Here's the ideal statistics book for teachers with no statistical background. Written in an informal style with easy-to-grasp examples, The Basics of Data Literacy teaches you how to help your students understand data. Then, in turn, they learn how to collect, summarize, and analyze statistics inside and outside the classroom. The books 10 succinct chapters provide an introduction to types of variables and data, ways to structure and interpret data tables, simple statistics, and survey basics from a student perspective. The appendices include hands-on activities tailored to middle and high school investigations. Because data are so central to many of the ideas in the Next Generation Science Standards, the ability to work with such information is an important science skill for both you and your students. This accessible book will help you get over feeling intimidated as your students learn to evaluate messy data on the Internet, in the news, and in future negotiations with car dealers and insurance agents.

The vast majority of people in the world today do not receive a formal education that adequately prepares them for the level of data literacy required of them in their careers and by their communities. As a result, many are being left behind by the transition to data-driven dialogues and decisions all around them, and they're seeking ways to break down the barriers that are preventing them from participating. Data Literacy Fundamentals covers foundational topics such as the overall goal of data, various ways of measuring and categorizing the world, five different forms of data analysis and when they apply, pros and cons related to how we display data in tabular or graphic form, and the way teams work together to convert data into insight.This book has been written for anyone who is just getting started with data and who wants to feel more confident in their understanding of what it is, what it isn't, and what it's used for. This invaluable resource will cure you of your "dataphobia", teach you the basic concepts of data, and set you on a path of learning that will ultimately result in fluency in the language of data.

In this follow up to Statistics Made Simple for School Leaders Carroll and Carroll have provided an updated, easy to comprehend, manual for practitioners. Now more than ever, educators are being held accountable by taxpayers, students, parents, government officials and the business community for supportable documentation of educational results. Data management has become everyone’s job and everyone’s concern. But the regression of data has exposed a raw nerve. The lack of comfort that many educators have in working with data poses a great challenge as school districts make the transition from a data rich to an information rich environment. How to Become Data Literate is the solution. Educators need the ability to formulate and answer questions using data as part of evidence-based thinking, selecting and using appropriate data tools, interpreting information from data, evaluating evidence-based differences, using data to solve real problems and communicating solutions. This book is intended to be a user-friendly, educator’s primer. It will leave the reader with the confident attitude that “I can do this." In the long run, it is intended to underscore the magnificence of data. Decisions based on excellent data produce meaningful action strategies that benefit students, parents, staff, and the community at large.

Accelerate your journey to smarter decision making by mastering the fundamentals of data literacy and developing the mindset to work confidently with data Key FeaturesGet a solid grasp of data literacy fundamentals to support your next steps in your careerLearn how to work with data and extract meaningful insights to take the right actionsApply your knowledge to real-world business intelligence projectsBook Description Data is more than a mere commodity in our digital world. It is the ebb and flow of our modern existence. Individuals, teams, and enterprises working with data can unlock a new realm of possibilities. And the resultant agility, growth, and inevitable success have one origin—data literacy. This comprehensive guide is written by two data literacy pioneers, each with a thorough footprint within the data and analytics commercial world and lectures at top universities in the US and the Netherlands. Complete with best practices, practical models, and real-world examples, Data Literacy in Practice will help you start making your data work for you by building your understanding of data literacy basics and accelerating your journey to independently uncovering insights. You'll learn the four-pillar model that underpins all data and analytics and explore concepts such as measuring data quality, setting up a pragmatic data management environment, choosing the right graphs for your readers, and questioning your insights. By the end of the book, you'll be equipped with a combination of skills and mindset as well as with tools and frameworks that will allow you to find insights and meaning within your data for data-informed decision making. What you will learnStart your data literacy journey with simple and actionable stepsApply the four-pillar model for organizations to transform data into insightsDiscover which skills you need to work confidently with dataVisualize data and create compelling visual data storiesMeasure, improve, and leverage your data to meet organizational goalsMaster the process of drawing insights, ask critical questions and action your insightsDiscover the right steps to take when you analyze insightsWho this book is for This book is for data analysts, data professionals, and data teams starting or wanting to accelerate their data literacy journey. If you're looking to develop the skills and mindset you need to work independently with data, as well as a solid knowledge base of the tools and frameworks, you'll find this book useful.

Data Literacy: How to Make Your Experiments Robust and Reproducible provides an overview of basic concepts and skills in handling data, which are common to diverse areas of science. Readers will get a good grasp of the steps involved in carrying out a scientific study and will understand some of the factors that make a study robust and reproducible.The book covers several major modules such as experimental design, data cleansing and preparation, statistical analysis, data management, and reporting. No specialized knowledge of statistics or computer programming is needed to fully understand the concepts presented. This book is a valuable source for biomedical and health sciences graduate students andresearchers, in general, who are interested in handling data to make their research reproducibleand more efficient. - Presents the content in an informal tone and with many examples taken from the daily routine at laboratories - Can be used for self-studying or as an optional book for more technical courses - Brings an interdisciplinary approach which may be applied across different areas of sciences

WINNER: International Book Awards 2023 - Business: Technology (1st edition) Not everyone needs to be a data scientist, but everyone should be data literate in a fast-moving world that brings new technological developments every day. It is not enough for a business to have the best data if those using it don't understand the right questions to ask or how to use the information generated to make decisions. Be Data Literate is the essential guide to developing the curiosity, creativity and critical thinking necessary to make anyone data literate, without needing a data science degree. With learnings to show development and real-world examples from industries implementing data literacy skills, this new edition includes groundbreaking insights into the future of data and AI. Be Data Literate is a practical guide to understanding the four levels of analytics, how to analyze data and the key steps to making smarter, data-informed decisions.

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.