THe Lttle SAS Book; A primer second edition
595768
This is a great book for people who already  have some knowledge of SAS, but do not use it really often.  The book is often short on example but is a great resource when you are looking for the exact coding.  This book is  more user freindly than the SAS manual and give you the code you need on a  regular basis, proc freq, reg, date format.....
One limitation of this  book was the lack of advance techniques. But if you need a small SAS  reference book next to your computer "the little sas book "  should be it.
3845638
A must read book from In-Memory Computing Guru with a deep dive on the past, present, and future of databases and big data technologies.  In todays world, its hard for an aspiring data scientist to choose the number of programming languages, statistical framework, business intelligence tools, in-memory computing databases, big data platforms, SAP HANA database compression technology with various techniques and examples of compression.  This book aids the aspiring data scientists, data analysts, and programmers to choose the technology to build their career.

 The book covers various Big data in-memory computing databases like

 SAP HANA with SAP BW and multiple SAP NetWeaver platforms
 IBM DB2 Blu
 Oracle Exadata

Big data technologies

 Apache Hadoop
 MapReduce
 NoSQL databases
 Google BigQuery

NoSQL databases

 Aerospike
 MemSQL
 Tokyo Cabinet
 Apache CouchDB
 Redis
 MongoDB
 Apache Cassandra

Business intelligence tools

 SAP BW, SAP BI
 Tableau
 QlikView
 Splunk

Statistical framework

 R statistical programming language

I highly recommend you to get this book today.
2030701
This is a good SAS introductory book because it contains both basic information for the absolute beginner (e.g., what is a variable?), as well as chapters explaining some of the most commonly used Procedures.  The latter include the following: SORT, PRINT, PLOT, FREQ, MEANS, CORR, Analysis of Variance (ANOVA), and Regression. Although the book warns that it does not teach statistics, it somewhat accomplishes this through explanations of the programming statements and the resulting output.  Of course, it is not a substitute for a stats text. Indeed, some of the more complicated stats are reviewed in a cursory manner that was confusing or had little or no explanation: "This pattern might be the result of autocorrelation-we could use the P option in GLM to get a Durbin-Watson statistic to check for this," without any explanation of autocorrelation.
For those of us who use datasets that are already set up, the emphasis on inputting cards in the beginning chapters is somewhat annoying.  However, this may be more appropriate for students assigned work with small sets of raw data.  It's not a very long book, but I recommend it for getting right to several of the most frequently used PROCS.  On the other hand, some of the explanations could have been expanded for clarity; it may have been better to cover fewer statistical procedures but to explain each one in a more user-friendly style.  Not including the somewhat superfluous Appendices ("Getting Started: The SAS System at Your Installation," and "More About the SAS System") the book is only 89 pages long.
4175613
This handy reference offers a very nice update of the book by Coia et al that was indispensable for many years.  The color images give the book a modern appearance.  The trial summaries are concise and informative.  The staging rules are useful at a glance.  This book will make a valuable addition to my radiation oncology textbook collection.
2530018
This book is not worthless, but it's not particularly full of information either.  I am probably not the best reviewer of SAS books because I am not very experienced with SAS.  Nevertheless, my impression of the book is that while it is well organized and easy to navigate, it does not have the content to do even slightly more sophisticated programming than the bare basics.
3112098
I'm still reading the book and didn't reach chapters on data and stats yet, but found it well organized, highly accessible and focused.

I opted for a kindle version not only because it's cheaper, but also because with computer books you may want to have the text on the screen next to the R console itself, where you can try and test everything. With only few exceptions, R does not seem to be something extraordinary in terms of complexity and novelty - the same mathematical concepts, same object-oriented philosophy, more or less similar syntax. To fully appreciate the beauties of the language I need only to play and practice as much as possible. Although R treats and classifies data in ways similar to SQL and other data management/analysis tools, it provides a new perspective on data perception and, certainly, more flexibility in analysis.

Another book that I found very useful is Muenchen's "R for SAS and SPSS Users", the sample of which can be found on author's blog.

A note of caution - you may not want to go back to SPSS and SAS once you master R.
776835
Often these predictive analytics books get bogged down in the weeds by exploring mathematical proofs rather than demonstrating potential applications.  While there are mild mathematical explanations within, this book mainly leverages multiple R-packages to produce a methodology for analyzing various data and for assessing applications of various regression and classification models available in R.  There are a number of examples utilizing quantitative and qualitative attributes with tips on how best to preprocess each followed by an explanation of how to apply certain models to particular problems within R.  Both qualitative and quantitative targets get substantial treatment in the book, as does the process of measuring and comparing model performance in each instance.

In addition, each chapter contains an extensive "how-to" section utilizing the R-language allowing the reader to execute the methods, models, and graphs found within the chapter.  If the reader's goal is to learn about options for preprocessing data and how to apply multiple regression and/or classification models in R, this is an invaluable resource.  I'm very pleased with the content in this book.
4825183
"Machine Learning with R-Second Edition" is an excellent introduction to building algorithms using R.  Bert Lantz, the author, provides hands-on case studies combined with essential theory to understand basic to advanced methods. I served as an Assistant Vice President for Carnegie Mellon University and a Director with Oracle and PeopleSoft; I am confident that "Machine Learning with R" will provide a strong framework for new students, but also an excellent guide for working professionals learning how to use R. I found that the example code worked perfectly and that I could quickly build the case study models.
My firm, Pacific Health Software-PHS, is building Open Source free Predictive Analytic (PA) software with PA models built upon Open Source clinical systems: OpenEMR; OpenMRS & VISTA. Initial free models (built in R) with links to OpenEMR clinical data-sets will be released in October 2015; http://phs-redmine.bitnamiapp.com/redmine/.We anticipate that models can bring higher than 90% to 98% prediction rates on the course to take with major diseases such as Heart Disease.
We plan to work with the US Peace Corps to assist clinical decision making in Peace Corp clinics in Africa and Asia that use OpenEMR clinical systems; we will be asking a community of volunteers to build and distribute additional free PA models. We will recommend that community volunteers, developers, who are new to using R, buy "Machine Learning with R": to learn to build new models with R.
3400062
great book
provides useful programming examples
current to current version of SAS
useful for applied researchers
great explanations of methods.
4081354
Robin Vowel's book compliments Hughe's book for a variety of algorithms and none-MVS  platforms (emphasizing certain PL/I facilities well).

NB.
Caution with inexpensive used copies.  They may not be the latest edtion.  Previous editions are more verbose.
843859
This is an excellent books which brings out the salient features of R as a programming language. Rather than focusing on different statistical libraries available with R, the focus is more on R as a programming language and features that make it shine for statistical applications.

Good things about this book
-----------------------------
 - The writing is very clear and succinct.
 - The concepts are illustrated very nicely with lot of examples. The central concepts of R like vectorization, recycling have been given excellent treatment.
 - The R data structures list,vector, matrix, data frame,array and factors have been covered very well with good amount of examples.
 - The book covers best practices in R. R way of doing things - apply category of functions instead of loops, ifelse etc are explained very well.
 - The sections on debugging and performance tuning of R are very good.

And few bad things
---------------------
The reason that I am not giving 5 stars is that -
 - Lack of practice exercises. I think the best way to learn a language is through practicing. Some hands on practice exercises at the end of each chapter would have been a great addition.
1465701
ISBN 1482204584 (A Handbook of Statistical Analyses using R, 3rd ed.) is an excellent compact reference on R not for beginners in programming or statistics even though it starts with the brief introductions to R and data analysis. They are followed by chapters on inference, variance, regression, density, partitioning, smoothers, longitudinal data, comparisons, meta-analysis, principal component, scaling, and clustering, as in&nbsp;<a data-hook="product-link-linked" class="a-link-normal" href="/the-2nd-ed/dp/1420079336/ref=cm_cr_arp_d_rvw_txt?ie=UTF8">the 2nd ed.</a>, and additionally on quantile regression and missing values not in the&nbsp;<a data-hook="product-link-linked" class="a-link-normal" href="/the-2nd-ed/dp/1420079336/ref=cm_cr_arp_d_rvw_txt?ie=UTF8">the 2nd ed.</a>.

The text full of useful examples well teaching R to solve problems of explained statistical techniques and involved math described individually in separate chapters.  The descriptions are systematic explanations in the tutorial style: introduction of data, R example to purify data, and R example to find the data significance.  The book is well printed on good paper, but the relatively flimsy soft covers are prone to creasing. It could be complemented by&nbsp;<a data-hook="product-link-linked" class="a-link-normal" href="/Introduction-to-Scientific-Programming-and-Simulation-Using-R/dp/1466569999/ref=cm_cr_arp_d_rvw_txt?ie=UTF8">Introduction to Scientific Programming and Simulation Using R</a>.
2641007
The book is a collection of invited papers, from a conference at the Ohio State University in 1980, devoted to the teaching of statistics and statistical consulting. The papers explore five areas: graduate programs in statistics; teaching service courses and short courses; training statisticians for employment in industry and government; the role of statistical consulting in graduate training; and teaching statistics at the Open University. A number of "Big Names" contribute. While some of the articles are now a bit dated, there are still some interesting things for the person interested in teaching or consulting.  Review contributed by Frederick Ruland (Author of: A Universal 'Pocket' Statistical Tool - Based on the Pearson Chi-Square Goodness of Fit Test.)
1336607
Outstanding guide to learn and apply SAS ODS skills to your everyday needs. I found this book to be really handy and easy to understand with excellent concepts and examples on ODS very cleary explained.
4772390
Robert M. Clark's "Intelligence Analysis: A Target-Centric Approach" is an up-to-date, practical manual on the conduct of analysis in the context of the current global war on terrorism.  This manual is well suited for classroom use for intelligence professionals, whether in the military, in civilian government agencies, or private industry.

Clark divides his topic into three principal sections.  In the first, he provides a detailed break-down of the target-centric approach as the collaborative, interactive, information network-enabled analysis that has replaced the hierarchial stovepipe architecture of the Cold War.

In the second section, on modeling, Clark explains in clear and understandable language the process by which analysts synthesize available information into a conceptualization of the intelligence problem.  This key step produces the basis to which analysts will apply predictive analysis.

The heart of the book is Clark's exploration of the techniques and potential pitfalls of predictive analysis.  Clark discusses a variety of methods to approach analysis, along with their practical limits and familar challenges such as bias and customer interaction.  His liberal use of examples from recent intelligence failures help make clear just what a challenging combination of art, science, and team effort good intelligence analysis should be.

This book is not without some faults.  His definitions of Strategic, Operational, and Tactical intelligence are imprecise and not those commonly in use in, for example, the Department of Defense.  Strategic intelligence is better defined by the level of the customer served and not by whether it is long range or short range.  Similarly, his breakdown of the standard intelligence disciplines achieves simplicity at the expense of considerable accuracy.  As an example, his explanation of TECHINT confuses the acquisition of foreign materials with their actual exploitation for intelligence value.  It should be noted in Clark's defense that the U.S. Intelligence Community lacks standardization, which fault contributes to the challenges of collaboration.

As of 2016, this book has been updated and reissued.  The value of a comprehensive approach to analysis has only been re-emphasized over the last few years in the Intelligence Community's approach to terrorism, illicit drug smuggling, cyber warfare, and other areas of intelligence interest.  It turns out that a great many problems can be successfully approached using intelligence analytical techniques.

This book is very highly recommended to intelligence professionals interested in a systematic and unclassified exploration of the techniques of good analysis.
1531744
I have worked with Teradata but in senior leadership roles and was familiar with some terms and concepts but not hands on with the system or tools.  This book helped to cover the basics and mapped well with what was on the first level exam.  One thing I would have liked more of in the book was the purpose and intent of each component.  This could add some valuable context and help with reference points for memory and practical application.
2515559
The new 2nd edition of this book has been completely updated for Windows Server 2003 Service Pack 1 and release R2, and information on administration using command-line utilities has been added to each chapter.

This book is an extremely clear, concise, yet comprehensive guide to the server-specific features of Windows Server 2003 -- particularly features that are new or enhanced compared with Windows Server 2000.  Coverage of topics like DNS and clustering, Active Directory and LDAP, Group Policy (with the new Group Policy Management Console) & the IntelliMirror application distribution system, volume shadow copy for automatic backup file versioning, offline files and folders (an improvement on the Windows briefcase), file indexing services, permission levels & security analysis with the new Security Configuration Wizard, IISv6 (greatly improved), the .NET framework, VPNs, Radius, & IPSec, is extremely good. The main focus is on deployment, administration & management tools.

This book seems far more useful than the MS Administrator's Pocket Consultant, and the only topics that seem to be omitted are mention of WSH (to go with the added information on command-line utilities), service recovery, and WINS for systems with pre-Windows 2000 legacy clients.  Windows SharePoint Services gets only brief mention. The appendix on features likely to be in Longhorn is a good bonus.

I would highly recommend this book.