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Show Me the Data! by RinaMarie S. Leon-Guerrero,Chris Matsumoto,Jaime Martin Pdf
Data collection is one of the critical elements of an effective, efficient, and accountable program that supports student development and learning. However, many intervention teams struggle to develop reasonable and sustainable methods of gathering information about student performance and progress. Now comes SHOW ME THE DATA! This must-have resource consists of a brief discussion on data collection accompanied by a CD containing a comprehensive compilation of data sheets to help make that once-dreaded task easier and simpler.
This lined notebook makes the perfect gift for data nerds, scientists, analysts, engineers, mathematicians, STEM students and programmers. 6x9 inches 110 pages Lined White paper Matte-finished cover
Show Me the Money: Tracking Consumer Spending with Daily Card Transaction Data During the Pandemic by Mr. Serhan Cevik Pdf
The COVID-19 pandemic has been an unprecedented shock to economic activity with abrupt and unexpected changes in household consumption behavior. This paper investigates how the spread of the pandemic and government interventions have affected consumer spending using daily card transaction data in the Baltics. The analysis shows significant effects on the amount and composition of debit and credit card transactions. First, the number of new COVID-19 infections or deaths has a strongly negative effect. Second, while public health measures designed to contain the spread of the pandemic has a negative effect, economic support measures designed to assist businesses and households have a stimulative effect. Third, there is heterogeneity across spending categories, but the drop is mostly concentrated in sectors that are restricted by lockdowns and the risk of infection. Fourth, the impact of government interventions, especially in terms of stimulating consumer spending, appears to be more pronounced on goods than services.
R for Data Science by Hadley Wickham,Garrett Grolemund Pdf
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Wake Me Up When the Data Is Over by Lori L. Silverman Pdf
This book includes real-life examples from over 70 respected organizations, small and large, representing a multitude of industries using stories to drive results. Leaders from organizations such as Microsoft, Lands’ End, Verizon, U.S. Air Force, and World Vision demonstrate the strong positive influence stories can have. No abstract theories or platitudes are conveyed here. The book spells out how Kevin Roberts, CEO worldwide of Saatchi & Saatchi, achieved sustained sales growth after several mergers and downsizings caused the organization to fall on hard times. It also shows how Erik Shaw, president and CEO of FivePoint Federal Credit Union, overcame resistance to an organizational name change, resulting in membership growth exceeding the national average.
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.
Creating Data Stories with Tableau Public by Ashley Ohmann,Matt Floyd Pdf
Illustrate your data in a more interactive and interesting way using Tableau Public About This Book Learn the basics of creating visualizations with Tableau using this concise reference book Understand how to join and aggregate your data sources using Tableau It is a step by step guide that uses examples to help you understand the key concepts and feature of Tableau Public Who This Book Is For This book is targeted at investigative journalists and bloggers with an interest in making rich and interactive data visualizations. Intermediate Tableau Public users and organizations can also use this book as a reference guide and teaching aid. Members of the media team, such as data specialists, web developers, editors, producers, and managers can also benefit from an understanding of the structure and challenges of writing an interactive and interesting data visualization using Tableau Public. What You Will Learn Connect to various data sources and understand what data is appropriate for Tableau Public Understand chart types and when to use specific chart types with different types of data Join and aggregate data for use in Tableau Public data stories Discover features of Tableau Public, from basic to advanced Involve calculations in Tableau Public Build geographic maps to bring context to data Create dashboards from one or more separate data visualizations Create filters and actions to allow greater interactivity to Tableau Public visualizations and dashboards Publish and embed Tableau visualizations and dashboards in articles In Detail Tableau Public is a very useful tool in anyone's data reporting toolbox that allows authors to add an interactive data element to any article. It allows investigative journalists and bloggers to tell a “data story”, allowing others to explore your data visualization. The relative ease of Tableau Public visualization creation allows data stories to be developed rapidly. It allows readers to explore data associations in multiple-sourced public data, and uses state-of-the-art dashboard and chart graphics to immerse the users in an interactive experience. This book offers investigative journalists, bloggers, and other data story tellers a rich discussion of visualization creation topics, features, and functions. This book allows data story tellers to quickly gain confidence in understanding and expanding their visualization-creation knowledge, and allows them to quickly create interesting, interactive data visualizations to bring a richness and vibrancy to complex articles. The book takes you from basic concepts in visualization creation, like connecting to data sources, cleansing data, chart types, common functions, map creation, and publishing to the Web, to more advanced functions. It is a great overview and reference guide for beginner to intermediate Tableau Public data story tellers, and covers creation of Tableau Public visualizations of varying complexities. Style and approach This book is a crisp, systematic, and tutorial-styled guide to building interactive Tableau visualizations.
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!
Fundamentals of Data Visualization by Claus O. Wilke Pdf
Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story
Interpreting language-learning data by Amanda Edmonds,Pascale Leclercq,Aarnes Gudmestad Pdf
This book provides a forum for methodological discussions emanating from researchers engaged in studying how individuals acquire an additional language. Whereas publications in the field of second language acquisition generally report on empirical studies with relatively little space dedicated to questions of method, the current book gave authors the opportunity to more fully develop a discussion piece around a methodological issue in connection with the interpretation of language-learning data. The result is a set of seven thought-provoking contributions from researchers with diverse interests. Three main topics are addressed in these chapters: the role of native-speaker norms in second-language analyses, the impact of epistemological stance on experimental design and/or data interpretation, and the challenges of transcription and annotation of language-learning data, with a focus on data ambiguity. Authors expand on these crucial issues, reflect on best practices, and provide in many instances concrete examples of the impact they have on data interpretation.
You receive an e-mail. It contains an offer for a complete personal computer system. It seems like the retailer read your mind since you were exploring computers on their web site just a few hours prior…. As you drive to the store to buy the computer bundle, you get an offer for a discounted coffee from the coffee shop you are getting ready to drive past. It says that since you’re in the area, you can get 10% off if you stop by in the next 20 minutes…. As you drink your coffee, you receive an apology from the manufacturer of a product that you complained about yesterday on your Facebook page, as well as on the company’s web site…. Finally, once you get back home, you receive notice of a special armor upgrade available for purchase in your favorite online video game. It is just what is needed to get past some spots you’ve been struggling with…. Sound crazy? Are these things that can only happen in the distant future? No. All of these scenarios are possible today! Big data. Advanced analytics. Big data analytics. It seems you can’t escape such terms today. Everywhere you turn people are discussing, writing about, and promoting big data and advanced analytics. Well, you can now add this book to the discussion. What is real and what is hype? Such attention can lead one to the suspicion that perhaps the analysis of big data is something that is more hype than substance. While there has been a lot of hype over the past few years, the reality is that we are in a transformative era in terms of analytic capabilities and the leveraging of massive amounts of data. If you take the time to cut through the sometimes-over-zealous hype present in the media, you’ll find something very real and very powerful underneath it. With big data, the hype is driven by genuine excitement and anticipation of the business and consumer benefits that analyzing it will yield over time. Big data is the next wave of new data sources that will drive the next wave of analytic innovation in business, government, and academia. These innovations have the potential to radically change how organizations view their business. The analysis that big data enables will lead to decisions that are more informed and, in some cases, different from what they are today. It will yield insights that many can only dream about today. As you’ll see, there are many consistencies with the requirements to tame big data and what has always been needed to tame new data sources. However, the additional scale of big data necessitates utilizing the newest tools, technologies, methods, and processes. The old way of approaching analysis just won’t work. It is time to evolve the world of advanced analytics to the next level. That’s what this book is about. Taming the Big Data Tidal Wave isn’t just the title of this book, but rather an activity that will determine which businesses win and which lose in the next decade. By preparing and taking the initiative, organizations can ride the big data tidal wave to success rather than being pummeled underneath the crushing surf. What do you need to know and how do you prepare in order to start taming big data and generating exciting new analytics from it? Sit back, get comfortable, and prepare to find out!
Navigating Sustainability Data by Sherry Madera Pdf
With sustainability now a fundamental strategic pillar for organizations, ESG metrics are vital for decision making, yet this data is complex and ever evolving. Navigating Sustainability Data provides a focused guide for leaders and executives who want to understand how sustainability impacts a company's future and how they can use data as a key tool to facilitate growth, access to capital and meet regulatory requirements. It explores what data is required to make decisions that directly affect the company's valuation and helps leadership teams decide what data points they need to be preparing for their boards to support the organization's financial, strategic and reputational future. Showing how leaders can move from measuring data to managing future organizational risks and rewards, the book explores how to use the right data for the appropriate situation to drive efficient sustainable business decisions. It explains ESG data in plain English and highlights what data is relevant to leaders; what investors consider as essential data; and how regulation, disclosure and transparency are on a rapidly changing journey. Navigating Sustainability Data holds the information you need to avoid greenwashing, to shape your role in the sustainability landscape, and to be a leader for the future.
Big Data by Balamurugan Balusamy,Nandhini Abirami R,Seifedine Kadry,Amir H. Gandomi Pdf
Learn Big Data from the ground up with this complete and up-to-date resource from leaders in the field Big Data: Concepts, Technology, and Architecture delivers a comprehensive treatment of Big Data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students. Beginning with a fulsome overview of what we mean when we say, “Big Data,” the book moves on to discuss every stage of the lifecycle of Big Data. You’ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like SQL, data processing, data analytics, machine learning, and data mining. You’ll also discover how specific technologies like Apache Hadoop, SQOOP, and Flume work. Big Data also covers the central topic of big data visualization with Tableau, and you’ll learn how to create scatter plots, histograms, bar, line, and pie charts with that software. Accessibly organized, Big Data includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. Some of those concepts include: The common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns Relational and non-relational databases, like RDBMS, NoSQL, and NewSQL databases Virtualizing Big Data through encapsulation, partitioning, and isolating, as well as big data server virtualization Apache software, including Hadoop, Cassandra, Avro, Pig, Mahout, Oozie, and Hive The Big Data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization Perfect for data scientists, data engineers, and database managers, Big Data also belongs on the bookshelves of business intelligence analysts who are required to make decisions based on large volumes of information. Executives and managers who lead teams responsible for keeping or understanding large datasets will also benefit from this book.