Data Analytics: Books

Big Data, Big Innovation by Evan Stubbs A practical guide to leveraging your data to spur innovation and growth Your business generates reams of data, but what do you do with it? Reporting is only the beginning. Your data holds the key to innovation and growth - you just need the proper analytics. In "Big Data, Big Innovation: Enabling Competitive Differentiation Through Business Analytics," author Evan Stubbs explores the potential gold hiding in your un-mined data. As Chief Analytics Officer for SAS Australia/New Zealand, Stubbs brings an industry insider's perspective to guide you through pattern recognition, analysis, and implementation. "Big Data, Big Innovation: Enabling Competitive Differentiation Through Business Analytics" details a groundbreaking approach to ensuring your company's upward trajectory. Use this guide to leverage your customer information, financial reports, performance metrics, and more to build a rock-solid foundation for future growth. Build an effective analytics team, and empower them with the right tools Learn how big data drives both evolutionary and revolutionary innovation, and who should be responsible Identify data collection and analysis opportunities and implement action plans Design the platform that suits your company's current and future needs Quantify performance with statistics, programming, and research for a more complete picture of operations Effective management means combining data, people, and analytics to create a synergistic force for innovation and growth. If you want your company to move forward with confidence, "Big Data, Big Innovation: Enabling Competitive Differentiation Through Business Analytics" can show you how to use what you already have and acquire what you need to succeed.

Excel® 2016 All-in-One for Dummies® by Greg Harvey Your one-stop guide to all things Excel 2016 Excel 2016 All-in-One For Dummies, the most comprehensive Excel reference on the market, is completely updated to reflect Microsoft's changes in the popular spreadsheet tool. It offers you everything you need to grasp basic Excel functions, such as creating and editing worksheets, setting up formulas, importing data, performing statistical functions, editing macros with Visual Basic'and beyond. In no time, your Excel skills will go from 'meh' to excellent. Written by expert Greg Harvey, who has sold more than 4.5 million copies of his previous books combined and has taught and trained extensively in Microsoft Excel, this all-encompassing guide offers everything you need to get started with Excel. From generating pivot tables and performing financial functions to performing error trapping and building and running macros'and everything in between'this hands-on, friendly guide makes working with Excel easier than ever before. Serves as the ideal reference for solving common questions and Excel pain points quickly and easily Helps to increase productivity and efficiency when working in Excel Fully updated for the new version of Excel Covers basic and more advanced Excel topics If working in Excel occasionally makes you want to scream, this will be the dog-eared, dust-free reference you'll turn to again and again.

Data Smart by John W. Foreman Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.  Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype.  But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.  Each chapter will cover a different technique in a spreadsheet so you can follow along: Mathematical optimization, including non-linear programming and genetic algorithms Clustering via k-means, spherical k-means, and graph modularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, and bag-of-words models Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

Fundamentals of Database Systems by Ramez Elmasri; Shamkant B. Navathe This edition combines clear explanations of database theory and design with up-to-date coverage of models and real systems. It features excellent examples and access to Addison Wesley's database Web site that includes further teaching, tutorials and many useful student resources.

SQL by James R. Groff; Paul N. Weinberg; James Groff; Paul Weinberg; Andy Oppel The Definitive Guide to SQL Get comprehensive coverage of every aspect of SQL from three leading industry experts. Revised with coverage of the latest RDBMS software versions, this one-stop guide explains how to build, populate, and administer high-performance databases and develop robust SQL-based applications.  SQL: The Complete Reference, Third Edition shows you how to work with SQL commands and statements, set up relational databases, load and modify database objects, perform powerful queries, tune performance, and implement reliable security policies. Learn how to employ DDL statements and APIs, integrate XML and Java scripts, use SQL objects, build web servers, handle remote access, and perform distributed transactions. Techniques for managing in-memory, stream, and embedded databases that run on today's mobile, handheld, and wireless devices are included in this in-depth volume. Build SQL-based relational databases and applications Create, load, and modify database objects using SQL Construct and execute simple, multitable, and summary queries Implement security measures with authentication, privileges, roles, and views Handle database optimization, backup, recovery, and replication Work with stored procedures, functions, extensions, triggers, and objects Extend functionality using APIs, dynamic SQL, and embedded SQL Explore advanced topics such as DBMS transactions, locking mechanisms, materialized views, and two-phase commit protocol Understand the latest market trends and the future of SQL

Delivering Business Analytics by Evan Stubbs AVOID THE MISTAKES THAT OTHERS MAKE - LEARN WHAT LEADS TO BEST PRACTICE AND KICKSTART SUCCESS This groundbreaking resource provides comprehensive coverage across all aspects of business analytics, presenting proven management guidelines to drive sustainable differentiation. Through a rich set of case studies, author Evan Stubbs reviews solutions and examples to over twenty common problems spanning managing analytics assets and information, leveraging technology, nurturing skills, and defining processes. "Delivering Business Analytics" also outlines the Data Scientist's Code, fifteen principles that when followed ensure constant movement towards effective practice. Practical advice is offered for addressing various analytics issues; the advantages and disadvantages of each issue's solution; and how these solutions can optimally create organizational value. With an emphasis on real-world examples and pragmatic advice throughout, "Delivering Business Analytics" provides a reference guide on: The economic principles behind how business analytics leads to competitive differentiation The elements which define best practice The Data Scientist's Code, fifteen management principles that when followed help teams move towards best practice Practical solutions and frequent missteps to twenty-four common problems across people and process, systems and assets, and data and decision-making Drawing on the successes and failures of countless organizations, author Evan Stubbs provides a densely packed practical reference on how to increase the odds of success in designing business analytics systems and managing teams of data scientists. Uncover what constitutes best practice in business analytics and start achieving it with "Delivering Business Analytics."

Statistical Hypothesis Testing with Sas and R by Dirk Taeger A comprehensive guide to statistical hypothesis testing with examples in SAS and R When analyzing datasets the following questions often arise: " Is there a short hand procedure for a statistical test available in SAS or R?" "If so, how do I use it?" "If not, how do I program the test myself?" This book answers these questions and provides an overview of the most common statistical test problems in a comprehensive way, making it easy to find and perform an appropriate statistical test. A general summary of statistical test theory is presented, along with a basic description for each test, including the necessary prerequisites, assumptions, the formal test problem and the test statistic. Examples in both SAS and R are provided, along with program code to perform the test, resulting output and remarks explaining the necessary program parameters. Key features: - Provides examples in both SAS and R for each test presented. - Looks at the most common statistical tests, displayed in a clear and easy to follow way. - Supported by a supplementary website http: //www.d-taeger.de featuring example program code. Academics, practitioners and SAS and R programmers will find this book a valuable resource. Students using SAS and R will also find it an excellent choice for reference and data analysis.

The Art of R Programming by Norman Matloff

Excel Data Analysis for Dummies by Stephen L. Nelson; E. C. Nelson Harness the power of Excel to discover what your numbers are hiding "Excel Data Analysis For Dummies, 2nd Edition" is the ultimate guide to getting the most out of your data. Veteran "Dummies" author Stephen L. Nelson guides you through the basic and not-so-basic features of Excel to help you discover the gems hidden in your rough data. From input, to analysis, to visualization, the book walks you through the steps that lead to superior data analysis. Excel is the number-one spreadsheet application, with ever-expanding capabilities. If you're only using it to balance the books, you're missing out on a host of functions that can benefit your business or personal finances by uncovering trends and other important information hidden within the numbers. "Excel Data Analysis For Dummies, 2nd Edition" eliminates the need for advanced statistics or analysis courses by allowing you to harness the full power of Excel to do the heavy lifting for you. This 2nd Edition is fully updated to include information about Excel's latest features, making it a your go-to Excel guide for data analysis. Topics include: Working with external databases PivotTables and PivotCharts Using Excel for statistical and financial functions Solver, Small Business Finance Manager, and more The book also includes a guide to chart types and formatting, and advice on effective visual data presentation. You already have the data, so you might as well get something great out of it. "Excel Data Analysis For Dummies, 2nd Edition" is the key to discovering what your numbers are hiding.

Big Data Analytics by Frank J. Ohlhorst Unique insights to implement big data analytics and reap big returns to your bottom line Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in "Big Data Analytics." This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantage Takes an in-depth look at the financial value of big data analytics Offers tools and best practices for working with big data Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, "Big Data Analytics" shows how you can leverage big data into a key component in your business's growth strategy.

Oracle Business Intelligence by Yuli Vailiev This book is a quick guide to getting started with Oracle Business Intelligence SE platform that will answer common business questions and help you make quick business decisions. The examples in this book will help you prepare the database for analysis and create business reports in no time. If you are an business analyst, a report builder, a DBA, or an application developer who wants to learn how to use the Oracle Business Intelligence platform for analysis and reporting, this is the perfect book for you. Previous knowledge of Oracle Business Intelligence tools is not required, but you should have a competent grasp of using an Oracle Database.

Ruby and MongoDB Web Development Beginner's Guide by Gautam Rege Create dynamic web applications by combining the power of Ruby and MongoDB with this book and ebook

SQL for Dummies by Allen G. Taylor Uncover the secrets of SQL and start building better relational databases today This fun and friendly guide will help you demystify database management systems so you can create more powerful databases and access information with ease. Updated for the latest SQL functionality, "SQL For Dummies, 8th Edition" covers the core SQL language and shows you how to use SQL to structure a DBMS, implement a database design, secure your data, and retrieve information when you need it. Includes new enhancements of SQL:2011, including temporal data functionality which allows you to set valid times for transactions to occur and helps prevent database corruption Covers creating, accessing, manipulating, maintaining, and storing information in relational database management systems like Access, Oracle, SQL Server, and MySQL Provides tips for keeping your data safe from theft, accidental or malicious corruption, or loss due to equipment failures and advice on eliminating errors in your work Don't be daunted by database development anymore - get "SQL For Dummies, 8th Edition," and you'll be on your way to SQL stardom.

Introductory Statistics for the Behavioral Sciences by Barry H. Cohen; R. Brooke Lea; Joan Welkowitz A comprehensive and user-friendly introduction to statistics forbehavioral science students--revised and updated Refined over seven editions by master teachers, this book givesinstructors and students alike clear examples and carefully craftedexercises to support the teaching and learning of statistics forboth manipulating and consuming data. One of the most popular and respected statistics texts in thebehavioral sciences, the Seventh Edition of Introductory Statisticsfor the Behavioral Sciences has been fully revised. The new editionpresents all the topics students in the behavioral sciences need ina uniquely accessible and easy-to-understand format, aiding in thecomprehension and implementation of the statistical analyses mostcommonly used in the behavioral sciences. The Seventh Edition features: A continuous narrative that clearly explains statistics whiletracking a common data set throughout, making the conceptsunintimidating and memorable, and providing a framework thatconnects all of the topics and allows for easy comparison ofdifferent statistical analyses Coverage of important aspects of research design throughout thetext, such as the "correlation is not causality" principle Updated and annotated SPSS output at the end of each chapterwith step-by-step instructions Updated examples and exercises An expanded website, at www.wiley.com/go/welkowitz, with testbank, chapter quizzes, and PowerPoint slides for instructors, aswell as a second website for students with additional basic mathcoverage, math review exercises, a study guide, a set of additionalSPSS exercises, and more downloadable data sets

Information Storage and Management by EMC Education Services Staff (Editor) The new edition of a bestseller, now revised and update throughout This new edition of the unparalleled bestseller serves as a full training course all in one and as the world's largest data storage company, EMC is the ideal author for such a critical resource. They cover the components of a storage system and the different storage system models while also offering essential new material that explores the advances in existing technologies and the emergence of the "Cloud" as well as updates and vital information on new technologies. Features a separate section on emerging area of cloud computing Covers new technologies such as: data de-duplication, unified storage, continuous data protection technology, virtual provisioning, FCoE, flash drives, storage tiering, big data, and more Details storage models such as Network Attached Storage (NAS), Storage Area Network (SAN), Object Based Storage along with virtualization at various infrastructure components Explores Business Continuity and Security in physical and virtualized environment Includes an enhanced Appendix for additional information This authoritative guide is essential for getting up to speed on the newest advances in information storage and management.

Data Mining Techniques by Gordon S. Linoff; Michael J. A. Berry The leading introductory book on data mining, fully updated and revised When Berry and Linoff wrote the first edition of "Data Mining Techniques" in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition-more than 50% new and revised-is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel "Data Mining Techniques, Third Edition" covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results."

Hadoop by Garry Turkington As a Packt Beginner's Guide, the book is packed with clear step-by-step instructions for performing the most useful tasks, getting you up and running quickly, and learning by doing. This book assumes no existing experience with Hadoop or cloud services. It assumes you have familiarity with a programming language such as Java or Ruby but gives you the needed background on the other topics.

Data Mining by Ian H. Witten; Eibe Frank As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. * Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods * Performance improvement techniques that work by transforming the input or output * Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface