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DESCRIPTION:

Master how to build dynamic HTML5-ready SVG charts using Python and the pygal library

About This Book:​

• A practical guide that helps you break into the world of data visualization with Python
• Understand the fundamentals of building charts in Python
• Packed with easy-to-understand tutorials for developers who are new to Python or charting in Python

Who This Book Is For:​

If you are a Python novice or an experienced developer and want to explore data visualization libraries, then this is the book for you. No prior charting or graphics experience is...

DESCRIPTION:

Are you new to SciPy and NumPy? Do you want to learn it quickly and easily through examples and a concise introduction? Then this is the book for you. You’ll cut through the complexity of online documentation and discover how easily you can get up to speed with these Python libraries.

Ideal for data analysts and scientists in any field, this overview shows you how to use NumPy for numerical processing, including array indexing, math operations, and loading and saving data. You’ll learn how SciPy helps you work with advanced mathematical functions such as optimization, interpolation, integration, clustering, statistics, and other...

DESCRIPTION:

This is a book about doing things with Django admin. It takes the form of about forty questions and common tasks with Django admin.

This book serves as a reference when you are trying to do more with Django Admin, and as a tutorial to expand your knowledge and skill with Django.

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Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.

Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this.

This book is based...

DESCRIPTION:

This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities.

The book is designed with the student in mind, making it suitable for self-study or...

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Graphics for Statistics and Data Analysis with R presents the basic principles of sound graphical design and applies these principles to engaging examples using the graphical functions available in R. It offers a wide array of graphical displays for the presentation of data, including modern tools for data visualization and representation.

The book considers graphical displays of a single discrete variable, a single continuous variable, and then two or more of each of these. It includes displays and the R code for producing the displays for the dot chart, bar chart, pictographs, stemplot, boxplot, and variations on the...

DESCRIPTION:

Master the programming skills you need to turn raw, unfiltered data into deep insights and get ready for a data science and analytics career with this definitive guide to R Programming for Beginners!​

Do you want to get started learning how to program, but don't know where to begin? Are you interested in moving beyond Excel sheets and learning one of the most powerful programming language used in cutting edge research such as machine learning?

If you answered yes to any of these questions, then this book might just be what you need.

R can be a royal pain in the neck sometimes. Even seasoned programmers and data analysts...

DESCRIPTION:

Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications.

The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis...

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See How Graphics Reveal Information

Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA.

Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what...

DESCRIPTION:

Data science has recently gained much attention for a number of reasons, and among them is Big Data. Scientists (from almost all disciplines including physics, chemistry, biology, sociology, among others) and engineers (from all fields including civil, environmental, chemical, mechanical, among others) are faced with challenges posed by data volume, variety, and velocity, or Big Data. This book is designed to highlight the unique characteristics of geospatial data, demonstrate the need to different approaches and techniques for obtaining new knowledge from raw geospatial data, and present select state-of-the-art geospatial data...

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Praise for the First Edition
"I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics."
—Statistics in Medical Research

"[This book] is written in a lucid conversational style, which is so rare in mathematical writings. It does an excellent job of presenting Bayesian statistics as a perfectly reasonable approach to elementary problems in statistics."
—STATS: The Magazine for Students of Statistics, American Statistical Association

"Bolstad offers clear explanations of every concept and method making the book accessible and valuable to...

DESCRIPTION:

Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical representations of probability, and Bayesian analysis is applied to parameter estimation using the MatLab and Python programs provided online. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to become familiar with the basic principles of...

DESCRIPTION:

This quirky, fast-paced excursion through the world of statistics brings basic statistical concepts down to earth for general readers by showing how statistics are applied in our everyday lives. Drawing on such diverse examples as how pills are manufactured, elections are forecast, and chess tournaments are structured, psychologist Joseph Tal familiarizes readers with variables, means, medians, scales of measurement, sampling, estimating, and other stock-in-trade tools of the statistician.

An unusually lively, informal review of statistical concepts Reading Between the Numbers:
• Features dozens of fascinating, often...

DESCRIPTION:

The R version of Andy Field′s hugely popular Discovering Statistics Using SPSS takes students on a journey of statistical discovery using the freeware R. Like its sister textbook, Discovering Statistics Using R is written in an irreverent style and follows the same ground breaking structure and pedagogical approach. The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

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DESCRIPTION:

Designed for an intermediate undergraduate course, Probability and Statistics with R shows students how to solve various statistical problems using both parametric and nonparametric techniques via the open source software R. It provides numerous real-world examples, carefully explained proofs, end-of-chapter problems, and illuminating graphs to facilitate hands-on learning.

Integrating theory with practice, the text briefly introduces the syntax, structures, and functions of the S language, before covering important graphically and numerically descriptive methods. The next several chapters elucidate probability and random variables...

DESCRIPTION:

R is a programming language and software environment for statistical analysis, graphics representation, and reporting. If you are trying to understand the R programming language as a beginner, this short book will give you enough understanding of almost all the concepts of the R language. The author will guide you through examples, how to program in R and how to use R for effective data analysis.Buy your copy Now!

Book Objectives​

This book is about R programming. The following are the objectives of the author:

• To familiarize you with the basics of R programming language.
• To help...

DESCRIPTION:

Applied Spatial Data Analysis with R, second edition, 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 and the handling of spatio-temporal data. 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...

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The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.

See What’s New in the Second Edition:

• Increased emphasis on more idiomatic R provides a grounding in the functionality of base R.
• Discussions of the use of RStudio helps new R users avoid as many...

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The cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability. While it is a valuable tool for students who are first learning statistics, proper introductory materials are needed for its adoption.

Using R for Introductory Statistics fills this gap in the literature, making the software accessible to the introductory student. The author presents a self-contained...

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[John Verzani] Using R for Introductory Statistics

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Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment.

From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary.

They begin with an introduction to R, discussing the syntax, general operators, and basic data manipulation while...

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[Robert Gentleman, Kurt Hornik, Giovanni Parmigiani] Use R!

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This tutorial manual provides a comprehensive introduction to R, a software package for statistical computing and graphics.

R supports a wide range of statistical techniques and is easily extensible via user-defined functions. One of R's strengths is the ease with which publication-quality plots can be produced in a wide variety of formats.

This is a printed edition of the tutorial documentation from the R distribution, with additional examples, notes and corrections. It is based on R version 2.9.0, released April 2009. R is free software, distributed under the terms of the GNU General Public License (GPL). It can be used with...

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Summary

R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines...

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[John Maindonald, John Braun] Data Analysis And Graphics Using R

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