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Everyone encounters statistics on a daily basis. They are used in proposals, reports, requests, and advertisements, among others, to support assertions, opinions, and theories. Unless you’re a trained statistician, it can be bewildering. What are the numbers really saying or not saying? Better Busin
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Everyone encounters statistics on a daily basis. They are used in proposals, reports, requests, and advertisements, among others, to support assertions, opinions, and theories. Unless you’re a trained statistician, it can be bewildering. What are the numbers really saying or not saying? Better Business Decisions from Data: Statistical Analysis for Professional Success provides the answers to these questions and more. It will show you how to use statistical data to improve small, every-day management judgments as well as major business decisions with potentially serious consequences. Author Peter Kenny—with deep experience in industry—believes that "while the methods of statistics can be complicated, the meaning of statistics is not." He first outlines the ways in which we are frequently misled by statistical results, either because of our lack of understanding or because we are being misled intentionally. Then he offers sound approaches for understanding and assessing statistical data to make excellent decisions. Kenny assumes no prior knowledge of statistical techniques; he explains concepts simply and shows how the tools are used in various business situations. With the arrival of Big Data, statistical processing has taken on a new level of importance. Kenny lays a foundation for understanding the importance and value of Big Data, and then he shows how mined data can help you see your business in a new light and uncover opportunity. Among other things, this book covers: How statistics can help you assess the probability of a successful outcome How data is collected, sampled, and best interpreted How to make effective forecasts based on the data at hand How to spot the misuse or abuse of statistical evidence in advertisements, reports, and proposals How to commission a statistical analysis Arranged in seven parts—Uncertainties, Data, Samples, Comparisons, Relationships, Forecasts, and Big DataBetter Business Decisions from Data is a guide for busy people in general management, finance, marketing, operations, and other business disciplines who run across statistics on a daily or weekly basis. You’ll return to it again and again as new challenges emerge, making better decisions each time that boost your organization’s fortunes—as well as your own. What you’ll learn How raw data are processed to obtain information, with known reliability, for the basis of decision making. What a statistical analysis can--and can't--do. Why certainty is illusive and how we can be misled by statistical results. The basics of probability, sampling, reliability, regression, distribution and other statistical techniques essential for decision making in all aspects of business. How to commission data gathering and processing in advance of big decisions Who this book is for The primary audience includes managers and professionals in business and industry who need to understand statistics to make or approve decisions, or to commission statistical investigations and assess their results. It's also for those who want to understand how statistics can be used to mislead or shroud the true facts. A secondary audience consists of students of disciplines that require some knowledge of statistics—economics, finance, political science, physics, biology, and more—as well as general readers who simply wish to have a more informed view of the daily dose of statistics offered up by news organizations, advocacy groups, and the government, among others. Table of Contents Part I: Uncertainties Chapter 1: The Scarcity of Certainty Chapter 2: Sources of Uncertainty Chapter 3: Probability Part II: Data Chapter 4: Sampling Chapter 5: The Raw Data Part III: SamplesThe Chapter 6: Descriptive Data Chapter 7: Numerical Data Part IV: Comparisons Chapter 8: Levels of Significance Chapter 9: General Procedure for Comparisons Chapter 10: Comparisons with Numerical Data Chapter 11: Comparisons with Descriptive Data Chapter 12: Types of Error Part V: Relationships Chapter 13: Cause and Effect Chapter 14: Relationships with Numerical Data Chapter 15: Relationships with Descriptive Data Chapter 16: Multivariate Data Part VI: Forecasts Chapter 17: Extrapolation Chapter 18: Forecasting from Known Distributions Chapter 19: Time Series Chapter 20: Control Charts Chapter 21: Reliability Part VII: Big Data Chapter 22: Data Mining Chapter 23: Predictive Analytics Chapter 24: Getting Involved with Big Data Chapter 25: Concerns with Big Data Chapter 26: References and Further Reading
Book Description Business Intelligence (BI) software allows you to view different components of a business using a single visual platform, which makes comprehending mountains of data easier. BI is everywhere. Applications that include reports, analytics, statistics, and historical and predictive modeling are all examples of BI. Currently, we are in the second generation of BI software—called BI 2.0—which is focused on writing BI software that is predictive, adaptive, simple, and interactive. As computers and software have evolved, more data can be presented to end users with increasingly visually rich techniques. Rich Internet Application (RIA) technologies such as Microsoft Silverlight can be used to transform traditional user-interfaces filled with boring data into fully interactive analytical applications that quickly deliver insight from large data sets. Furthermore, RIA applications now include 3D spatial-design capabilities that move beyond a simple list or grid and allow for interesting layouts of aggregated data. BI 2.0 implemented via an RIA technology can truly bring out the power of BI and deliver it to an average user on the Web. Next-Generation Business Intelligence Software with Silverlight 4 provides developers, designers, and architects with a solid foundation in BI design and architecture concepts for Microsoft Silverlight. This book covers key BI design concepts and how they can be applied without an existing BI infrastructure. Author Bart Czernicki provides you with examples of how to build small BI applications that are interactive, highly visual, statistical, predictive—and most importantly—intuitive to the end-user. BI isn’t just for the executive branch of a Fortune 500 company—it is for the masses. Let Next-Generation Business Intelligence Software with Silverlight 4 show you how to unlock the rich intelligence you already have. What you’ll learn Design rich BI solutions for the masses Discover tips for using engaging BI designs for application presentation Consume BI data as a service Understand and create SaaS models in BI Craft BI solutions using the most advanced Silverlight 4.0 technology through C# 4.0 and Visual Studio 2010 Who this book is for There are four core audiences for this book: Business intelligence professionals/developers: Cube designers, report writers, SharePoint administrators, and others in this group are looking for a resource to gain additional wisdom on cutting-edge BI concepts. These readers will gain ideas that go beyond the capabilities of the traditional BI tools they are used to. Senior developers or architects: Developers who are familiar with writing traditional software applications and want to broaden their understanding of concepts in order to include BI fall into this category. Some may have never written any BI applications in their careers but are interested in what BI has to offer. These readers will understand the core BI concepts and how to apply them using application techniques. CTOs/BI directors: These are senior managers who make strategic decisions with BI. The technical examples will probably be overly technical for these readers. However, the numerous visual aids within this book will help these strategic managers make better decisions after seeing next-generation BI concepts implemented. BI consultants: These readers may be any combination of the preceding three categories. The BI consultant can use this book as a resource for ideas and examples of next-generation BI designs. Table of Contents 1.Business Intelligence 2.0 Defined 2.Advantages of Applying BI 2.0 Using Microsoft Silverlight 3.Silverlight as a BI Client 4.Adding Interactivity to BI Data 5.Introduction to Data Visualizations 6.Creating Data Visualizations for Analysis 7.Enhancing Visual Intelligence in Silverlight 8.Applying Collective Intelligence 9.Predictive Analytics 10.Improving Performance with Concurrent Programming 11.Integrating with Business Intelligence Systems 12.Delivering Mobile Intelligence with Silverlight 13.Silverlight Business Intelligence in SharePoint 2010 14.Working with Interactive Pivot Collections 15.Appendix A – Prototyping Applications with Dynamic Data 16.Appendix B – Creating a Bullet Graph user control About the Author Bart Czernicki has been playing around with computers since 1988 and has spent years as a professional in the IT field. He currently works as a senior software architect at a software development company. Book Details Paperback: 550 pages Publisher: Apress; 2 edition (September, 2010) Language: English ISBN-10: 1430230606 ISBN-13: 978-1430230601 File Size: 11.9 MiB Hits: 1,158 times
Intelligent Analytics for your Intelligent devices 针对智能设备的数据智能分析 Book Description Break through the hype and learn how to extract actionable intelligence from the flood of IoT data About This Book Make better business decisions and acquire greater control of your IoT infrastructure Learn techniques to solve unique problems associated with IoT and examine and analyze data from your IoT devices Uncover the business potential generated by data from IoT devices and bring down business costs Who This Book Is For This book targets developers, IoT professionals, and those in the field of data science who are trying to solve business problems through IoT devices and would like to analyze IoT data. IoT enthusiasts, managers, and entrepreneurs who would like to make the most of IoT will find this equally useful. A prior knowledge of IoT would be helpful but is not necessary. Some prior programming experience would be useful What You Will Learn Overcome the challenges IoT data brings to analytics Understand the variety of transmission protocols for IoT along with their strengths and weaknesses Learn how data flows from the IoT device to the final data set Develop techniques to wring value from IoT data Apply geospatial analytics to IoT data Use machine learning as a predictive method on IoT data Implement best strategies to get the most from IoT analytics Master the economics of IoT analytics in order to optimize business value In Detail We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques. Next we review how IoT devices generate data and how the information travels over networks. You’ll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns. Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We’ll also review the economics of IoT analytics and you’ll discover ways to optimize business value. By the end of the book, you’ll know how to handle scale for both data storage and analytics, how Apache Spark can be leveraged to handle scalability, and how R and Python can be used for analytic modeling. Style and approach This book follows a step-by-step, practical approach to combine the power of analytics and IoT and help you get results quickly Contents Chapter 1. Questions Chapter 2. Defining Iot Analytics And Challenges Chapter 3. Iot Devices And Networking Protocols Chapter 4. Iot Analytics For The Cloud Chapter 5. Creating An Aws Cloud Analytics Environment Chapter 6. Collecting All That Data – Strategies And Techniques Chapter 7. Getting To Know Your Data – Exploring Iot Data Chapter 8. Decorating Your Data – Adding External Datasets To Innovate Chapter 9. Communicating With Others – Visualization And Dashboarding Chapter 10. Applying Geospatial Analytics To Iot Data Chapter 11. Data Science For Iot Analytics Chapter 12. Strategies To Organize Data For Analytics Chapter 13. The Economics Of Iot Analytics Chapter 14. Bringing It All Together
Silverlight 4 Business Intelligence Software Business Intelligence (BI) software allows you to view different components of a business using a single visual platform, which makes comprehending mountains of data easier. BI is everywhere. Applications that include reports, analytics, statistics, and historical and predictive modeling are all examples of BI. Currently, we are in the second generation of BI software—called BI 2.0—which is focused on writing BI software that is predictive, adaptive, simple, and interactive. As computers and software have evolved, more data can be presented to end users with increasingly visually rich techniques. Rich Internet Application (RIA) technologies such as Microsoft Silverlight can be used to transform traditional user-interfaces filled with boring data into fully interactive analytical applications that quickly deliver insight from large data sets. Furthermore, RIA applications now include 3D spatial-design capabilities that move beyond a simple list or grid and allow for interesting layouts of aggregated data. BI 2.0 implemented via an RIA technology can truly bring out the power of BI and deliver it to an average user on the Web. Next-Generation Business Intelligence Software with Silverlight 4 provides developers, designers, and architects with a solid foundation in BI design and architecture concepts for Microsoft Silverlight. This book covers key BI design concepts and how they can be applied without an existing BI infrastructure. Author Bart Czernicki provides you with examples of how to build small BI applications that are interactive, highly visual, statistical, predictive—and most importantly—intuitive to the end-user. BI isn’t just for the executive branch of a Fortune 500 company—it is for the masses. Let Next-Generation Business Intelligence Software with Silverlight 4 show you how to unlock the rich intelligence you already have. What you’ll learn •Design rich BI solutions for the masses •Discover tips for using engaging BI designs for application presentation •Consume BI data as a service •Understand and create SaaS models in BI •Craft BI solutions using the most advanced Silverlight 4.0 technology through C# 4.0 and Visual Studio 2010 Who this book is for There are four core audiences for this book: •Business intelligence professionals/developers: Cube designers, report writers, SharePoint administrators, and others in this group are looking for a resource to gain additional wisdom on cutting-edge BI concepts. These readers will gain ideas that go beyond the capabilities of the traditional BI tools they are used to. •Senior developers or architects: Developers who are familiar with writing traditional software applications and want to broaden their understanding of concepts in order to include BI fall into this category. Some may have never written any BI applications in their careers but are interested in what BI has to offer. These readers will understand the core BI concepts and how to apply them using application techniques. •CTOs/BI directors: These are senior managers who make strategic decisions with BI. The technical examples will probably be overly technical for these readers. However, the numerous visual aids within this book will help these strategic managers make better decisions after seeing next-generation BI concepts implemented. •BI consultants: These readers may be any combination of the preceding three categories. The BI consultant can use this book as a resource for ideas and examples of next-generation BI designs. Table of Contents 1.Business Intelligence 2.0 Defined 2.Advantages of Applying BI 2.0 Using Microsoft Silverlight 3.Silverlight as a BI Client 4.Adding Interactivity to BI Data 5.Introduction to Data Visualizations 6.Creating Data Visualizations for Analysis 7.Enhancing Visual Intelligence in Silverlight 8.Applying Collective Intelligence 9.Predictive Analytics 10.Improving Performance with Concurrent Programming 11.Integrating with Business Intelligence Systems 12.Delivering Mobile Intelligence with Silverlight 13.Silverlight Business Intelligence in SharePoint 2010 14.Working with Interactive Pivot Collections 15.Appendix A - Prototyping Applications with Dynamic Data 16.Appendix B - Creating a Bullet Graph user control
Extract valuable data from your social media sites and make better business decisions using R About This Book Explore the social media APIs in R to capture data and tame it Employ the machine learning capabilities of R to gain optimal business value A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data Who This Book Is For If you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you. Those with experience in data analysis who are interested in mining social media data will find this book useful. What You Will Learn Access APIs of popular social media sites and extract data Perform sentiment analysis and identify trending topics Measure CTR performance for social media campaigns Implement exploratory data analysis and correlation analysis Build a logistic regression model to detect spam messages Construct clusters of pictures using the K-means algorithm and identify popular personalities and destinations Develop recommendation systems using Collaborative Filtering and the Apriori algorithm In Detail With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data. This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming. With this handy guide, you will be ready to embark on your journey as an independent social media analyst. Style and approach This easy-to-follow guide is packed with hands-on, step-by-step examples that will enable you to convert your real-world social media data into useful, practical information. Table of Contents Chapter 1: Fundamentals of Mining Chapter 2: Mining Opinions, Exploring Trends, and More with Twitter Chapter 3: Find Friends on Facebook Chapter 4: Finding Popular Photos on Instagram Chapter 5: Let's Build Software with GitHub Chapter 6: More Social Media Websites

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