The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics (2nd Edition) Review

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The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics (2nd Edition) ReviewThis book is poorly titled, because it discusses the algorithms and techniques of visualization much more than the use of the Visualization Toolkit software. However, it is a good introduction to scientific visualization, and the free software included on the CD is an extremely powerful and useful tool. However, if you only wish to learn to use the software, The Visualization Toolkit User's Guide would be much more appropriate for you.The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics (2nd Edition) Overview95469-3 All you need to build state-of-the-art 3D visualization applications! From weather maps to MRIs, from marketing to finance, 3D visualization technology is revolutionizing the way information is delivered. And the revolution has just begun. Breakthrough tools like the Visualization Toolkit (vtk) Release 2.0 make visualization more practical and powerful than ever. This book delivers all you need to start building your own visualization applications: A complete introduction to visualization architectures and image processing algorithms-including essential algorithms for contouring, surface smoothing, cutting, slicing, decimation, and Delaunay triangulation:*An extensive collection of real-world case studies. *A working architecture and software design techniques for applying visualization to business and technical problems. *The complete vtk Release 2.0 software tools-complete C++ class libraries and source code for visualization, plus Tcl-based prototyping libraries and Java bindings...all the software you need for Win95/NT, Solaris, HP-UX, Digital UNIX, and IBM AIX!Learn how to leverage the power of vtk 2.0 to:*Build Web-based 3D visualization applications that take advantage of VRML, Java and Java3D.*Use volume rendering, which allows you to "see into" 3D datasets. *Use vtk's new 2D imaging pipeline to manipulate, display and combine 2D images into 3D datasets for further processing-and much more.Advanced 3D visualization is now practical in an unprecedented range of applications. If you're a C++, Java or Tcl/Tk programmer, you need to understand these tools and technologies. Now's the time - and there's no better place to start than The Visualization Toolkit.

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Corporate Financial Analysis with Microsoft Excel (McGraw-Hill Finance & Investing) Review

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Corporate Financial Analysis with Microsoft Excel (McGraw-Hill Finance & Investing) ReviewThe book is a step by step guide to creating models. If you want to go from 0 to 60 or beyond, this book can take you there and you wont hit any speed bumps. I recommend reading it in a linear fashion, and therefore you should create the models as you read. That is how the book should be read.
Kindle software modifies the ebook version of this book and does not format it correctly. Therefore, if you are interested in the ebook version, I would try a non-kindle version and import that version to a kindle. If you by the kindle version you will find that you can't enlarge the graphs and pictures, which you need to view in order to complete the models. This is a problem with Kindle ebook version and not the book.Corporate Financial Analysis with Microsoft Excel (McGraw-Hill Finance & Investing) Overview
Corporate Financial Analysis with Microsoft®Excel® visualizes spreadsheets as an effectivemanagement tool both for financial analysisand for coordinating its results and actionswith marketing, sales, production and serviceoperations, quality control, and other businessfunctions.

Taking an integrative view that promotesteamwork across corporate functions andresponsibilities, the book contains dozens ofcharts, diagrams, and actual Excel® screenshotsto reinforce the practical applications ofevery topic it covers. The first two sections—Financial Statements and Cash Budgeting—explain how to use spreadsheets for:
Preparing income statements,balance sheets, and cashflow statements
Performing vertical andhorizontal analyses offinancial statements
Determining financial ratiosand analyzing their trendsand significance
Combining quantitative andjudgmental techniques to improveforecasts of sales revenues andcustomer demands
Calculating and applying thetime value of money
Managing inventories,safety stocks, and theallocation of resources

The third and final section—Capital Budgeting—covers capital structure, the cost ofcapital, and leverage; the basics of capitalbudgeting, including taxes and depreciation;applications, such as new facilities, equipmentreplacement, process improvement, leasingversus buying, and nonresidential real estate;and risk analysis of capital budgets and thepotential impacts of unforeseen events.

Corporate Financial Analysis with Microsoft®Excel® takes a broad view of financial functionsand responsibilities in relation to thoseof other functional parts of modern corporations,and it demonstrates how to use spreadsheetsto integrate and coordinate them. Itprovides many insightful examples and casestudies of real corporations, including Wal-Mart, Sun Microsystems, Nike, H. J. Heinz,Dell, Microsoft, Apple Computer, and IBM.

Corporate Financial Analysis with Microsoft®Excel® is the ideal tool for managing yourfirm's short-term operations and long-termcapital investments.


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S Programming Review

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S Programming Review
For those seeking an introduction to programming in S, or seeking information on how to use S for statistical applications, this is NOT a good choice as a "first purchase", nor is it intended to be. Instead, choose these authors' other textbook, Modern Applied Statistics with S-Plus.
This book isn't primarily about using S, using S-PLUS (a commercial version of S), nor using R (an Open Source version of S), but rather, it's about showing how to write programming extensions to the base S functions. In fact, for those seeking such guidance, this is the book's great virtue.
It's written by two world-class authorities on the subject of S programming, persons who are generous in their efforts to help others and who can be contacted through the Internet. However, this particular text assumes the reader is already committed to using S, and hence, contains little 'motivational' material. Yet for the audience to whom it's addressed, it's essential, or at least highly recommended, reading.
The latest commercial version of S (S-PLUS 2000 from Mathsoft, Inc.) has a graphical user interface (GUI). A chapter is included in the latter portion of the book on how to program such interfaces.
Also, a chapter is devoted to extending S with compiled code written in C, Fortran, or C++. Since S is an interpreted language, using compiled code can increase the speed of newly created functions. This is highly technical, however, and not for the neophyte.

Potential software developers of vertical-market applications involving S-PLUS, among others, will benefit from purchasing "S Programming" by Venables & Ripley.S Programming OverviewS is a high-level language for manipulating, analysing and displayingdata. It forms the basis of two highly acclaimed and widely used dataanalysis software systems, the commercial S-PLUS and the OpenSource R. This book provides an in-depth guide to writing software inthe S language under either or both of those systems. It is intendedfor readers who have some acquaintance with the S language and want toknow how to use it more effectively, for example to build re-usabletools for streamlining routine data analysis or to implement newstatistical methods.One of the outstanding strengths of the S language is the ease withwhich it can be extended by users. S is a functional language, andfunctions written by users are first-class objects treated in the sameway as functions provided by the system. S code is eminently readableand so a good way to document precisely what algorithms were used, andas much of the implementations are themselves written in S, they can bestudied as models and to understand their subtleties. The currentimplementations also provide easy ways for S functions to callcompiled code written in C, Fortran and similar languages; this isdocumented here in depth.Increasingly S is being used for statistical or graphical analysiswithin larger software systems or for whole vertical-marketapplications. The interface facilities are most developed onWindows and these are covered with worked examples.The authors have written the widely used Modern Applied Statisticswith S-PLUS, now in its third edition, and several software librariesthat enhance S-PLUS and R; these and the examples used in both booksare available on the Internet.Dr. W.N. Venables is a senior Statistician with the CSIRO/CMISEnvironmetrics Project in Australia, having been at the Department ofStatistics, University of Adelaide for many years previously.Professor B.D. Ripley holds the Chair of Applied Statistics at theUniversity of Oxford, and is the author of four other books on spatialstatistics, simulation, pattern recognition and neural networks. Bothauthors are known and respected throughout the international S and Rcommunities, for their books, workshops, short courses, freelyavailable software and through their extensive contributions to theS-news and R mailing lists.

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Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies Review

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Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies ReviewQuite introductory material, good for beginners (students as well as teachers planning a course) and strong in showing the links between critical discourse analysis and corpus linguistics.Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies OverviewIntroducing Electronic Text Analysis is a practical and much needed introduction to corpora-bodies of linguistic data. Written specifically for students studying this topic for the first time, the book begins with a discussion of the underlying principles of electronic text analysis. It then examines how these corpora enhance our understanding of literary and non-literary works. In the first section the author introduces the concepts of concordance and lexical frequency, concepts whichare then applied to a range of areas of language study. Key areas examined are the use of on-line corpora to complement traditional stylistic analysis, and the ways in which methods such as concordance and frequency counts can reveal a particular ideology within a text. Presenting an accessible and thorough understanding of the underlying principles of electronic text analysis, the book contains abundant illustrative examples and a glossary with definitions of main concepts. Itwill also besupported by a companion website with links to on-line corpora so that students can apply their knowledge to further study. The accompanying website to this book can be found at http://www.routledge.com/textbooks/0415320216

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Handbook of Statistical Analysis and Data Mining Applications Review

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Handbook of Statistical Analysis and Data Mining Applications ReviewThe "Handbook of Statistical Analysis & Data Mining Applications" is the finest book I have seen on the subject. It is not only a beautifully crafted book, with numerous color graphs, chart, tables, and screen shots, but the statistical discussion is both clear and comprehensive.
The text does not use only one statistical data mining application to display examples, but provides a rather thorough training in the use of both SAS-Enterprise Miner and STATISTICA Data Miner. A section on SPSS Clementine is also provided, giving comparisons between the various packages. Also employed are STATISTICA's C&RT, CHAID, MARSpline, and other data mining and graphical analytic tools.
The text does not burden the typical data mining researcher with the internals of how the various tools work. It is therefore not steeped in equations. Some are to be found, of course, but the emphasis is on understanding the concepts involved and on how to apply these concepts to real data - which is provided to the reader in terms of data tutorials. Specialized datasets have been prepared by both authors and outside experts in various areas of inquiry ranging from entertainment, financial, engineering, clinical psychology, dentistry, demographics, medical informatics, meteorology, astronomy, and more. Each tutorial is associated with data stored on either the associated CD that comes with the book, or which can be downloaded from a companion web site. Worked out examples of how to use data mining techniques on such data is provided to help the reader gain a solid feel for the data mining enterprise. The final third of the book is devoted to a partial selection of the available tutorials. The two earlier chapters demonstrate how to use data mining software for the analysis of data.
I highly recommend this work to anyone having an interest in data mining. I might also add that the Amazon price of $72.37 is truly excellent for an 864 page academic text, having full color tables and screen shots on some one-third of the pages, plus a CD. A bargain indeed.
Handbook of Statistical Analysis and Data Mining Applications OverviewThe Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.

Written "By Practitioners for Practitioners"
Non-technical explanations build understanding without jargon and equations
Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models using Statistica, SAS and SPSS software
Practical advice from successful real-world implementations
Includes extensive case studies, examples, MS PowerPoint slides and datasets
CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book


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R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) Review

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R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) ReviewThis is a strange little book in that it seems somewhat directed toward statisticians who want to develop R packages. The OOP section takes up 50 pages and discusses "S3 and S4" implementations of OOP in R in great detail, all of which is not doubt important for those few dozen accomplished statisticians who wish to write packages. However, by the time you are ready to actually write an R function that other people will use I can't imagine you wouldn't already be familiar with some of the basic commands discussed elsewhere in this book. So I am wondering who the intended audience is.
I think the majority of R users (biologists and programmers) want to run through some common statistical routines in a procedural fashion and produce reports that perform some analysis and show some graphs. The difficulty with R is learning how to massage data into a form that an existing statistical function will accept. That will invariably involve helper R-specific helper functions that do not exist in programming languages (e.g. unsplit) or that require a precise understanding of input (e.g. xtabs), and statistical routines that almost never return meaningful errors (glm). Manipulating data structures in R is not particularly intuitive (e.g. as.numeric(levels(f))[f]), so tons of examples are a must. However this book simply does not include enough R code - probably fewer than 250 lines.
In some instances commands are discussed at length in the space it would take to simply show the command. For example, a beginner would want to know how to save a data frame. Instead of providing a useful example like:
save(myDataFrame,file="myDataFrame.frame.RData",compress=TRUE)
there is a bizarre paragraph called "Working with R's binary format", in which save and load are discussed in theory as if they are planned for a distant release.
There is no chapter on using Sweave to develop pdf reports despite the book being actually written in Sweave. The author is more focused on "vignettes" which appear to be for documentation akin to POD files.
This book does include excellent sections on string manipulation, connecting to databases, and C integration. I learned some things about some neat Bioconductor functions available but a dedicated chapter would be nice.
At no point do you ever sense the author does not know what he is talking about - he just doesn't know who he is talking to. I hope in the future "R Programming For Bioinformatics" is split this into two more comprehensive books: "Developing R Packages" and "R for Biologists"R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) OverviewDue to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems.Drawing on the author's first-hand experiences as an expert in R, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code.With numerous examples and exercises, this practical guide focuses on developing R programming skills in order to tackle problems encountered in bioinformatics and computational biology.

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From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) Review

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From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) ReviewIt is easy to read with lots of examples. Gives planty of ideas if you are interested in corpus study.From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) OverviewFrom Corpus to Classroom summarises and makes accessible recent work in corpus research, focusing particularly on spoken data. It is based on analysis of corpora such as CANCODE and Cambridge International Corpus, and written with particular reference to the development of corpus-informed pedagogy.The book explains how corpora can be designed and used, and focuses on what they tell us about language teaching. It examines the relevance of corpora to materials writers, course designers and language teachers and considers the needs of the learner in relation to authentic data. It shows how the answers to key questions such as 'Is there a basic, everyday vocabulary for English?', 'How should idioms be taught?' and 'What are the most common spoken language chunks?' are best explored by means of a clearer understanding of the workings of language in context.

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