User research is global – yet despite its pervasiveness, practitioners are not all well equipped to work globally. What may have worked in Nigeria may not be accepted in Russia, may be done differently in Brazil, may partly work in China, and may completely fail in Kuwait. And what often goes less noticed, but can be equally vexing are technical, logistical and planning issues such as hiring qualified translators, payment procedures, travel issues, setting up facilities and finding test participants.The Handbook of Global User Research is the first book to focus on global user research. The book collects insight from UX professionals from nine countries and, following a typical project timeline, presents practical insights into the preparation, fieldwork, analysis and reporting, and overall project management for global user research projects. Any user experience professional that works on global projects -- including those new to the field, UX veterans who need information on this expanding aspect of user research, and students -- will need this book to do their job effectively. - Presents the definitive collection of hard won lessons from user research professionals around the world - Includes real-world examples of global user research challenges and provides approaches to these issues - Contains anecdotes and hard-won from the field that illustrate actionable tactics for practitioners

High Growth Handbook is the playbook for growing your startup into a global brand. Global technology executive, serial entrepreneur, and angel investor Elad Gil has worked with high-growth tech companies including Airbnb, Twitter, Google, Stripe, and Square as they’ve grown from small companies into global enterprises. Across all of these breakout companies, Gil has identified a set of common patterns and created an accessible playbook for scaling high-growth startups, which he has now codified in High Growth Handbook. In this definitive guide, Gil covers key topics, including: · The role of the CEO · Managing a board · Recruiting and overseeing an executive team · Mergers and acquisitions · Initial public offerings · Late-stage funding. Informed by interviews with some of the biggest names in Silicon Valley, including Reid Hoffman (LinkedIn), Marc Andreessen (Andreessen Horowitz), and Aaron Levie (Box), High Growth Handbook presents crystal-clear guidance for navigating the most complex challenges that confront leaders and operators in high-growth startups.

Heroin is a fascinating drug to most people.It is often referred to as the “hardest drug.” By this logic, people might start with alcohol, work up to marijuana and maybe LSD. Then they reach to cocaine or methamphetamine. And finally, at the end of the journey is heroin. But like most things about heroin, this is more myth than reality. For non-users, this mythic power is exciting. And writers for the last century have been more than willing to pander to such readers in pulp and art novels all the way up to television crime novels. But it is rare for the most people to get a real look at what is, after all, the very core of what heroin is about for its users. To users, the interest is obvious. But ignorance of the the details of drug use among heroin users is rife — usually based on what the author calls “old junkie tales.” The difference between such folklore and the truth is often the difference between life and death. The Heroin User’s Handbook reveals the largely hidden world of heroin use based upon actual work with users and countless scholarly books and articles. And it does it in an extremely readable, non-technical manner — even while providing detained and accurate information. The book discusses all aspects of heroin use: the acquisition of drugs, the administration of them, health risks, legal issues, social aspects, and addiction and detox. It provides the non-heroin world with a detailed look inside a very rarefied subculture. But it also provides the those in the heroin using world life-saving information.

Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) Clear step-by-step instructions on executing the analyses Clear guidance on how to interpret results Primary instruction in R but added sections for Python coders Discussion exercises and data exercises for each of the main chapters Final chapter of practice material and datasets ideal for class homework or project work.

The first version of this book, Packaging Materials and Containers was published in 1967 and was revised extensively ten years later under the title The Packaging Media. Some thirty or so authors were involved in producing the initial texts for these books, and I must acknowledge their material, much of which is still valid. It is now thirteen years since The Packaging Media-high time to take stock and incorporate the considerable advances in materials, forms, techniques and machinery that have taken place. In 1977, wherever possible, we asked the original authors to carry out the revisions, but retirements and job changes have now eliminated over twenty of the original authors. We have therefore appointed an Editorial Board to advise on this more extensive revision, and I wish to thank them for their detailed and helpful assistance: Dr C. J. Mackson and Professor Y. Dagel for general comments and guidance on the overall plan and, in particular, the Introduction (chapter 1); Graham Gordon and Harri Mostyn for assistance with much of Part D on Distribution Packages, and Dennis Hine and Susan Selke for their work in respect of paperboard and plastics retail packaging, respectively. A major contribution was made by the seventh member of the Editorial Board, David Osborne, who advised in the area of glass.

Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks. Starting with an overview of the origins of graph theory and its current applications in the social sciences, the book proceeds to give in-depth technical instruction on how to construct and store graphs from data, how to visualize those graphs compellingly and how to convert common data structures into graph-friendly form. The book explores critical elements of network analysis in detail, including the measurement of distance and centrality, the detection of communities and cliques, and the analysis of assortativity and similarity. An extension chapter offers an introduction to graph database technologies. Real data sets from various research contexts are used for both instruction and for end of chapter practice exercises and a final chapter contains data sets and exercises ideal for larger personal or group projects of varying difficulty level. Key features: Immediately implementable code, with extensive and varied illustrations of graph variants and layouts. Examples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigation. Dedicated chapter on graph visualization methods. Practical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachment. Various downloadable data sets for use both in class and individual learning projects. Final chapter dedicated to individual or group project examples.