queries for Microsoft SQL Server Analysis Services 2008 contains more than 250 downloadable DMX queries you can use to more » extract and visualize data. The application, syntax, and results of each query are described in detail. The book emphasizes DMX for use in SSMS against SSAS, but the queries also apply to SSRS, SSIS, DMX in SQL, WinForms, WebForms, and many other applications. Techniques for generating DMX syntax from graphical tools are also demonstrated in this valuable resource. * View cases within data mining structures and models using DMX Case queries * Examine the content of a data mining model with DMX Content queries * Perform DMX Prediction queries based on the Decision Trees algorithm and the Time Series algorithm * Run Prediction and Cluster queries based on the Clustering algorithm * Execute Prediction queries with Association and Sequence Clustering algorithms * Use DMX DDL queries to create, alter, drop, back up, and restore data mining objects * Display various parameters for each algorithm with Schema queries * Examine the values of discrete, discretized, and continuous structure columns using Column queries * Use graphical interfaces to generate Prediction, Content, Cluster, and DDL queries * Deliver DMX query results to end users Download the source code from www.mhprofessional.com/computingdownload « less
2010