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Predict the future! This practical guide will help you use Big Data and technology to discover real-world insights, define projects, and help you create goals.

Digitale communicatie --- Management --- Data mining. --- Data processing. --- Mathematical models.

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Applied Spatial Data Analysis with R is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003. Roger Bivand is Professor of Geography in the Department of Economics at Norges Handelshøyskole, Bergen, Norway. Edzer Pebesma is Professor of Geoinformatics at Westfälische Wilhelms-Universität, Münster, Germany. Virgilio Gómez-Rubio is Research Associate in the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom.

Spatial analysis (Statistics) --- R (Computer program language) --- Data processing --- R (Computer program language). --- Data processing. --- Mathematical statistics --- Analysis, Spatial (Statistics) --- Correlation (Statistics) --- Spatial systems --- GNU-S (Computer program language) --- Domain-specific programming languages --- Spatial analysis (Statistics) - Data processing

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Stata is the most flexible and extensible data analysis package available from a commercial vendor. R is a similarly flexible free and open source package for data analysis, with over 3,000 add-on packages available. This book shows you how to extend the power of Stata through the use of R. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches. When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. A glossary defines over 50 R terms using Stata jargon and again using more formal R terminology. The table of contents and index allow you to find equivalent R functions by looking up Stata commands and vice versa. The example programs and practice datasets for both R and Stata are available for download. Robert A. Muenchen is the author of the book, R for SAS and SPSS Users, and is a consulting statistician with 29 years of experience. He has served on the advisory boards of SAS Institute, SPSS Inc., and the Statistical Graphics Corporation. He currently manages Research Computing Support at The University of Tennessee. Joseph M. Hilbe is Solar System Ambassador with NASA/Jet Propulsion Laboratory, California Institute of Technology, an adjunct professor of statistics at Arizona State, and emeritus professor at the University of Hawaii. He is a Fellow of the American Statistical Association and elected member of the International Statistical Institute. Hilbe was the first editor of the Stata Technical Bulletin, (later named the Stata Journal) and is author of a number of textbooks, including Logistic Regression Models and Negative Binomial Regression.

Statistics. --- Statistics and Computing/Statistics Programs. --- Mathematical statistics. --- Statistique --- Statistique mathématique --- R (Computer program language) --- Statistics --- Data processing --- Stata --- R (Computer program language). --- Stata. --- Statistics --Data processing. --- Mathematics --- Mathematical Statistics --- Physical Sciences & Mathematics --- Data processing. --- GNU-S (Computer program language) --- Domain-specific programming languages --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Statistics - Data processing

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The biological sciences are now in the midst of a true life sciences revolution akin to what physics experienced just after the turn of the last century. We are now in a phase of unparalleled growth that is reflected by the amount of data generated from each experiment. At the time of this writing, the rate of data acquisition was approaching 2 terabytes over the course of 5 days with first pass analysis proceeding over the following 2-3 week period. This fundamental shift has provided unprecedented opportunities that for the first time afford us the ability, i.e., means, breadth, and depth of data, to truly address human biology at the systems level. This wealth of information from seemingly disparate datasets and its integration is being realized through bioinformatics. It is with this philosophy that the text Bioinformatics for Systems Biology was born. This revolution has spawned true personalized medicine that encompasses diagnostics and treatment through to cure. For the physical and computer scientist, this text provides an introduction to the basic biological principles governing a cell. This quickly moves from the fundamentals to exploring the underlying genetic processes. While providing a rudimentary and necessary overview for the life scientist, the physical and computer scientist will be apprised of various nuances within the field reflecting the reality of "wet-bench" science. For those in the life sciences, it I rapidly becoming appreciated that we now progressing from examining our favorite "pet" gene to the system. Statistics is now an essential component to understand the vast datasets and this is emphasized throughout the text. The majority of the text is devoted to the common ground that these groups share. It provides rich examples of tools, databases, and strategies to mine the databases to reveal novel insights. A host of examples of parsing the data into a series of overlays that use various presentation systems are reviewed. The goal is to provide a representation most comfortable to the user to enable the user to thoroughly explore the data. The text concludes with examples of how the systems information is used to inform personalized medicine in a true "bench to bedside" manner. Bioinformatics for Systems Biology bridges and unifies many disciplines. It presents the life scientist, computational biologist, and mathematician with a common framework. Only by linking the groups together may the true life sciences revolution move forward in the mostly uncharted and emerging field of Systems Biology.

Bioinformatics --- Systems biology --- Computational biology --- Cytology --- Data processing. --- Bioinformatique --- Cytologie --- Traitement des données

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Programming Languages: An Active Learning Approach introduces students to three programming paradigms: object-oriented/imperative languages using C++ and Ruby, functional languages using Standard ML, and logic programming using Prolog. This interactive textbook is intended to be used in and outside of class. Each chapter follows a pattern of presenting a topic followed by a practice exercise or exercises that encourage students to try what they have just read. This textbook is best-suited for students with a 2-3 course introduction to imperative programming. Key Features: Accessible structure guides the student through various programming languages Seamlessly integrated practice exercises Classroom-tested Online support materials Advance Praise for Lee’s Programming Languages: An Active Learning Approach "The Programming Languages book market is overflowing with books, but none like this. In many ways, it is precisely the book I have been searching for to use in my own programming languages course. One of the main challenges I perpetually face is how to teach students to program in functional and logical languages, but also how to teach them about compilers. This book melds the two approaches very well." -- David Musicant, Carleton College .

Computer Science. --- Symbolic and Algebraic Manipulation. --- Logics and Meanings of Programs. --- Programming Languages, Compilers, Interpreters. --- Computer science. --- Logic design. --- Algebra --- Informatique --- Structure logique --- Algèbre --- Data processing. --- IBM 1620 (Computer) -- Programming. --- Programming (Electronic computers). --- Programming languages (Electronic computers). --- Engineering & Applied Sciences --- Computer Science --- Programming languages (Electronic computers) --- Electronic data processing. --- ADP (Data processing) --- Automatic data processing --- Data processing --- EDP (Data processing) --- IDP (Data processing) --- Integrated data processing --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Computer logic. --- Computer science --- Mathematics. --- Computers --- Office practice --- Electronic data processing --- Languages, Artificial --- Automation --- Design, Logic --- Design of logic systems --- Digital electronics --- Electronic circuit design --- Logic circuits --- Machine theory --- Switching theory --- Informatics --- Science --- Computer science—Mathematics. --- Computer science logic --- Logic, Symbolic and mathematical