It's great to have theory and evidenced-based research at your disposal, but what will you do when somebody asks you to make your dashboard 'cooler' by adding packed bubbles and donut charts?The expert authors have a combined 30-plus years of hands-on experience helping people in hundreds of organizations build effective visualizations. They have fought many 'best practices' battles and having endured bring an uncommon empathy to help you, the reader of this book, survive and thrive in the data visualization world.A well-designed dashboard can point out risks, opportunities, and more; but common challenges and misconceptions can make your dashboard useless at best, and misleading at worst. The Big Book of Dashboards gives you the tools, guidance, and models you need to produce great dashboards that inform, enlighten, and engage.

    We start off easy, defining some of the tech lingo you may have heard before, but didn’t fully understand. From there, each lesson introduces something new, slowly building up to the point where you are confident about what an API is and, for the brave, could actually take a stab at using one.

    Whether you're a rocket scientist or a poet, whether you're ten years old or ninety, we want to make it possible for you to build great projects using computers and electronics.The parts in this kit and the projects explained here form the skeleton of your projects. Arduino can make your projects responsive. It's up to you to make them beautiful.

    Chapter 1: Introduction to Production and Operations Management Chapter 2: Strategic Operations Management Chapter 3: Production Processes, Manufacturing and Service Operations Chapter 4: Design of Production Systems Chapter 5: Manufacturing Technology Chapter 6: Long-Range Capacity Planning Chapter 7: Facility Location Chapter 8: Facility Layout Chapter 9: Design of Work Systems Chapter 10: Production/Operations Planning and Control Chapter 10: Aggregate Planning and Master Production Scheduling Chapter 11: Resource Requirement Planning Chapter 13: Shop Floor Planning and Control Chapter 14: Quality Management Chapter 15: Maintenance Management Chapter 16: Introduction to Materials Management Chapter 17: Inventory Management Chapter 18: JustlnTime Systems Chapter 19: Logistics and Supply Chain Management Index 557564

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    

    This practical book takes experienced web developers through the new discipline of frontend architecture, including the latest tools, standards, and best practices that have elevated frontend web development to an entirely new level.Using real-world examples, case studies, and practical tips and tricks throughout, author Micah Godbolt introduces you to the four pillars of frontend architecture. He also provides compelling arguments for developers who want to embrace the mantle of frontend architect and fight to make it a first-class citizen in their next project.The four pillars include:Code: how to approach the HTML, CSS, and JavaScript of a design systemProcess: tools and processes for creating an efficient and error-proof workflowTesting: creating a stable foundation on which to build your siteDocumentation: tools for writing documentation while the work is in progress

    

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    The test is multiple choice and the other study guides take you through the 452 possible questions including all four answers for each question. But, three of the four answers are WRONG! You are reading 1,356 wrong answers and that is both confusing and frustrating. The Easy Way is a concise explanation of every question and answer focusing on the right answers. There are also hints and cheats help you remember the correct answer. Which would you rather study: right answers or over 250 pages with three-quarters of the answers wrong? Instructors: This book is perfect for review or weekend courses. Have the students read the narrative before class, then go over the concepts with them rather than slogging through all those wrong answers. You'll be done in no time and the students will be fully prepared to take their tests.

    Learn how to leverage the Django web framework to its full potential in this advanced tutorial and reference. Endorsed by Django, Pro Django more or less picks up where The Definitive Guide to Django left off and examines in greater detail the unusual and complex problems that Python web application developers can face and how to solve them.Provides in-depth information about advanced tools and techniques available in every Django installation Runs the gamut from the theory of Django's internal operations to actual code that solves real-world problems for high-volume environments Goes above and beyond other books, leaving the basics behind Shows how Django can do things even its core developers never dreamed possible

    Routing TCP/IP, Volume II, provides you with the expertise necessary to understand and implement Border Gateway Protocol Version 4 (BGP-4), multicast routing, Network Address Translation (NAT), IPv6, and effective router management techniques. Jeff Doyle's practical approach, easy-to-read format, and comprehensive topic coverage make this book an instant classic and a must-have addition to any network professional's library. Routing TCP/IP, Volume II, expands upon the central theme of Volume I: scalability and management of network growth. Volume II moves beyond the interior gateway protocols covered in Volume I to examine both inter-autonomous system routing and more exotic routing issues such as multicasting and IPv6. This second volume follows the same informational structure used effectively in Volume I: discussing the topic fundamentals, following up with a series of configuration examples designed to show the concept in a real-world environment, and relying on tested troubleshooting measures to resolve any problems that might arise. This book helps you accomplish more than earning the highly valued CCIE number after your name; it also helps you develop the knowledge and skills that are essential to perform your job at an expert level. Whether you are pursuing CCIE certification, need to review for your CCIE recertification exam, or are just looking for expert-level advice on advanced routing issues, Routing TCP/IP, Volume II, helps you understand foundation concepts and apply best practice techniques for effective network growth and management.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    

    If you're working with a relational database--whether you're writing applications, performing administrative tasks, or generating reports--you need to know how to interact with your data. Even if you are using a tool that generates SQL for you, such as a reporting tool, there may still be cases where you need to bypass the automatic generation feature and write your own SQL statements.To help you attain this fundamental SQL knowledge, look to "Learning SQL," an introductory guide to SQL, designed primarily for developers just cutting their teeth on the language."Learning SQL" moves you quickly through the basics and then on to some of the more commonly used advanced features. Among the topics discussed: The history of the computerized databaseSQL Data Statements--those used to create, manipulate, and retrieve data stored in your database; example statements include select, update, insert, and deleteSQL Schema Statements--those used to create database objects, such as tables, indexes, and constraintsHow data sets can interact with queriesThe importance of subqueriesData conversion and manipulation via SQL's built-in functionsHow conditional logic can be used in Data StatementsBest of all, "Learning SQL" talks to you in a real-world manner, discussing various platform differences that you're likely to encounter and offering a series of chapter exercises that walk you through the learning process. Whenever possible, the book sticks to the features included in the ANSI SQL standards. This means you'll be able to apply what you learn to any of several different databases; the book covers MySQL, Microsoft SQL Server, and Oracle Database, but the features and syntax should apply just as well (perhaps with some tweaking) to IBM DB2, Sybase Adaptive Server, and PostgreSQL.Put the power and flexibility of SQL to work. With "Learning SQL" you can master this important skill and know that the SQL statements you write are indeed correct.