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Data Visualization with D3 and AngularJS
by Christoph Korner<P><P>Build dynamic and interactive visualizations from real-world data with D3 on AngularJS <P><P>About This Book <P><P>Explore the powerful vector graphics capabilities of modern browsers to build customized cross-platform visualizations using D3.js's data-driven techniques <P><P>Learn how to modularize a visualization into reusable and testable components using the powerful concepts of modern web application design with AngularJS <P><P>This is a step-by-step learning guide closely focused on developing responsive data visualization apps and AngularJS best practices with D3.js <P><P>Who This Book Is For <P><P>If you are a web developer with experience in AngularJS and want to implement interactive visualizations using D3.js, this book is for you. Knowledge of SVG or D3.js will give you an edge to get the most out of this book. <P><P>What You Will Learn <P><P>Design, implement, and integrate an interactive dashboard to visualize server logs in real time using D3 graphics <P><P>Learn cross-platform vector graphics to implement a dashboard visualization <P><P>Perform data-driven transformations on selected HTML and SVG nodes <P><P>Map, group, and filter datasets and create scales and axes <P><P>Modularize data visualization information into reusable components to seamlessly integrate them into an AngularJS application <P><P>Load, parse, and preprocess external data and autoupdate the visualization <P><P>Design various chart types such as scatter, line, bar, or area and extend built-in shapes <P><P>Create custom animations and transitions for the visualization <P><P>Implement interactions and controls for the visualization preserving two-way binding between D3 and AngularJS components <P><P>In Detail <P><P>Using D3.js, the powerful JavaScript toolkit for creating cross-platform vector graphics, you can now combine performance with maximum compatibility to build a web-based visualization and present data in an interactive and convenient way. We'll reach top-notch reusability and testability by combining D3 graphics with our favorite web application framework, AngularJS. <P><P>This book teaches the basics of vector graphics, D3, and AngularJS integration, and then dives into controlling, manipulating, and filtering data. You will learn about the testability of components and how to implement custom interactions, filters, and controllers; discover how to parse and map data in D3.js; and get a grasp on drawing D3.js built-in shapes and curves. After reading the last few chapters, you'll be able to bring life to your visualizations with more features of D3.js such as interactions, animations, and transitions. You will finish your journey by implementing a parser for different server application logs and display them on a Google Analytics style interactive dashboard.
Data Visualization with D3.js Cookbook
by Nick Qi ZhuPacked with practical recipes, this is a step-by-step guide to learning data visualization with D3 with the help of detailed illustrations and code samples.If you are a developer familiar with HTML, CSS, and JavaScript, and you wish to get the most out of D3, then this book is for you. This book can also serve as a desktop quick-reference guide for experienced data visualization developers.
Data Visualization with Excel Dashboards and Reports
by Dick KusleikaLarge corporations like IBM and Oracle are using Excel dashboards and reports as a Business Intelligence tool, and many other smaller businesses are looking to these tools in order to cut costs for budgetary reasons. An effective analyst not only has to have the technical skills to use Excel in a productive manner but must be able to synthesize data into a story, and then present that story in the most impactful way. Microsoft shows its recognition of this with Excel. In Excel, there is a major focus on business intelligence and visualization. Data Visualization with Excel Dashboards and Reports fills the gap between handling data and synthesizing data into meaningful reports. This title will show readers how to think about their data in ways other than columns and rows. Most Excel books do a nice job discussing the individual functions and tools that can be used to create an "Excel Report". Titles on Excel charts, Excel pivot tables, and other books that focus on "Tips and Tricks" are useful in their own right; however they don't hit the mark for most data analysts. The primary reason these titles miss the mark is they are too focused on the mechanical aspects of building a chart, creating a pivot table, or other functionality. They don't offer these topics in the broader picture by showing how to present and report data in the most effective way. What are the most meaningful ways to show trending? How do you show relationships in data? When is showing variances more valuable than showing actual data values? How do you deal with outliers? How do you bucket data in the most meaningful way? How do you show impossible amounts of data without inundating your audience? In Data Visualization with Excel Reports and Dashboards, readers will get answers to all of these questions. Part technical manual, part analytical guidebook; this title will help Excel users go from reporting data with simple tables full of dull numbers, to creating hi-impact reports and dashboards that will wow management both visually and substantively. This book offers a comprehensive review of a wide array of technical and analytical concepts that will help users create meaningful reports and dashboards. After reading this book, the reader will be able to: Analyze large amounts of data and report their data in a meaningful way Get better visibility into data from different perspectives Quickly slice data into various views on the fly Automate redundant reporting and analyses Create impressive dashboards and What-If analyses Understand the fundamentals of effective visualization Visualize performance comparisons Visualize changes and trends over time
Data Visualization with JavaScript
by Stephen A. Thomas<P>You’ve got data to communicate. But what kind of visualization do you choose, how do you build your visualizations, and how do you ensure that they're up to the demands of the Web? <P> In Data Visualization with JavaScript, you’ll learn how to use JavaScript, HTML, and CSS to build practical visualizations for your data. Step-by-step examples walk you through creating, integrating, and debugging different types of visualizations and you'll be building basic visualizations (like bar, line, and scatter graphs) in no time. <P>You'll also learn how to: <br>–Create tree maps, heat maps, network graphs, word clouds, and timelines <br>–Map geographic data, and build sparklines and composite charts–Add interactivity and retrieve data with AJAX <br>–Manage data in the browser and build data-driven web applications <br>–Harness the power of the Flotr2, Flot, Chronoline.js, D3.js, Underscore.js, and Backbone.js libraries <P>If you already know your way around building a web page but aren’t quite sure how to build a good visualization, Data Visualization with JavaScript will help you get your feet wet without throwing you into the deep end. You’ll soon be well on your way to creating simple, powerful data visualizations.
Data Visualization with Microsoft Power BI: How to Design Savvy Dashboards
by Alex Kolokolov Maxim ZelenskyThe sheer volume of business data has reached an all-time high. Using visualizations to transform this data into useful and understandable information can facilitate better decision-making. This practical book shows data analysts as well as professionals in finance, sales, and marketing how to quickly create visualizations and build savvy dashboards.Alex Kolokolov from Data2Speak and Maxim Zelensky from Intelligent Business explain in simple and clear language how to create brilliant charts with Microsoft Power BI and follow best practices for corporate reporting. No technical background is required. Step-by-step guides help you set up any chart in a few clicks and avoid common mistakes. Also, experienced data analysts will find tips and tricks on how to enrich their reports with advanced visuals.This book helps you understand:The basic rules for classic charts that are used in 90% of business reportsExceptions to general rules based on real business casesBest practices for dashboard designHow to properly set up interactionsHow to prepare data for advanced visualsHow to avoid pitfalls with eye-catching charts
Data Visualization with Python and JavaScript: Scrape, Clean, Explore & Transform Your Data
by Kyran DaleLearn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries--including Scrapy, Matplotlib, Pandas, Flask, and D3--for crafting engaging, browser-based visualizations.As a working example, throughout the book Dale walks you through transforming Wikipedia's table-based list of Nobel Prize winners into an interactive visualization. You'll examine steps along the entire toolchain, from scraping, cleaning, exploring, and delivering data to building the visualization with JavaScript's D3 library. If you're ready to create your own web-based data visualizations--and know either Python or JavaScript-- this is the book for you.Learn how to manipulate data with PythonUnderstand the commonalities between Python and JavaScriptExtract information from websites by using Python's web-scraping tools, BeautifulSoup and ScrapyClean and explore data with Python's Pandas, Matplotlib, and Numpy librariesServe data and create RESTful web APIs with Python's Flask frameworkCreate engaging, interactive web visualizations with JavaScript's D3 library
Data Visualization with Python and JavaScript: Scrape, Clean, Explore, and Transform Your Data
by Kyran DaleHow do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries.Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started.You'll learn how to:Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful SoupClean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+SeabornDeliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful APIPick up enough web development skills (HTML, CSS, JS) to get your visualized data on the webUse the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries
Data Visualization with Python for Beginners: Learn to visualize data from scratch with Python
by AI Sciences OUThis book works as a guide to present fundamental Python libraries and basics related to data visualization using PythonKey FeaturesDetailed introductions to several data visualization libraries such as Matplotlib and SeabornGuided instructions to more advanced data visualization skills such as 3D plotting or interactive visualizationHands-on projects for interactive practice designed to cement your new skills in practical memoryBook DescriptionData science and data visualization are two different but interrelated concepts. Data science refers to the science of extracting and exploring data to find patterns that can be used for decision making at different levels. Data visualization can be considered a subdomain of data science. You visualize data with graphs and tables to find out which data is most significant and help identify meaningful patterns.This book is dedicated to data visualization and explains how to perform data visualization on different datasets using various data visualization libraries written in the Python programming language. It is suggested that you use this book for data visualization purposes only and not for decision making. For decision making and pattern identification, read this book in conjunction with a dedicated book on machine learning and data science.We will start by digging into Python programming as all the projects are developed using it, and it is currently the most used programming language in the world. We will also explore some of the most famous libraries for data visualization, such as Pandas, NumPy, Matplotlib, and Seaborn.You will learn all about Python in three modules—plotting with Matplotlib, plotting with Seaborn, and a final one, Pandas for data visualization. All three modules will contain hands-on projects using real-world datasets and a lot of exercises. By the end of this course, you will have the knowledge and skills required to visualize data with Python all on your own.The code bundle for this course is available at https://www.aispublishing.net/book-data-visualizationWhat you will learnBegin visualizing data with MatplotlibExplore the Python Seaborn library for advanced plottingAnalyze data with the Pandas libraryExpand your visualization skills with PandasPlot in three dimensions with MatplotlibPractice interactive data visualization with Bokeh and PlotlyComplete several hands-on projectsWho this book is forThis book is written with one goal in mind—to help beginners overcome their initial obstacles in learning data visualization using Python. This book aims to isolate the different concepts so that beginners can gradually gain competency in the fundamentals of Python before working on a project. As such, no prior experience is required.
Data Visualization with Python: Create an impact with meaningful data insights using interactive and engaging visuals
by Tim GroßmannData Visualization with Python is designed for developers and scientists, who want to get into data science, or want to use data visualizations to enrich their personal and professional projects. You do not need any prior experience in data analytics and visualization, however it’ll help you to have some knowledge of Python and high school level mathematics. Even though this is a beginner level course on data visualization, experienced developers will benefit from improving their Python skills working with real world data.
Data Visualization with d3.js
by Swizec TellerThis book is a mini tutorial with plenty of code examples and strategies to give you many options when building your own visualizations.This book is ideal for anyone interested in data visualization. Some rudimentary knowledge of JavaScript is required.
Data Visualization: A Practical Introduction
by Kieran HealyThis book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions
Data Visualization: A Practical Introduction
by Kieran HealyAn accessible primer on how to create effective graphics from dataThis book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way.Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible.Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings.Provides hands-on instruction using R and ggplot2Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistentIncludes a library of data sets, code, and functions
Data Visualization: Charts, Maps, and Interactive Graphics (ASA-CRC Series on Statistical Reasoning in Science and Society)
by Robert GrantThis is the age of data. There are more innovations and more opportunities for interesting work with data than ever before, but there is also an overwhelming amount of quantitative information being published every day. Data visualisation has become big business, because communication is the difference between success and failure, no matter how clever the analysis may have been. The ability to visualize data is now a skill in demand across business, government, NGOs and academia. Data Visualization: Charts, Maps, and Interactive Graphics gives an overview of a wide range of techniques and challenges, while staying accessible to anyone interested in working with and understanding data. Features: Focusses on concepts and ways of thinking about data rather than algebra or computer code. Features 17 short chapters that can be read in one sitting. Includes chapters on big data, statistical and machine learning models, visual perception, high-dimensional data, and maps and geographic data. Contains more than 125 visualizations, most created by the author. Supported by a website with all code for creating the visualizations, further reading, datasets and practical advice on crafting the images. Whether you are a student considering a career in data science, an analyst who wants to learn more about visualization, or the manager of a team working with data, this book will introduce you to a broad range of data visualization methods. Cover image: Landscape of Change uses data about sea level rise, glacier volume decline, increasing global temperatures, and the increasing use of fossil fuels. These data lines compose a landscape shaped by the changing climate, a world in which we are now living. Copyright © Jill Pelto (jillpelto.com).
Data Visualization: Principles and Practice, Second Edition
by Alexandru C. TeleaDesigning a complete visualization system involves many subtle decisions. When designing a complex, real-world visualization system, such decisions involve many types of constraints, such as performance, platform (in)dependence, available programming languages and styles, user-interface toolkits, input/output data format constraints, integration wi
Data Visualization: Representing Information on Modern Web
by Swizec Teller Simon Timms Andy Kirk Ændrew RininslandUnleash the power of data by creating interactive, engaging, and compelling visualizations for the web About This Book * Get a portable, versatile, and flexible data visualization design approach that will help you navigate the complex path towards success * Get thorough explanation of the many visual variables and visualization taxonomy to provide you with a menu of creative options * A comprehensive and contemporary introduction to data-driven visualization design and the most effective approaches to designing impact-maximizing and cognition-amplifying visualizations Who This Book Is For This course is for developers who are excited about data and who want to share that excitement with others and it will be handy for the web developers or data scientists who want to create interactive visualizations for the web. Prior knowledge of developing web applications is required. You should have a working knowledge of both JavaScript and HTML. What You Will Learn * Harness the power of D3 by building interactive and real-time data-driven web visualizations * Find out how to use JavaScript to create compelling visualizations of social data * Identify the purpose of your visualization and your project's parameters to determine overriding design considerations across your project's execution * Apply critical thinking to visualization design and get intimate with your dataset to identify its potential visual characteristics * Explore the various features of HTML5 to design creative visualizations * Discover what data is available on Stack Overflow, Facebook, Twitter, and Google+ * Gain a solid understanding of the common D3 development idioms * Find out how to write basic D3 code for server using Node.js In Detail Do you want to create more attractive charts? Or do you have huge data sets and need to unearth the key insights in a visual manner? Data visualization is the representation and presentation of data, using proven design techniques to bring alive the patterns, stories, and key insights that are locked away. This learning path is divided into three modules. The first module will equip you with the key techniques required to overcome contemporary data visualization challenges. In the second module, Social Data Visualization with HTML5 and JavaScript, it teaches you how to leverage HTML5 techniques through JavaScript to build visualizations. In third module, Learning d3.js Data Visualization, will lead you to D3, which has emerged as one of the leading platforms to develop beautiful, interactive visualizations over the web. By the end of this course, you will have unlocked the mystery behind successful data visualizations. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: ? Data Visualization: a successful design process by Andy Kirk ? Social Data Visualization with HTML5 and JavaScript by Simon Timms ? Learning d3.js Data Visualization, Second Edition by Ændrew Rininsland and Swizec Teller Style and approach This course includes all the resources that will help you jump into creating interactive and engaging visualizations for the web. Through this comprehensive course, you'll learn how to create engaging visualizations for the web to represent your data from start to finish!
Data Visualization: Trends and Challenges Toward Multidisciplinary Perception
by S. Margret Anouncia Hardik A. Gohel Subbiah VairamuthuThis book discusses the recent trends and developments in the fields of information processing and information visualization. In view of the increasing amount of data, there is a need to develop visualization techniques to make that data easily understandable. Presenting such approaches from various disciplines, this book serves as a useful resource for graduates.
Data Visualization: a successful design process
by Andy KirkA comprehensive yet quick guide to the best approaches to designing data visualizations, with real examples and illustrative diagrams. Whatever the desired outcome ensure success by following this expert design process. This book is for anyone who has responsibility for, or is interested in trying to find innovative and effective ways to visually analyze and communicate data. There is no skill, no knowledge and no role-based pre-requisites or expectations of anyone reading this book.
Data Warehouse Designs: Achieving ROI with Market Basket Analysis and Time Variance
by Fon SilversMarket Basket Analysis (MBA) provides the ability to continually monitor the affinities of a business and can help an organization achieve a key competitive advantage. Time Variant data enables data warehouses to directly associate events in the past with the participants in each individual event. In the past however, the use of these powerful tools in tandem led to performance degradation and resulted in unactionable and even damaging information. Data Warehouse Designs: Achieving ROI with Market Basket Analysis and Time Variance presents an innovative, soup-to-nuts approach that successfully combines what was previously incompatible, without degradation, and uses the relational architecture already in place. Built around two main chapters, Market Basket Solution Definition and Time Variant Solution Definition, it provides a tangible how-to design that can be used to facilitate MBA within the context of a data warehouse. Presents a solution for creating home-grown MBA data marts Includes database design solutions in the context of Oracle, DB2, SQL Server, and Teradata relational database management systems (RDBMS) Explains how to extract, transform, and load data used in MBA and Time Variant solutions The book uses standard RDBMS platforms, proven database structures, standard SQL and hardware, and software and practices already accepted and used in the data warehousing community to fill the gaps left by most conceptual discussions of MBA. It employs a form and language intended for a data warehousing audience to explain the practicality of how data is delivered, stored, and viewed. Offering a comprehensive explanation of the applications that provide, store, and use MBA data, Data Warehouse Designs provides you with the language and concepts needed to require and receive information that is relevant and actionable.
Data Warehouse Requirements Engineering: A Decision Based Approach
by Naveen Prakash Deepika PrakashAs the first to focus on the issue of Data Warehouse Requirements Engineering, this book introduces a model-driven requirements process used to identify requirements granules and incrementally develop data warehouse fragments. In addition, it presents an approach to the pair-wise integration of requirements granules for consolidating multiple data warehouse fragments. The process is systematic and does away with the fuzziness associated with existing techniques. Thus, consolidation is treated as a requirements engineering issue. The notion of a decision occupies a central position in the decision-based approach. On one hand, information relevant to a decision must be elicited from stakeholders; modeled; and transformed into multi-dimensional form. On the other, decisions themselves are to be obtained from decision applications. For the former, the authors introduce a suite of information elicitation techniques specific to data warehousing. This information is subsequently converted into multi-dimensional form. For the latter, not only are decisions obtained from decision applications for managing operational businesses, but also from applications for formulating business policies and for defining rules for enforcing policies, respectively. In this context, the book presents a broad range of models, tools and techniques. For readers from academia, the book identifies the scientific/technological problems it addresses and provides cogent arguments for the proposed solutions; for readers from industry, it presents an approach for ensuring that the product meets its requirements while ensuring low lead times in delivery.
Data Warehousing For Dummies
by Thomas C. HammergrenData warehousing is one of the hottest business topics, and there's more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition.Data is probably your company's most important asset, so your data warehouse should serve your needs. The fully updated Second Edition of Data Warehousing For Dummies helps you understand, develop, implement, and use data warehouses, and offers a sneak peek into their future. You'll learn to:Analyze top-down and bottom-up data warehouse designsUnderstand the structure and technologies of data warehouses, operational data stores, and data martsChoose your project team and apply best development practices to your data warehousing projectsImplement a data warehouse, step by step, and involve end-users in the processReview and upgrade existing data storage to make it serve your needsComprehend OLAP, column-wise databases, hardware assisted databases, and middlewareUse data mining intelligently and find what you needMake informed choices about consultants and data warehousing productsData Warehousing For Dummies, 2nd Edition also shows you how to involve users in the testing process and gain valuable feedback, what it takes to successfully manage a data warehouse project, and how to tell if your project is on track. You'll find it's the most useful source of data on the topic!
Data Warehousing Fundamentals for IT Professionals
by Paulraj PonniahCutting-edge content and guidance from a data warehousing expert--now expanded to reflect field trendsData warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of Data Warehousing Fundamentals, numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field.The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery.This practical Second Edition highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information.Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, Data Warehousing Fundamentals presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.
Data Warehousing and Analytics: Fueling the Data Engine (Data-Centric Systems and Applications)
by David Taniar Wenny RahayuThis textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge.The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics).This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.
Data Warehousing for Biomedical Informatics
by Richard E. BiehlData Warehousing for Biomedical Informatics is a step-by-step how-to guide for designing and building an enterprise-wide data warehouse across a biomedical or healthcare institution, using a four-iteration lifecycle and standardized design pattern. It enables you to quickly implement a fully-scalable generic data architecture that supports your organization's clinical, operational, administrative, financial, and research data. By following the guidelines in this book, you will be able to successfully progress through the Alpha, Beta, and Gamma versions, plus fully implement your first production release in about a year.
Data Warehousing with SAP BW7 BI in SAP Netweaver 2004s
by Christian Mehrwald Sabine MorlockBI in SAP NetWeaver 2004s is the official abbreviation for the successor of the Business Information Warehouse (BW) which has been completely revised by SAP with its latest release. Core elements of this comprehensive suite for decision making applications are functions for extraction, transformation and data management. With this new release, these functions aim more heavily at company-wide data warehousing. The book focuses on these core tasks of SAP BW and gives well-founded insights into the system architecture. As practical handbook and well-structured reference book, the book is for SAP consultants and IT staff that are responsible for or planning a BW-based data warehouse implementation. Apart from system architecture, the book focuses on detailed descriptions of data management (data models and Analytical Engine) as well as the Staging Engine which have been completely revised and deal with new data transfer process technology. The design of the controlled operations has been substantially expanded and besides a comprehensive description of automization techniques by using process chains, regular maintenance and administration tasks are also discussed (model trimming, technical validation). The book emphasizes a comprehensive view on aspects to manageability and system performance which are discussed in individual chapters but also implicitly in all other ranges of topics.
Data Wise, Revised and Expanded Edition: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning
by Kathryn Parker BoudettData Wise, Third Edition will be available in August 2025 ***Data Wise, Revised and Expanded Edition presents a continuous, sustainable process that allows school leaders to harness classroom metrics to inform educational practice. At its core, the Data Wise method fosters effective collaboration among educators, enabling teams to study a wide range of evidence and then use what they learn to enrich school culture and climate and ensure that each student thrives. Kathryn Parker Boudett, Elizabeth A. City, and Richard J. Murnane offer clear guidance for enacting all stages of the Data Wise improvement process and for integrating data inquiry into long-term institutional practice. They begin with actions that lay the groundwork for collaboration: advancing assessment literacy among contributors, building productive professional learning communities, and identifying targets for change. They continue with advice on evaluating progress and boosting accountability. Throughout the book, the authors recommend practical tools and proven practices, such as the plus/delta protocol and the ACE Habits of Mind (focusing on action, collaboration, and evidence), that help school leaders optimize the quality of meetings, especially those in which educators analyze data. They also provide tips for how to make best use of developments in education and technology, from Common Core State Standards to online collaboration tools. The field-tested strategies of the Data Wise improvement process have been used to great success in schools around the world, showing that careful examination of test scores, classroom data, and other educational evaluations can become a catalyst for important schoolwide conversations and transformations.