Alex Liu – författare

Joy at work—why settle for anything less? In Joy Works: Empowering Teams in the New Era of Work, Alex Liu delivers an engaging blueprint for ensuring people feel safe and inspired at work. Liu, the managing partner and chairman of Kearney, asks, "Why would we settle for anything less than joy at work?" In the book, you'll find a step-by-step action plan for approaching joy at work using the three key drivers that determine employee happiness—people, praise, and purpose—and learn how to implement that plan for maximum results and maximum joy. The author demonstrates how to create more joy for your people at work, in both virtual and in-person environments, as well as how to incorporate joyfulness even in periods of dramatically heightened stress. He calls on his years of conversations with leaders around the world, both as an advisor to executives and through his popular podcast, Joy@Work. Readers will learn from a diverse collection of leaders, from psychologists, academics, athletes, nonprofit and board leaders, and a Broadway producer, to leaders at companies including HPE, Cisco, T-Mobile, SAP, and UPS. In the dialogues and research, readers will also find:  An introduction to "ikigai," a Japanese concept meaning "reason for being"—a framework we can all use to find joy and meaning in our workAn investigation into the link between social justice and joy, using conversations with leaders who have committed to making social progress a priorityA new perspective on how the next generation will view joy at work, the Great Reflection, and the shifting balance of power in work culturesIn-depth discussions about people, purpose, and praise: the three key elements in building a joyful work experienceA call for more reflective leadership—a new approach to power leaders through uncertain and challenging timesJoy Works is an essential handbook for anyone who wants to create more joy in their work — the leaders who want to shift corporate cultures, managers who are facing pressures to innovate, young people who are adamant that they can have a life and a career that's centered around joy and meaning, and anyone who thinks "joy at work" is a near-term possibility, not an oxymoron. This guide to the changing reality and opportunity of work belongs in the libraries of anyone interested in creating a more engaging and productive virtual, hybrid, or in-person workspace.Let's build more joy.

Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guideKey Features[*] Customize Apache Spark and R to fit your analytical needs in customer research, fraud detection, risk analytics, and recommendation engine development[*] Develop a set of practical Machine Learning applications that can be implemented in real-life projects[*] A comprehensive, project-based guide to improve and refine your predictive models for practical implementationBook DescriptionThere's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data.Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers. What you will learn[*] Set up Apache Spark for machine learning and discover its impressive processing power[*] Combine Spark and R to unlock detailed business insights essential for decision making[*] Build machine learning systems with Spark that can detect fraud and analyze financial risks[*]Build predictive models focusing on customer scoring and service ranking[*]Build a recommendation systems using SPSS on ApacheSpark[*]Tackle parallel computing and find out how it can support your machine learning projects[*]Turn open data and communication data into actionable insights by making use of various forms of machine learningWho this book is forIf you are a data scientist, a data analyst, or an R and SPSS user with a good understanding of machine learning concepts, algorithms, and techniques, then this is the book for you. Some basic understanding of Spark and its core elements and application is required.

This two volume set LNCS 10039 and LNCS 10040 constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Cloud Computing and Security, ICCCS 2016, held in Nanjing, China, during July 29-31, 2016. The 97 papers of these volumes were carefully reviewed and selected from 272 submissions.