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Whether you're a startup founder trying to disrupt an industry or an entrepreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without. Understand Lean Startup, analytics fundamentals, and the data-driven mindset Look at six sample business models and how they map to new ventures of all sizes Find the One Metric That Matters to you Learn how to draw a line in the sand, so you'll know it's time to move forward Apply Lean Analytics principles to large enterprises and established products
Data Analysis for Continuous School Improvement provides a new definition of school improvement, away from a singular focus on compliance, toward a true commitment to excellence. This book is a call to action. It is about inspiring schools and school districts to commit to continuous school improvement by providing a framework that will result in improving teaching for every teacher and learning for every student through the comprehensive use of data. A culmination of over 30 years of doing the hard work in schools and districts both nationally and internationally, Data Analysis for Continuous School Improvement shares new, evidence-based learnings about how to analyze, report, communicate, and use multiple measures of data. The updated edition provides a wealth of tools, protocols, timelines, examples, and strategies that will help schools and districts become genuine learning organizations.
Beginning and experienced programmers will use this comprehensive guide to persistent memory programming. You will understand how persistent memory brings together several new software/hardware requirements, and offers great promise for better performance and faster application startup times—a huge leap forward in byte-addressable capacity compared with current DRAM offerings. This revolutionary new technology gives applications significant performance and capacity improvements over existing technologies. It requires a new way of thinking and developing, which makes this highly disruptive to the IT/computing industry. The full spectrum of industry sectors that will benefit from this technology include, but are not limited to, in-memory and traditional databases, AI, analytics, HPC, virtualization, and big data. Programming Persistent Memory describes the technology and why it is exciting the industry. It covers the operating system and hardware requirements as well as how to create development environments using emulated or real persistent memory hardware. The book explains fundamental concepts; provides an introduction to persistent memory programming APIs for C, C++, JavaScript, and other languages; discusses RMDA with persistent memory; reviews security features; and presents many examples. Source code and examples that you can run on your own systems are included. What You’ll Learn Understand what persistent memory is, what it does, and the value it brings to the industry Become familiar with the operating system and hardware requirements to use persistent memory Know the fundamentals of persistent memory programming: why it is different from current programming methods, and what developers need to keep in mind when programming for persistence Look at persistent memory application development by example using the Persistent Memory Development Kit (PMDK)Design and optimize data structures for persistent memoryStudy how real-world applications are modified to leverage persistent memoryUtilize the tools available for persistent memory programming, application performance profiling, and debugging Who This Book Is For C, C++, Java, and Python developers, but will also be useful to software, cloud, and hardware architects across a broad spectrum of sectors, including cloud service providers, independent software vendors, high performance compute, artificial intelligence, data analytics, big data, etc.
How Product Owners and Business Analysts can maximize the value delivered to stakeholders by integrating BA competencies with agile methodologies "This book will become a staple reference that both product owners and business analysis practitioners should have by their side." -- From the Foreword by Alain Arseneault, former IIBA Acting President & CEO "[This book] is well organized in bite-sized chunks and structured for ready access to the essential concepts, terms, and practices that can help any agile team be more successful." -- Karl Wiegers The Agile Guide to Business Analysis and Planning provides practical guidance for eliminating unnecessary errors and delays in agile product development through effective planning, backlog refinement and acceptance criteria specification ---with hard-to-find advice on how and when to analyze the context for complex changes within an agile approach---including when to use Journey Maps, Value Stream Mapping, Personas, Story Maps, BPMN, Use Cases and other UML models. Renowned author and consultant Howard Podeswa teaches best practices drawn from agile and agile-adjacent frameworks, including ATDD, BDD, DevOps, CI/CD, Kanban, Scrum, SAFe, XP, Lean Thinking, Lean Startup, Circumstance-Based Market Segmentation, and theories of disruptive innovation. He offers a comprehensive agile roadmap for analyzing customer needs and planning product development, including discussion of legacy business analysis tools that still offer immense value to agile teams. Using a running case study, Podeswa walks through the full agile product lifecycle, from visioning through release and continuous value delivery. You learn how to carry out agile analysis and planning responsibilities more effectively, using tools such as Kano analysis, minimum viable products (MVPs), minimum marketable features (MMFs), story maps, product roadmaps, customer journey mapping, value stream mapping, spikes, and the definition of ready (DoR). Podeswa presents each technique in context: what you need to know and when to apply each tool. Read this book to Master principles, frameworks, concepts, and practices of agile analysis and planning in order to maximize value delivery throughout the product's lifecycle Explore planning and analysis for short-term, long-term, and scaled agile initiatives using MVPs and data-informed learning to test hypotheses and find high-value features Split features into MMFs and small stories that deliver significant value and enable quick wins Refine, estimate, and specify features, stories, and their acceptance criteria, following ATDD/BDD guidance Address the unique analysis and planning challenges of scaled agile organizations Implement 13 practices for optimizing enterprise agility Supported by 175+ tools, techniques, examples, diagrams, templates, checklists, and other job aids, this book is a complete toolkit for every practitioner. Whatever your role, you'll find indispensable guidance on agile planning and analysis responsibilities so you can help your organization respond more nimbly to a fast-changing environment. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Many researchers jump straight from data collection to data analysis without realizing how analyses and hypothesis tests can go profoundly wrong without clean data. This book provides a clear, step-by-step process of examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are research-based and will motivate change in practice by empirically demonstrating, for each topic, the benefits of following best practices and the potential consequences of not following these guidelines. If your goal is to do the best research you can do, draw conclusions that are most likely to be accurate representations of the population(s) you wish to speak about, and report results that are most likely to be replicated by other researchers, then this basic guidebook will be indispensible.
The laboratory examination of a lubricant's characteristics, suspended impurities, and wear debris is known as oil analysis (OA). OA is carried out as part of regular predictive maintenance to deliver precise and useful data on lubricant and machine condition. Trends can be found by following the findings of oil analysis samples over the course of a certain machine. These trends can help avoid expensive repairs. Tribology is the study of wear in machinery. Tribologists frequently perform or interpret results from oil analyses. Oil analysis is a long-term program that, where relevant, can eventually be more predictive than any of the other technologies. It can take years for a plant's oil program to reach this level of sophistication and effectiveness. This book includes what all practitioners need to know to build an oil analysis program for their machine inspection. This book includes three real case studies and numerous industrial examples to improve machine reliability and enhance the condition monitoring program.
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