Data Action Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Data Action book. This book definitely worth reading, it is an incredibly well-written.
How to use data as a tool for empowerment rather than oppression. Big data can be used for good, from tracking disease to exposing human rights violations, and for bad, implementing surveillance and control. Data inevitably represents the ideologies of those who control its use; data analytics and algorithms too often exclude women, the poor, and ethnic groups. In Data Action, Sarah Williams provides a guide for working with data in more ethical and responsible ways. Williams outlines a method that emphasizes collaboration among data scientists, policy experts, data designers, and the public. The approach generates policy debates, influences civic decisions, and informs design to help ensure that the voices of people represented in the data are neither marginalized nor left unheard.
How Teachers Can Turn Data into Action by Daniel R. Venables Pdf
" From state and Common Core tests to formative and summative assessments in the classroom, teachers are awash in data. Reviewing the data can be time-consuming, and the work of translating data into real change can seem overwhelming. Tapping more than 30 years' experience as an award-winning teacher and a trainer of PLC coaches, Daniel R. Venables, author of The Practice of Authentic PLCs: A Guide to Effective Teacher Teams, soothes the trepidation of even the biggest ""dataphobes"" in this essential resource. Field-tested and fine-tuned with professional learning communities around the United States, the Data Action Model is a teacher-friendly, systematic process for reviewing and responding to data in cycles of two to nine weeks. This powerful tool enables you and your teacher team to * Identify critical gaps in learning and corresponding instructional gaps; * Collaborate on solutions and develop a goal-driven action plan; and * Evaluate the plan's effectiveness after implementation and determine the next course of action. With easy-to-use templates and protocols to focus and deepen data conversations, this indispensable guide delineates exactly what should be accomplished in each team meeting to translate data into practice. In the modern sea of data, this book is your life preserver! "
This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.
Summary R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Business pros and researchers thrive on data, and R speaks the language of data analysis. R is a powerful programming language for statistical computing. Unlike general-purpose tools, R provides thousands of modules for solving just about any data-crunching or presentation challenge you're likely to face. R runs on all important platforms and is used by thousands of major corporations and institutions worldwide. About the Book R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining, and dynamic report writing. What's Inside Complete R language tutorial Using R to manage, analyze, and visualize data Techniques for debugging programs and creating packages OOP in R Over 160 graphs About the Author Dr. Rob Kabacoff is a seasoned researcher and teacher who specializes in data analysis. He also maintains the popular Quick-R website at statmethods.net. Table of Contents PART 1 GETTING STARTED Introduction to R Creating a dataset Getting started with graphs Basic data management Advanced data management PART 2 BASIC METHODS Basic graphs Basic statistics PART 3 INTERMEDIATE METHODS Regression Analysis of variance Power analysis Intermediate graphs Resampling statistics and bootstrapping PART 4 ADVANCED METHODS Generalized linear models Principal components and factor analysis Time series Cluster analysis Classification Advanced methods for missing data PART 5 EXPANDING YOUR SKILLS Advanced graphics with ggplot2 Advanced programming Creating a package Creating dynamic reports Advanced graphics with the lattice package available online only from manning.com/kabacoff2
Data governance is broken. Its time we fix it. Why is data governance so ineffective? The truth is data governance programs arent designed for the way we run our data teams, they arent even designed for a modern organization at all. They were designed when reports still came through inter-office mail. The flow of data into, within, and out of todays organizations is a tsunami breaking through rigid data governance methods. Yet our programs still rely on that command and control approach. Have you ever tried to control a tsunami? Every organization that uses data knows that they need a data governance program. Data literacy efforts and legislation like GDPR have become the bellwethers for our governance functions. But we still sit in data governance meetings without enough people and too many questions to move things forward. Theres no agility to the program because we imply a degree of frailty to the data that doesnt exist. We continue to insist on archaic methods that bring no value to our organizations. Achieving deep insights from data cant happen without good governance practices. All indicators point to the need to create a resilient and responsive data governance function. Where we go from here, and how we achieve success in data governance requires a radically different way. The hard truth: its time to challenge everything we know about data governance. Laura Madsen shows you how to redefine governance for the modern age. With a casual, witty style Madsen taps on her decades of experience, shares interviews with other best-in-field experts and grounds her perspective in research. Witness where it all fell apart, challenge long-held beliefs, and commit to a fundamental shiftthat governance is not about stopping or preventing usage but about supporting the usage of data. Be able to bring back trust and value to our data governance functions, and learn the: People-driven approach to governance; Processes that support the tsunami of data; Cutting edge technology thats enabling data governance.
R in Action, Third Edition by Robert I. Kabacoff Pdf
R is the most powerful tool you can use for statistical analysis. This definitive guide smooths R’s steep learning curve with practical solutions and real-world applications for commercial environments. In R in Action, Third Edition you will learn how to: Set up and install R and RStudio Clean, manage, and analyze data with R Use the ggplot2 package for graphs and visualizations Solve data management problems using R functions Fit and interpret regression models Test hypotheses and estimate confidence Simplify complex multivariate data with principal components and exploratory factor analysis Make predictions using time series forecasting Create dynamic reports and stunning visualizations Techniques for debugging programs and creating packages R in Action, Third Edition makes learning R quick and easy. That’s why thousands of data scientists have chosen this guide to help them master the powerful language. Far from being a dry academic tome, every example you’ll encounter in this book is relevant to scientific and business developers, and helps you solve common data challenges. R expert Rob Kabacoff takes you on a crash course in statistics, from dealing with messy and incomplete data to creating stunning visualizations. This revised and expanded third edition contains fresh coverage of the new tidyverse approach to data analysis and R’s state-of-the-art graphing capabilities with the ggplot2 package. About the technology Used daily by data scientists, researchers, and quants of all types, R is the gold standard for statistical data analysis. This free and open source language includes packages for everything from advanced data visualization to deep learning. Instantly comfortable for mathematically minded users, R easily handles practical problems without forcing you to think like a software engineer. About the book R in Action, Third Edition teaches you how to do statistical analysis and data visualization using R and its popular tidyverse packages. In it, you’ll investigate real-world data challenges, including forecasting, data mining, and dynamic report writing. This revised third edition adds new coverage for graphing with ggplot2, along with examples for machine learning topics like clustering, classification, and time series analysis. What's inside Clean, manage, and analyze data Use the ggplot2 package for graphs and visualizations Techniques for debugging programs and creating packages A complete learning resource for R and tidyverse About the reader Requires basic math and statistics. No prior experience with R needed. About the author Dr. Robert I Kabacoff is a professor of quantitative analytics at Wesleyan University and a seasoned data scientist with more than 20 years of experience. Table of Contents PART 1 GETTING STARTED 1 Introduction to R 2 Creating a dataset 3 Basic data management 4 Getting started with graphs 5 Advanced data management PART 2 BASIC METHODS 6 Basic graphs 7 Basic statistics PART 3 INTERMEDIATE METHODS 8 Regression 9 Analysis of variance 10 Power analysis 11 Intermediate graphs 12 Resampling statistics and bootstrapping PART 4 ADVANCED METHODS 13 Generalized linear models 14 Principal components and factor analysis 15 Time series 16 Cluster analysis 17 Classification 18 Advanced methods for missing data PART 5 EXPANDING YOUR SKILLS 19 Advanced graphs 20 Advanced programming 21 Creating dynamic reports 22 Creating a package
Introduction to Research - E-Book by Elizabeth DePoy,Laura N. Gitlin Pdf
Bridge the gap between research and practice with Introduction to Research: Understanding and Applying Multiple Strategies, 5th Edition. This easy-to-read edition covers all the major research design strategies: qualitative, quantitative, naturalistic, experimental-type, and mixed method. And with the text’s up-to-date research information and references, you will have a solid foundation from which to critique and understand research designs and their applications to healthcare and human service settings. Case examples provide real-life snapshots of what it is like to participate in different types of research processes, identify research dilemmas relevant to chapter subjects, and alert you to problems you might encounter. Authors make the topics more accessible, so research becomes more relevant - and topics come to life. Covers experimental-type, naturalistic, and mixed method design strategies to improve your ability to compare, contrast, and integrate different methods. Presents complex information clearly in a highly readable, and easy-to-understand, manner. Includes detailed discussions of qualitative and quantitative methodologies, a unique and balanced focus that makes this text more comprehensive than others in its field. NEW! Up-to-date research methods, strategies, and references, like digital sources, visual methods, and geographical analysis, give you the latest information on research in diverse areas of health and human services.
Data Feminism by Catherine D'Ignazio,Lauren F. Klein Pdf
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.
Storytelling with Data by Cole Nussbaumer Knaflic Pdf
Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!
Master the art and science of data storytelling—with frameworks and techniques to help you craft compelling stories with data. The ability to effectively communicate with data is no longer a luxury in today’s economy; it is a necessity. Transforming data into visual communication is only one part of the picture. It is equally important to engage your audience with a narrative—to tell a story with the numbers. Effective Data Storytelling will teach you the essential skills necessary to communicate your insights through persuasive and memorable data stories. Narratives are more powerful than raw statistics, more enduring than pretty charts. When done correctly, data stories can influence decisions and drive change. Most other books focus only on data visualization while neglecting the powerful narrative and psychological aspects of telling stories with data. Author Brent Dykes shows you how to take the three central elements of data storytelling—data, narrative, and visuals—and combine them for maximum effectiveness. Taking a comprehensive look at all the elements of data storytelling, this unique book will enable you to: Transform your insights and data visualizations into appealing, impactful data stories Learn the fundamental elements of a data story and key audience drivers Understand the differences between how the brain processes facts and narrative Structure your findings as a data narrative, using a four-step storyboarding process Incorporate the seven essential principles of better visual storytelling into your work Avoid common data storytelling mistakes by learning from historical and modern examples Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals is a must-have resource for anyone who communicates regularly with data, including business professionals, analysts, marketers, salespeople, financial managers, and educators.
The ASP.NET MVC 5 Framework is the latest evolution of Microsoft’s ASP.NET web platform. It provides a high-productivity programming model that promotes cleaner code architecture, test-driven development, and powerful extensibility, combined with all the benefits of ASP.NET. ASP.NET MVC 5 contains a number of advances over previous versions, including the ability to define routes using C# attributes and the ability to override filters. The user experience of building MVC applications has also been substantially improved. The new, more tightly integrated, Visual Studio 2013 IDE has been created specifically with MVC application development in mind and provides a full suite of tools to improve development times and assist in reporting, debugging and deploying your code. The popular Bootstrap JavaScript library has also now been included natively within MVC 5 providing you, the developer, with a wider range of multi-platform CSS and HTML5 options than ever before without the penalty of having to load-in third party libraries.
Schools Can Change by Dale W. Lick,Karl H. Clauset,Carlene U. Murphy Pdf
Build a dynamic system for change! From NCLB to Common Core standards, we are inundated with directives for improving our schools. How can we really create lasting change? By applying the Change Creation system! Learning community pioneers Dale Lick, Karl Clauset, and Carlene Murphy lead teachers, principals, and schools in this dynamic approach to school improvement. With a free, comprehensive online collection of practical resources, this book shows you how to: Develop the right vision, relationships, and culture to create and sustain change Model learning-inquiry cycles for action teams for success Build loyalty, trust, and responsibility within your teams and across the school
Data Protection Around the World by Elif Kiesow Cortez Pdf
This book provides a snapshot of privacy laws and practices from a varied set of jurisdictions in order to offer guidance on national and international contemporary issues regarding the processing of personal data and serves as an up-to-date resource on the applications and practice-relevant examples of data protection laws in different countries. Privacy violations emerging at an ever-increasing rate, due to evolving technology and new lifestyles linked to an intensified online presence of ever more individuals, required the design of a novel data protection and privacy regulation. The EU General Data Protection Regulation (GDPR) stands as an example of a regulatory response to these demands. The authors included in this book offer an in-depth analysis of the national data protection legislation of various countries across different continents, not only including country-specific details but also comparing the idiosyncratic characteristics of these national privacy laws to the GDPR. Valuable comparative information on data protection regulations around the world is thus provided in one concise volume. Due to the variety of jurisdictions covered and the practical examples focused on, both academics and legal practitioners will find this book especially useful, while for compliance practitioners it can serve as a guide regarding transnational data transfers. Elif Kiesow Cortez is Senior Lecturer at the International and European Law Program at The Hague University of Applied Sciences in The Netherlands.
Propose a new scalable data architecture paradigm, Data Lakehouse, that addresses the limitations of current data architecture patterns Key FeaturesUnderstand how data is ingested, stored, served, governed, and secured for enabling data analyticsExplore a practical way to implement Data Lakehouse using cloud computing platforms like AzureCombine multiple architectural patterns based on an organization's needs and maturity levelBook Description The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success. The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application. By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner. What you will learnUnderstand the evolution of the Data Architecture patterns for analyticsBecome well versed in the Data Lakehouse pattern and how it enables data analyticsFocus on methods to ingest, process, store, and govern data in a Data Lakehouse architectureLearn techniques to serve data and perform analytics in a Data Lakehouse architectureCover methods to secure the data in a Data Lakehouse architectureImplement Data Lakehouse in a cloud computing platform such as AzureCombine Data Lakehouse in a macro-architecture pattern such as Data MeshWho this book is for This book is for data architects, big data engineers, data strategists and practitioners, data stewards, and cloud computing practitioners looking to become well-versed with modern data architecture patterns to enable large-scale analytics. Basic knowledge of data architecture and familiarity with data warehousing concepts are required.
