Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop.Get started quickly with an R tutorial and hundreds of examplesExplore R syntax, objects, and other language detailsFind thousands of user-contributed R packages online, including BioconductorLearn how to use R to prepare data for analysisVisualize your data with R’s graphics, lattice, and ggplot2 packagesUse R to calculate statistical fests, fit models, and compute probability distributionsSpeed up intensive computations by writing parallel R programs for HadoopGet a complete desktop reference to R
Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your data
"Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time."—Jeffrey Ryan, software consultant and R package author
Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started.Use R’s default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing
An audacious, irreverent investigation of human behavior—and a first look at a revolution in the making
Our personal data has been used to spy on us, hire and fire us, and sell us stuff we don’t need. In Dataclysm, Christian Rudder uses it to show us who we truly are.
For centuries, we’ve relied on polling or small-scale lab experiments to study human behavior. Today, a new approach is possible. As we live more of our lives online, researchers can finally observe us directly, in vast numbers, and without filters. Data scientists have become the new demographers.
In this daring and original book, Rudder explains how Facebook "likes" can predict, with surprising accuracy, a person’s sexual orientation and even intelligence; how attractive women receive exponentially more interview requests; and why you must have haters to be hot. He charts the rise and fall of America’s most reviled word through Google Search and examines the new dynamics of collaborative rage on Twitter. He shows how people express themselves, both privately and publicly. What is the least Asian thing you can say? Do people bathe more in Vermont or New Jersey? What do black women think about Simon & Garfunkel? (Hint: they don’t think about Simon & Garfunkel.) Rudder also traces human migration over time, showing how groups of people move from certain small towns to the same big cities across the globe. And he grapples with the challenge of maintaining privacy in a world where these explorations are possible.
Visually arresting and full of wit and insight, Dataclysm is a new way of seeing ourselves—a brilliant alchemy, in which math is made human and numbers become the narrative of our time.
From the Hardcover edition.
You'll find recipes on reading data files, creating data frames, computing basic statistics, testing means and correlations, creating a scatter plot, performing simple linear regression, and many more. These solutions were selected from O'Reilly's R Cookbook, which contains more than 200 recipes for R that you'll find useful once you move beyond the basics.
This book supersedes ISBN 9780596550066, from O'Reilly.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.Get a crash course in PythonLearn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn Detail
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
This book covers:Arrays and lists: the most common data structuresStacks and queues: more complex list-like data structuresLinked lists: how they overcome the shortcomings of arraysDictionaries: storing data as key-value pairsHashing: good for quick insertion and retrievalSets: useful for storing unique elements that appear only onceBinary Trees: storing data in a hierarchical mannerGraphs and graph algorithms: ideal for modeling networksAlgorithms: including those that help you sort or search dataAdvanced algorithms: dynamic programming and greedy algorithms
By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.
New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.Develop an understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyImport data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data
Bradley Holt, co-founder of the creative services firm Found Line, is a web developer and entrepreneur ten years of PHP and MySQL experience. He began using CouchDB before the release of version 1.0. Bradley is an active member of the PHP community, and can be reached at bradley-holt.com.
This updated second edition provides guidance for database developers, advanced configuration for system administrators, and an overview of the concepts and use cases for other people on your project. Ideal for NoSQL newcomers and experienced MongoDB users alike, this guide provides numerous real-world schema design examples.Get started with MongoDB core concepts and vocabularyPerform basic write operations at different levels of safety and speedCreate complex queries, with options for limiting, skipping, and sorting resultsDesign an application that works well with MongoDBAggregate data, including counting, finding distinct values, grouping documents, and using MapReduceGather and interpret statistics about your collections and databasesSet up replica sets and automatic failover in MongoDBUse sharding to scale horizontally, and learn how it impacts applicationsDelve into monitoring, security and authentication, backup/restore, and other administrative tasks
The code-packed examples in this book will help you learn how to work with documents, populate a simple database, replicate data from one database to another, and a host of other tasks.Install CouchDB on Linux, Mac OS X, Windows, or (if you must) from the source codeInteract with data through CouchDB’s RESTful API, and use standard HTTP operations, such as PUT, GET, POST, and DELETEUse Futon—CouchDB’s web-based interface— to manage databases and documents, and to configure replicationsLearn how to create, update, and delete documents in JSON format, and how to create and delete databasesWork with design documents to get the formatting and indexing your application requires
"The authors have appreciated that MDM is a complex multidimensional area, and have set out to cover each of these dimensions in sufficient detail to provide adequate practical guidance to anyone implementing MDM. While this necessarily makes the book rather long, it means that the authors achieve a comprehensive treatment of MDM that is lacking in previous works." -- Malcolm Chisholm, Ph.D., President, AskGet.com Consulting, Inc.
Regain control of your master data and maintain a master-entity-centric enterprise data framework using the detailed information in this authoritative guide. Master Data Management and Data Governance, Second Edition provides up-to-date coverage of the most current architecture and technology views and system development and management methods. Discover how to construct an MDM business case and roadmap, build accurate models, deploy data hubs, and implement layered security policies. Legacy system integration, cross-industry challenges, and regulatory compliance are also covered in this comprehensive volume.Plan and implement enterprise-scale MDM and Data Governance solutions Develop master data model Identify, match, and link master records for various domains through entity resolution Improve efficiency and maximize integration using SOA and Web services Ensure compliance with local, state, federal, and international regulations Handle security using authentication, authorization, roles, entitlements, and encryption Defend against identity theft, data compromise, spyware attack, and worm infection Synchronize components and test data quality and system performance
It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.
This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision.Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth EditionSolved examples in Matlab, including real-life data sets in imaging and audio recognitionAvailable separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)
The Concept and Object Modeling Notation (COMN) is able to cover the full spectrum of analysis and design. A single COMN model can represent the objects and concepts in the problem space, logical data design, and concrete NoSQL and SQL document, key-value, columnar, and relational database implementations. COMN models enable an unprecedented level of traceability of requirements to implementation. COMN models can also represent the static structure of software and the predicates that represent the patterns of meaning in databases.
This book will teach you:the simple and familiar graphical notation of COMN with its three basic shapes and four line styles how to think about objects, concepts, types, and classes in the real world, using the ordinary meanings of English words that aren’t tangled with confused techno-speak how to express logical data designs that are freer from implementation considerations than is possible in any other notation how to understand key-value, document, columnar, and table-oriented database designs in logical and physical terms how to use COMN to specify physical database implementations in any NoSQL or SQL database with the precision necessary for model-driven development
Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps.Create analytics applications by using the agile big data development methodologyBuild value from your data in a series of agile sprints, using the data-value stackGain insight by using several data structures to extract multiple features from a single datasetVisualize data with charts, and expose different aspects through interactive reportsUse historical data to predict the future, and translate predictions into actionGet feedback from users after each sprint to keep your project on track
This book supersedes ISBN 9780596550066, from O'Reilly.
Written by Oracle ACE Director and MySQL expert Ronald Bradford, with coauthor Chris Schneider, Effective MySQL: Replication Techniques in Depth describes what is needed to understand and implement MySQL replication to build scalable solutions. This book includes detailed syntax examples to demonstrate the features, options, and limitations of native MySQL replication. Providing an evaluation of various new replication features and additional third-party product implementations, this Oracle Press guide helps to ensure your MySQL environment can support the various high-availability needs of your business.Master the strengths and limitations of native asynchronous replication in a MySQL topology Identify the important features to improve replication for growing business requirements Recognize the key business factors to determine your optimal highavailability needs Understand the benefits of using MySQL replication for failover scenarios Identify the key configuration variables and SQL commands affecting master/ slave replication Learn about the advancements in replication techniques provided by new products, including Tungsten Replicator and Galera Optimize your replication management with various utilities and toolkits
Find additional detailed information and presentations at EffectiveMySQL.com.
Implementing Splunk Second Edition is a learning guide that introduces you to all the latest features and improvements of Splunk 6.2. The book starts by introducing you to various concepts such as charting, reporting, clustering, and visualization. Every chapter is dedicated to enhancing your knowledge of a specific concept, including data models and pivots, speeding up your queries, backfilling, data replication, and so on. By the end of the book, you'll have a very good understanding of Splunk and be able to perform efficient data analysis.
· Introduces the concept of discrete event Monte Carlo simulation, the most commonly used methodology for modeling and analysis of complex systems
· Covers essential workings of the popular animated simulation language, ARENA, including set-up, design parameters, input data, and output analysis, along with a wide variety of sample model applications from production lines to transportation systems
· Reviews elements of statistics, probability, and stochastic processes relevant to simulation modeling
* Ample end-of-chapter problems and full Solutions Manual
* Includes CD with sample ARENA modeling programs
How to Cheat in Unity 5takes a no-nonsense approach to help you achieve fast and effective results with Unity 5. Geared towards the intermediate user, HTC in Unity 5 provides content beyond what an introductory book offers, and allows you to work more quickly and powerfully in Unity. Packed full with easy-to-follow methods to get the most from Unity, this book explores time-saving features for interface customization and scene management, along with productivity-enhancing ways to work with rendering and optimization. In addition, this book features a companion website at www.alanthorn.net, where you can download the book’s companion files and also watch bonus tutorial video content.
Learn bite-sized tips and tricks for effective Unity workflows
Become a more powerful Unity user through interface customization
Enhance your productivity with rendering tricks, better scene organization and more
Better understand Unity asset and import workflows
Learn techniques to save you time and money during development
The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.
Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to:
* Create artful graphs to visualize complex data sets and functions
* Write more efficient code using parallel R and vectorization
* Interface R with C/C++ and Python for increased speed or functionality
* Find new R packages for text analysis, image manipulation, and more
* Squash annoying bugs with advanced debugging techniques
Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.
Written by Oracle ACE Director and MySQL expert Ronald Bradford, Effective MySQL: Optimizing SQL Statements is filled with detailed explanations and practical examples that can be applied immediately to improve database and application performances. Featuring a step-by-step approach to SQL optimization, this Oracle Press book helps you to analyze and tune problematic SQL statements.Identify the essential analysis commands for gathering and diagnosing issues Learn how different index theories are applied and represented in MySQL Plan and execute informed SQL optimizations Create MySQL indexes to improve query performance Master the MySQL query execution plan Identify key configuration variables that impact SQL execution and performance Apply the SQL optimization lifecycle to capture, identify, confirm, analyze, and optimize SQL statements and verify the results Improve index utilization with covering indexes and partial indexes Learn hidden performance tips for improving index efficiency and simplifying SQL statements
Until now, there has not been a book focused squarely on the language topics of special concern to DBAs Oracle PL/SQL for DBAs fills the gap. Covering the latest Oracle version, Oracle Database 10g Release 2 and packed with code and usage examples, it contains:A quick tour of the PL/SQL language, providing enough basic information about language fundamentals to get DBAs up and runningExtensive coverage of security topics for DBAs: Encryption (including both traditional methods and Oracle's new Transparent Data Encryption, TDE); Row-Level Security(RLS), Fine-Grained Auditing (FGA); and random value generationMethods for DBAs to improve query and database performance with cursors and table functionsCoverage of Oracle scheduling, which allows jobs such as database monitoring andstatistics gathering to be scheduled for regular execution
Using Oracle's built-in packages (DBMS_CRYPTO, DBMS_RLS, DBMS_FGA, DBMS_RANDOM,DBMS_SCHEDULING) as a base, the book describes ways of building on top of these packages to suit particular organizational needs. Authors are Arup Nanda, Oracle Magazine 2003 DBA of the Year, and Steven Feuerstein, the world's foremost PL/SQL expert and coauthor of the classic reference, Oracle PL/SQL Programming.
DBAs who have not yet discovered how helpful PL/SQL can be will find this book a superb introduction to the language and its special database administration features. Even if you have used PL/SQL for years, you'll find the detailed coverage in this book to be an invaluable resource.
The textbook looks at the fundamentals of probability theory, from the basic concepts of set-based probability, through probability distributions, to bounds, limit theorems, and the laws of large numbers. Discrete and continuous-time Markov chains are analyzed from a theoretical and computational point of view. Topics include the Chapman-Kolmogorov equations; irreducibility; the potential, fundamental, and reachability matrices; random walk problems; reversibility; renewal processes; and the numerical computation of stationary and transient distributions. The M/M/1 queue and its extensions to more general birth-death processes are analyzed in detail, as are queues with phase-type arrival and service processes. The M/G/1 and G/M/1 queues are solved using embedded Markov chains; the busy period, residual service time, and priority scheduling are treated. Open and closed queueing networks are analyzed. The final part of the book addresses the mathematical basis of simulation.
Each chapter of the textbook concludes with an extensive set of exercises. An instructor's solution manual, in which all exercises are completely worked out, is also available (to professors only).Numerous examples illuminate the mathematical theories Carefully detailed explanations of mathematical derivations guarantee a valuable pedagogical approach Each chapter concludes with an extensive set of exercises
Importantly, the slides are editable, so they can be readily adapted to a lecturer’s personal style and course content needs. The lectures are based on excerpts from 12 of the first 13 chapters of DSBMS. They are designed to highlight the key course material, as a study guide and structure for students following the full text content.
The complete PowerPoint slide package (~25 MB) can be obtained by instructors (or prospective instructors) by emailing the author directly, at: firstname.lastname@example.org
Discover how this open source server can help your application gain scalability and performance.Learn how the server’s architecture affects the way you build and deploy your databaseStore data without defining a data structure—and retrieve it without complex queries or query languagesUse a formula to estimate your cluster size requirementsSet up individual nodes through a browser, command line, or REST APIEnable your application to read and write data with sub-millisecond latency through managed object cachingGet a quick guide to building applications that integrate Couchbase’s core protocolIdentify problems in your cluster with the web consoleExpand or shrink your cluster, handle failovers, and back up data
With Beautiful Data, you will:Explore the opportunities and challenges involved in working with the vast number of datasets made available by the WebLearn how to visualize trends in urban crime, using maps and data mashupsDiscover the challenges of designing a data processing system that works within the constraints of space travelLearn how crowdsourcing and transparency have combined to advance the state of drug researchUnderstand how new data can automatically trigger alerts when it matches or overlaps pre-existing dataLearn about the massive infrastructure required to create, capture, and process DNA data
That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:
Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.
With this handbook, you’ll learn how to use:IPython and Jupyter: provide computational environments for data scientists using PythonNumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in PythonPandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in PythonMatplotlib: includes capabilities for a flexible range of data visualizations in PythonScikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Now, what if you had a time machine and could go back and read this book. You would learn that even NoSQL databases like MongoDB require some level of data modeling. Data modeling is the process of learning about the data, and regardless of technology, this process must be performed for a successful application. You would learn the value of conceptual, logical, and physical data modeling and how each stage increases our knowledge of the data and reduces assumptions and poor design decisions.
Read this book to learn how to do data modeling for MongoDB applications, and accomplish these five objectives:
Understand how data modeling contributes to the process of learning about the data, and is, therefore, a required technique, even when the resulting database is not relational. That is, NoSQL does not mean NoDataModeling! Know how NoSQL databases differ from traditional relational databases, and where MongoDB fits. Explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts, and learn the basics of adding, querying, updating, and deleting data in MongoDB. Practice a streamlined, template-driven approach to performing conceptual, logical, and physical data modeling. Recognize that data modeling does not always have to lead to traditional data models! Distinguish top-down from bottom-up development approaches and complete a top-down case study which ties all of the modeling techniques together.
This book is written for anyone who is working with, or will be working with MongoDB, including business analysts, data modelers, database administrators, developers, project managers, and data scientists. There are three sections:In Section I, Getting Started, we will reveal the power of data modeling and the tight connections to data models that exist when designing any type of database (Chapter 1), compare NoSQL with traditional relational databases and where MongoDB fits (Chapter 2), explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts (Chapter 3), and explain the basics of adding, querying, updating, and deleting data in MongoDB (Chapter 4).
In Section II, Levels of Granularity, we cover Conceptual Data Modeling (Chapter 5), Logical Data Modeling (Chapter 6), and Physical Data Modeling (Chapter 7). Notice the “ing” at the end of each of these chapters. We focus on the process of building each of these models, which is where we gain essential business knowledge.
In Section III, Case Study, we will explain both top down and bottom up development approaches and go through a top down case study where we start with business requirements and end with the MongoDB database. This case study will tie together all of the techniques in the previous seven chapters.
Nike Senior Data Architect Ryan Smith wrote the foreword. Key points are included at the end of each chapter as a way to reinforce concepts. In addition, this book is loaded with hands-on exercises, along with their answers provided in Appendix A. Appendix B contains all of the book’s references and Appendix C contains a glossary of the terms used throughout the text.
This book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social media platforms and perform statistical analysis in order to produce useful insights from data. The book assumes a basic understanding of the Python Standard Library and provides practical examples to guide you toward the creation of your data analysis project based on social data.What You Will LearnInteract with a social media platform via their public API with PythonStore social data in a convenient format for data analysisSlice and dice social data using Python tools for data scienceApply text analytics techniques to understand what people are talking about on social mediaApply advanced statistical and analytical techniques to produce useful insights from dataBuild beautiful visualizations with web technologies to explore data and present data productsIn Detail
Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights.
This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data.Style and approach
This practical, hands-on guide will help you learn everything you need to perform data mining for social media. Throughout the book, we take an example-oriented approach to use Python for data analysis and provide useful tips and tricks that you can use in day-to-day tasks.
This book is for data analysts, business analysts, data science professionals or anyone who wants to learn analytic approaches to business problems. Basic familiarity with R is expected.What You Will LearnExtract, clean, and transform dataValidate the quality of the data and variables in datasetsLearn exploratory data analysisBuild regression modelsImplement popular data-mining algorithmsVisualize results using popular graphsPublish the results as a dashboard through Interactive Web Application frameworksIn Detail
Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance.
In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards.
After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.Style and approach
This book will take a step-by-step approach and instruct you in how you can achieve Business Intelligence from scratch using R. We will start with extracting data and then move towards exploring, analyzing, and visualizing it. Eventually, you will learn how to create insightful dashboards that help you make informed decisions—and all of this with the help of real-life examples.
Complete Web Monitoring demonstrates how to measure every aspect of your web presence -- including analytics, backend performance, usability, communities, customer feedback, and competitive analysis -- whether you're running an e-commerce site, a community, a media property, or a Software-as-a-Service company. This book's concrete examples, clear explanations, and practical recommendations make it essential for anyone who runs a website.
With this book you will:
Discover how visitors use and interact with your site through web analytics, segmentation, conversions, and user interaction analysisFind out your market's motivations with voice-of-the-customer researchMeasure the health and availability of your website with synthetic testing and real-user monitoringTrack communities related to your online presence, including social networks, forums, blogs, microblogs, wikis, and social news aggregatorsUnderstand how to assemble this data into clear reports tailored to your organization and audience
You can't fix what you don't measure. Complete Web Monitoring shows you how to transform missed opportunities, frustrated users, and spiraling costs into online success.
"This is a very comprehensive view of just about everything one needs to know about how websites work and what one needs to know about them. I'd like to make this book required reading for every employee at Gomez."-- Imad Mouline, CTO of Gomez
Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra’s non-relational design, with special attention to data modeling. If you’re a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra’s speed and flexibility.Understand Cassandra’s distributed and decentralized structureUse the Cassandra Query Language (CQL) and cqlsh—the CQL shellCreate a working data model and compare it with an equivalent relational modelDevelop sample applications using client drivers for languages including Java, Python, and Node.jsExplore cluster topology and learn how nodes exchange dataMaintain a high level of performance in your clusterDeploy Cassandra on site, in the Cloud, or with DockerIntegrate Cassandra with Spark, Hadoop, Elasticsearch, Solr, and Lucene
The 54 revised full papers presented in this volume were carefully reviewed and selected from 148 submissions.
The Algorithms and Data Structures Symposium - WADS (formerly Workshop on Algorithms And Data Structures), which alternates with the Scandinavian Workshop on Algorithm Theory, is intended as a forum for researchers in the area of design and analysis of algorithms and data structures. WADS includes papers presenting original research on algorithms and data structures in all areas, including bioinformatics, combinatorics, computational geometry, databases, graphics, and parallel and distributed computing.