In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

    With this concise book, you will quickly gain working knowledge of Sinatra and its minimalist approach to building both standalone and modular web applications. Sinatra serves as a lightweight wrapper around Rack middleware, with syntax that maps closely to functions exposed by HTTP verbs, which makes it ideal for web services and APIs. If you have experience building applications with Ruby, you’ll quickly learn language fundamentals and see under-the-hood techniques, with the help of several practical examples. Then you’ll get hands-on experience with Sinatra by building your own blog engine. Learn Sinatra’s core concepts, and get started by building a simple application Create views, manage sessions, and work with Sinatra route definitions Become familiar with the language’s internals, and take a closer look at Rack Use different subclass methods for building flexible and robust architectures Put Sinatra to work: build a blog that takes advantage of service hooks provided by the GitHub API

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    Rogers argues that digital transformation is not about updating your technology but about upgrading your strategic thinking. Based on Rogers's decade of research and teaching at Columbia Business School, and his consulting for businesses around the world, The Digital Transformation Playbook shows how pre-digital-era companies can reinvigorate their game plans and capture the new opportunities of the digital world.Rogers shows why traditional businesses need to rethink their underlying assumptions in five domains of strategy--customers, competition, data, innovation, and value. He reveals how to harness customer networks, platforms, big data, rapid experimentation, and disruptive business models--and how to integrate these into your existing business and organization.Rogers illustrates every strategy in this playbook with real-world case studies, from Google to GE, from Airbnb to the New York Times. With practical frameworks and nine step-by-step planning tools, he distills the lessons of today's greatest digital innovators and makes them usable for businesses at any stage.Many books offer advice for digital start-ups, but The Digital Transformation Playbook is the first complete treatment of how legacy businesses can transform to thrive in the digital age. It is an indispensable guide for executives looking to take their firms to the next stage of profitable growth.

    In The Phoenix Project, Bill, an IT manager at Parts Unlimited, is tasked with a project critical to the future of the business, code named Phoenix Project. But the project is massively over budget and behind schedule. The CEO demands Bill fix the mess in ninety days or else Bill's entire department will be outsourced. In The Unicorn Project, we follow Maxine, a senior lead developer and architect, as she is exiled to the Phoenix Project, to the horror of her friends and colleagues, as punishment for contributing to a payroll outage. She tries to survive in what feels like a heartless and uncaring bureaucracy and to work within a system where no one can get anything done without endless committees, paperwork, and approvals. One day, she is approached by a ragtag bunch of misfits who say they want to overthrow the existing order, to liberate developers, to bring joy back to technology work, and to enable the business to win in a time of digital disruption. To her surprise, she finds herself drawn ever further into this movement, eventually becoming one of the leaders of the Rebellion, which puts her in the crosshairs of some familiar and very dangerous enemies. The Age of Software is here, and another mass extinction event looms--this is a story about "red shirt" developers and business leaders working together, racing against time to innovate, survive, and thrive in a time of unprecedented uncertainty...and opportunity.

    In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success.Topics include: What it means to be "data driven." The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team.

    Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud-computing technologies. This collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.

    Based on the five years of work in the field with companies like Toyota, 3M, Schlumberger, and Autodesk, The Scrum Fieldbook offers a hands on approach to implementing the practices of the Scrum framework among traditional, non-technology companies.In J.J. Sutherland's first book, Scrum, written with his father, Jeff, he laid out the Scrum framework used by almost all of today's leading technology companies, based on Agile software development. The book has gone on to sell over 120,000 copies in its print and ebooks editions, and another 95,000 copies as an audio download. Since publication, the Scrum framework has exploded across the corporate world. J.J. and his team at Scrum Inc. have worked with private space companies, global oil and gas firms, banks, medical device manufacturers, and firms on the cutting edge of genetic science.In the Scrum Fieldbook, J.J. takes leaders, managers, and employees deeper into the specific challenges and opportunities the company has faced in working with major established companies like Toyota, 3M, Schlumberger, and Autodesk. He shows how the simple scrum framework can be successfully applied to any situation, and in every industry, from automobile manufacturers in the USA and Europe, to nonprofits in Africa, from home renovation contractors in Minnesota to gas exploration companies in South America, from building fighter planes to improving the banking industry.

    

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    In many ways this has improved our lives, yet, we as individuals do not benefit from this wealth of data as much as we could. Moreover, whether it is a bank evaluating our credit worthiness, an insurance company determining our risk level, or a potential employer deciding whether we get a job, it is likely that this data will be used against us rather than for us. In Data for the People, Andreas Weigend draws on his years as a consultant for commerce, education, healthcare, travel and finance companies to outline how Big Data can work better for all of us. As of today, how much we benefit from Big Data depends on how closely the interests of big companies align with our own. Too often, outdated standards of control and privacy force us into unfair contracts with data companies, but it doesn't have to be this way. Weigend makes a powerful argument that we need to take control of how our data is used to actually make it work for us. Only then can we the people get back more from Big Data than we give it. Big Data is here to stay. Now is the time to find out how we can be empowered by it.

    The 7th edition was revised with updated content, a modern design, and engaging media for today's students. Media can be found in myBIZlab, an online system which provides students with extensive online resources, assessment, and actual interaction with business topics.

    . . joy. As a package-delivery person once remarked, “I don’t know what you do, but whatever it is, I want to work here.”Every year, thousands of visitors come from around the world to visit Menlo Innovations, a small software company in Ann Arbor, Michigan. They make the trek not to learn about technology but to witness a radically different approach to company culture.CEO and “Chief Storyteller” Rich Sheridan removed the fear and ambiguity that typically make a workplace miserable. His own experience in the software industry taught him that, for many, work was marked by long hours and mismanaged projects with low-quality results. There had to be a better way.With joy as the explicit goal, Sheridan and his team changed everything about how the company was run. They established a shared belief system that supports working in pairs and embraces making mistakes, all while fostering dignity for the team.The results blew away all expectations. Menlo has won numerous growth awards and was named an Inc. magazine “audacious small company.” It has tripled its physical office three times and produced products that dominate markets for its clients.Joy, Inc. offers an inside look at how Sheridan and Menlo created a joyful culture, and shows how any organization can follow their methods for a more passionate team and sustainable, profitable results. Sheridan also shows how to run smarter meetings and build cultural training into your hiring process.Joy, Inc. offers an inspirational blueprint for readers in any field who want a committed, energizing atmosphere at work—leading to sustainable business results.

    But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.When AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions. Prediction tools increase productivity--operating machines, handling documents, communicating with customers. Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete. Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.

    The authors help students learn key sociological concepts through real research examples related to the dynamic interplay of race, class, gender, and other social variables.