This book is associated with the 'Data Literacy Level 1' on-demand online course: https://dataliteracy.com/courses/data-literacy-level-1 For most of us, it's rare to go a full day without coming across data in the form of a chart, map or dashboard. Graphical displays of data are all around us, from performance indicators at work to election trackers on the news to traffic maps on the road. But few of us have received training or instruction in how to actually read and interpret them. How many times have we been misled simply because we aren't aware of the pitfalls to avoid when interpreting data visualizations. Learning to See Data will teach you the different ways that data can be encoded in graphical form, and it will give you a deeper understanding of the way our human visual system interprets these encodings. You will also learn about the most common chart types, and the situations in which they are most appropriate. From basic bar charts to overused pie charts to helpful maps and many more, a wide array of chart types are covered in detail, and conventions, pitfalls, strengths and weaknesses of each of them are revealed. This book will help you develop fluency in the interpretation of charts, an ability that we all need to hone and perfect if we are to make meaningful contributions in the professional, public and personal arenas of life. The principles covered in it also serve as a critical background for anyone looking to create charts that others will be able to understand. "This book is clear and evocative, thorough and thoughtful, and remarkably readable: a marvelous launchpad into the world of data." –Tamara Munzner, Professor, University of British Columbia Computer Science "Everyone of us needs good data literacy skills to survive in the modern world. Without them, it's hard to succeed at work, or survive the onslaught of information (and misinformation) across all our media. Ben's book provides the necessary building blocks for a strong foundation. From that foundation, Ben's approach will inspire you to own the process of developing your skills further." –Andy Cotgreave, Technical Evangelism Director, Tableau

Lean production is the gold standard in production systems, but has proven famously difficult to implement in North America. Mass production relies on large inventories, uses "push" processes and struggles with long lead times. Moving towards a system that eliminates muda ("waste") caused by overproduction, while challenging, proves necessary for improved efficiency. Often overlooked, value stream mapping is the essential planning stage for any Lean transformation. In Mike Rother and John Shook's essential guide, you follow the value stream mapping undertaken for Acme Stamping, for its current and future state. Fully illustrated and well-organized, Learning to See is a must-see for the value stream manager.

This book helps you make sense of the data your school district collects, including state student achievement results as well as other qualitative and quantitative data. Easy-to-use templates, tools, and examples are available on the accompanying downloadable resources.

Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices. Provides a practical guide for non-experts with a focus on business users Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting Uses a practical tone and integrates multiple topics in a coherent framework Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R Shows readers how to visualize results in static and interactive reports Supplementary materials includes PDF slides based on the book’s content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.

Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not learned for their own sake; rather, they are used t

"Gathering data and using it to inform instruction is a requirement for many schools, yet educators are not necessarily formally trained in how to do it. This book helps bridge the gap between classroom practice and the principles of educational psychology. Teachers will find cutting-edge advances in research and theory on human learning and teaching in an easily understood and transferable format. The text's integrated model shows teachers, school leaders, and district administrators how to establish a data culture and transform quantitative and qualitative data into actionable knowledge based on: assessment; statistics; instructional and differentiated psychology; classroom management."--Publisher's description.

An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.