In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    In Zero to One, legendary entrepreneur and investor Peter Thiel shows how we can find singular ways to create those new things. Thiel begins with the contrarian premise that we live in an age of technological stagnation, even if we’re too distracted by shiny mobile devices to notice. Information technology has improved rapidly, but there is no reason why progress should be limited to computers or Silicon Valley. Progress can be achieved in any industry or area of business. It comes from the most important skill that every leader must master: learning to think for yourself.Doing what someone else already knows how to do takes the world from 1 to n, adding more of something familiar. But when you do something new, you go from 0 to 1. The next Bill Gates will not build an operating system. The next Larry Page or Sergey Brin won’t make a search engine. Tomorrow’s champions will not win by competing ruthlessly in today’s marketplace. They will escape competition altogether, because their businesses will be unique. Zero to One presents at once an optimistic view of the future of progress in America and a new way of thinking about innovation: it starts by learning to ask the questions that lead you to find value in unexpected places.

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    In Working Backwards, these two long-serving Amazon executives reveal and codify the principles and practices that drive the success of one of the most extraordinary companies the world has ever known. With twenty-seven years of Amazon experience between them, much of it in the early aughts—a period of unmatched innovation that brought products and services including Kindle, Amazon Prime, Amazon Studios, and Amazon Web Services to life—Bryar and Carr offer unprecedented access to the Amazon way as it was refined, articulated, and proven to be repeatable, scalable, and adaptable.With keen analysis and practical steps for applying it at your own company—no matter the size—the authors illuminate how Amazon’s fourteen leadership principles inform decision-making at all levels and reveal how the company’s culture has been defined by four characteristics: customer obsession, long-term thinking, eagerness to invent, and operational excellence. Bryar and Carr explain the set of ground-level practices that ensure these are translated into action and flow through all aspects of the business.Working Backwards is a practical guidebook and a corporate narrative, filled with the authors’ in-the-room recollections of what “Being Amazonian” is like and how it has affected their personal and professional lives. They demonstrate that success on Amazon’s scale is not achieved by the genius of any single leader, but rather through commitment to and execution of a set of well-defined, rigorously-executed principles and practices—shared here for the very first time. A Macmillan Audio production from St. Martin's Press

    The result is a tremendous amount of time and money wasted due to wrong assumptions, lack of focus, poor communication of objectives, lack of understanding and misalignment with overall goals. There has to be a better way to deliver!This handbook is a practical guide to impact mapping, a simple yet incredibly effective method for collaborative strategic planning that helps organisations make an impact with software. Impact mapping helps to create better plans and roadmaps that ensure alignment of business and delivery, and are easily adaptable to change. Impact mapping fits nicely into several current trends in software product management and release planning, including goal-oriented requirements engineering, frequent iterative delivery, agile and lean software methods, lean startup product development cycles, and design thinking.Who is this book for?The primary audience of this book are senior people involved in building software products or delivering software projects, from both business and delivery sides. This includes business sponsors and those whose responsibilities include product ownership, project oversight or portfolio management, architecture, business analysis, quality improvement and assurance and delivery. - Business people assigned to software projects will learn how to communicate their ideas better.- Senior product or project sponsors will learn how to communicate their assumptions more effectively to delivery teams, how to engage delivery teams to make better strategic decisions, and how to manage their project portfolio more effectively.- Delivery teams that are already working under the umbrella of agile or lean delivery methods, and more recently lean startup ideas, will learn how to better focus deliverables and engage business sponsors and users.- Delivery teams moving to agile or lean delivery methods will get ideas on how to address some common issues with scaling these practices, such as creating a big picture view, splitting work into small chunks that still have business value and reporting progress more meaningfully.About the authorGojko Adzic is a strategic software delivery consultant who works with ambitious teams to improve the quality of their software products and processes. Gojko won the 2012 Jolt Award for the best book, was voted by peers as the most influential agile testing professional in 2011, and his blog won the UK Agile Award for the best online publication in 2010. To get in touch, write to gojko@neuri.co.uk or visit http://gojko.net.

    It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures.

    Porter's Competitive Advantage explores the underpinnings of competitive advantage in the individual firm.Competitive Advantage introduces a whole new way of understanding what a firm does. Porter's groundbreaking concept of the value chain disaggregates a company into "activities," or the discrete functions or processes that represent the elemental building blocks of competitive advantage. Now an essential part of international business thinking, Competitive Advantage takes strategy from broad vision to an internally consistent configuration of activities. Its powerful framework provides the tools to understand the drivers of cost and a company's relative cost position. Porter's value chain enables managers to isolate the underlying sources of buyer value that will command a premium price, and the reasons why one product or service substitutes for another. He shows how competitive advantage lies not only in activities themselves but in the way activities relate to each other, to supplier activities, and to customer activities. Competitive Advantage also provides for the first time the tools to strategically segment an industry and rigorously assess the competitive logic of diversification. That the phrases "competitive advantage" and "sustainable competitive advantage" have become commonplace is testimony to the power of Porter's ideas. Competitive Advantage has guided countless companies, business school students, and scholars in understanding the roots of competition. Porter's work captures the extraordinary complexity of competition in a way that makes strategy both concrete and actionable.

    Companies that live and die by outputs often fall into the "build trap," cranking out features to meet their schedule rather than the customer’s needs. In this book, Melissa Perri explains how laying the foundation for great product management can help companies solve real customer problems while achieving business goals. By understanding how to communicate and collaborate within a company structure, you can create a product culture that benefits both the business and the customer. You’ll learn product management principles that can be applied to any organization, big or small. In five parts, this book explores: Why organizations ship features rather than cultivate the value those features represent How to set up a product organization that scales How product strategy connects a company’s vision and economic outcomes back to the product activities How to identify and pursue the right opportunities for producing value through an iterative product framework How to build a culture focused on successful outcomes over outputs

    With this hands-on guide, you'll learn how to write effective, idiomatic Python code by leveraging its best and possibly most neglected features. Author Luciano Ramalho takes you through Python's core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time.Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3.This book covers:Python data model: understand how special methods are the key to the consistent behavior of objectsData structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode ageFunctions as objects: view Python functions as first-class objects, and understand how this affects popular design patternsObject-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritanceControl flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packagesMetaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work"

    But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool.In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling.Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics.

    Data Governance should not be about command-and-control, yet at times could become invasive or threatening to the work, people and culture of an organization. Non-Invasive Data Governance™ focuses on formalizing existing accountability for the management of data and improving formal communications, protection, and quality efforts through effective stewarding of data resources. Non-Invasive Data Governance will provide you with a complete set of tools to help you deliver a successful data governance program. Learn how: Steward responsibilities can be identified and recognized, formalized, and engaged according to their existing responsibility rather than being assigned or handed to people as more work. Governance of information can be applied to existing policies, standard operating procedures, practices, and methodologies, rather than being introduced or emphasized as new processes or methods. Governance of information can support all data integration, risk management, business intelligence and master data management activities rather than imposing inconsistent rigor to these initiatives. A practical and non-threatening approach can be applied to governing information and promoting stewardship of data as a cross-organization asset. Best practices and key concepts of this non-threatening approach can be communicated effectively to leverage strengths and address opportunities to improve.

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    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.

    Yet, managing human beings and navigating complex product roadmaps is no easy task, and it's rare to find a product leader who can steward a digital product from concept to launch without a couple of major hiccups. Why do some product leaders succeed while others don't?This insightful book presents interviews with nearly 100 leading product managers from all over the world. Authors Richard Banfield, Martin Eriksson, and Nate Walkingshaw draw on decades of experience in product design and development to capture the approaches, styles, insights, and techniques of successful product managers. If you want to understand what drives good product leaders, this book is an irreplaceable resource.In three parts, Product Leadership helps you explore:Themes and patterns of successful teams and their leaders, and ways to attain those characteristicsThe best approaches for guiding your product team through the startup, emerging, and enterprise stages of a company's evolutionStrategies and tactics for working with customers, agencies, partners, and external stakeholders