Analyzing HTTP
Analyzing HTTP

Author: Jean Tunis

Publisher: Beyond Stop

Published: 2015-08-03

Total Pages: 58

ISBN-13: 1687341915

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Slow performing web applications can kill a business or organization! Whether you're the Owner providing them to your customers or the Performance Engineer responsible for managing them, you need to ensure that your web applications perform fast. This eBook will give you the tips, recommendations and guidelines needed to analyze your web applications for better performance. You will learn the things to look at to help save you time before, during or after your sites are slow. When your sites are performing slowly, time is of the essence! If not, you or your company could be losing customers and clients! What you'll learn from this book? - 10 questions you need to answer in order to analyze your web applications - 5 additional questions to ask when your sites are slow - 9 key performance metrics to look at during analysis - 5 common recommendations for better performance

Analyzing the Social Web
Analyzing the Social Web

Author: Jennifer Golbeck

Publisher: Newnes

Published: 2013-02-17

Total Pages: 291

ISBN-13: 0124058566

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Analyzing the Social Web provides a framework for the analysis of public data currently available and being generated by social networks and social media, like Facebook, Twitter, and Foursquare. Access and analysis of this public data about people and their connections to one another allows for new applications of traditional social network analysis techniques that let us identify things like who are the most important or influential people in a network, how things will spread through the network, and the nature of peoples' relationships. Analyzing the Social Web introduces you to these techniques, shows you their application to many different types of social media, and discusses how social media can be used as a tool for interacting with the online public. - Presents interactive social applications on the web, and the types of analysis that are currently conducted in the study of social media - Covers the basics of network structures for beginners, including measuring methods for describing nodes, edges, and parts of the network - Discusses the major categories of social media applications or phenomena and shows how the techniques presented can be applied to analyze and understand the underlying data - Provides an introduction to information visualization, particularly network visualization techniques, and methods for using them to identify interesting features in a network, generate hypotheses for analysis, and recognize patterns of behavior - Includes a supporting website with lecture slides, exercises, and downloadable social network data sets that can be used can be used to apply the techniques presented in the book

Bayesian Data Analysis, Third Edition
Bayesian Data Analysis, Third Edition

Author: Andrew Gelman

Publisher: CRC Press

Published: 2013-11-01

Total Pages: 677

ISBN-13: 1439840954

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Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

HTTP Developer's Handbook
HTTP Developer's Handbook

Author: Chris Shiflett

Publisher: Sams Publishing

Published: 2003

Total Pages: 306

ISBN-13: 9780672324543

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HTTP is the protocol that powers the Web. As Web applications become more sophisticated, and as emerging technologies continue to rely heavily on HTTP, understanding this protocol is becoming more and more essential for professional Web developers. By learning HTTP protocol, Web developers gain a deeper understanding of the Web's architecture and can create even better Web applications that are more reliable, faster, and more secure. The HTTP Developer's Handbook is written specifically for Web developers. It begins by introducing the protocol and explaining it in a straightforward manner. It then illustrates how to leverage this information to improve applications. Extensive information and examples are given covering a wide variety of issues, such as state and session management, caching, SSL, software architecture, and application security.

R for Data Science
R for Data Science

Author: Hadley Wickham

Publisher: "O'Reilly Media, Inc."

Published: 2016-12-12

Total Pages: 521

ISBN-13: 1491910364

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Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Analyzing meaning
Analyzing meaning

Author: Paul R. Kroeger

Publisher: Language Science Press

Published: 2019

Total Pages: 502

ISBN-13: 3961101361

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This book provides an introduction to the study of meaning in human language, from a linguistic perspective. It covers a fairly broad range of topics, including lexical semantics, compositional semantics, and pragmatics. The chapters are organized into six units: (1) Foundational concepts; (2) Word meanings; (3) Implicature (including indirect speech acts); (4) Compositional semantics; (5) Modals, conditionals, and causation; (6) Tense & aspect. Most of the chapters include exercises which can be used for class discussion and/or homework assignments, and each chapter contains references for additional reading on the topics covered. As the title indicates, this book is truly an INTRODUCTION: it provides a solid foundation which will prepare students to take more advanced and specialized courses in semantics and/or pragmatics. It is also intended as a reference for fieldworkers doing primary research on under-documented languages, to help them write grammatical descriptions that deal carefully and clearly with semantic issues. The approach adopted here is largely descriptive and non-formal (or, in some places, semi-formal), although some basic logical notation is introduced. The book is written at level which should be appropriate for advanced undergraduate or beginning graduate students. It presupposes some previous coursework in linguistics, but does not presuppose any background in formal logic or set theory.

The Art and Science of Analyzing Software Data
The Art and Science of Analyzing Software Data

Author: Christian Bird

Publisher: Elsevier

Published: 2015-09-02

Total Pages: 673

ISBN-13: 0124115438

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The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science. The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions. - Presents best practices, hints, and tips to analyze data and apply tools in data science projects - Presents research methods and case studies that have emerged over the past few years to further understanding of software data - Shares stories from the trenches of successful data science initiatives in industry

Analyzing Qualitative Data
Analyzing Qualitative Data

Author: Graham R Gibbs

Publisher: SAGE Publications Limited

Published: 2018-10-19

Total Pages: 232

ISBN-13: 9781473915817

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This book tackles the challenges of how to make sense of qualitative data. It offers students and researchers a hands-on guide to the practicalities of coding, comparing data, and using computer-assisted qualitative data analysis. Lastly, Gibbs shows you how to bring it all together, so you can see the steps of qualitative analysis, understand the central place of coding, ensure analytic quality and write effectively to present your results.

Bayesian Data Analysis, Second Edition
Bayesian Data Analysis, Second Edition

Author: Andrew Gelman

Publisher: CRC Press

Published: 2003-07-29

Total Pages: 717

ISBN-13: 1420057294

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Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collection Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.