They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.

    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

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

      Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

    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 mindsetLook at six sample business models and how they map to new ventures of all sizesFind the One Metric That Matters to youLearn how to draw a line in the sand, so you’ll know it’s time to move forwardApply Lean Analytics principles to large enterprises and established products

    For Pricing Strategy or Pricing and Product Policy courses in MBA and/or advanced undergraduate marketing courses The Strategy and Tactics of Pricing provides a comprehensive, practical, step-by-step guide to pricing analysis and strategy development.

    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.

    Courses tailored to fit individual pupils. Textbooks that talk back. This is tomorrow’s education landscape, thanks to the power of big data. These advances go beyond the much-discussed rise of online courses. As the New York Times-bestselling authors of Big Data explain, the truly fascinating changes are actually occurring in how we measure students’ progress and how we can use that data to improve education for everyone, in real time, both on- and offline. Learning with Big Data offers an eye-opening, insight-packed tour through these new trends, for educators, administrators, and readers interested in the latest developments in business and technology.

    In this updated and expanded 10th anniversary edition, internationally known leadership transition expert Michael D. Watkins gives you the keys to successfully negotiating your next move—whether you’re onboarding into a new company, being promoted internally, or embarking on an international assignment.In The First 90 Days, Watkins outlines proven strategies that will dramatically shorten the time it takes to reach what he calls the "breakeven point" when your organization needs you as much as you need the job. This new edition includes a substantial new preface by the author on the new definition of a career as a series of transitions; and notes the growing need for effective and repeatable skills for moving through these changes. As well, updated statistics and new tools make this book more reader-friendly and useful than ever.As hundreds of thousands of readers already know, The First 90 Days is a road map for taking charge quickly and effectively during critical career transition periods—whether you are a first-time manager, a mid-career professional on your way up, or a newly minted CEO.

    Topics covered range from the difference between deductive and inductive reasoning, to a discussion of how to highlight the structure of information.

    This edition also features the developments in Operations Research, such as metaheuristics, simulation, and spreadsheet modeling.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    In a brilliant and unconventional book, Phil Rosenzweig unmasks the delusions that are commonly found in the corporate world. These delusions affect the business press and academic research, as well as many bestselling books that promise to reveal the secrets of success or the path to greatness. Such books claim to be based on rigorous thinking, but operate mainly at the level of storytelling. They provide comfort and inspiration, but deceive managers about the true nature of business success.The most pervasive delusion is the Halo Effect. When a company's sales and profits are up, people often conclude that it has a brilliant strategy, a visionary leader, capable employees, and a superb corporate culture. When performance falters, they conclude that the strategy was wrong, the leader became arrogant, the people were complacent, and the culture was stagnant. In fact, little may have changed -- company performance creates a Halo that shapes the way we perceive strategy, leadership, people, culture, and more.Drawing on examples from leading companies including Cisco Systems, IBM, Nokia, and ABB, Rosenzweig shows how the Halo Effect is widespread, undermining the usefulness of business bestsellers from "In Search of Excellence" to "Built to Last" and "Good to Great."Rosenzweig identifies nine popular business delusions. Among them:"The Delusion of Absolute Performance: " Company performance is relative to competition, not absolute, which is why following a formula can never guarantee results. Success comes from doing things better than rivals, which means that managers have to take risks."The Delusion of Rigorous Research: " Many bestselling authors praise themselves for the vast amount of data they have gathered, but forget that if the data aren't valid, it doesn't matter how much was gathered or how sophisticated the research methods appear to be. They trick the reader by substituting sizzle for substance."The Delusion of Single Explanations: " Many studies show that a particular factor, such as corporate culture or social responsibility or customer focus, leads to improved performance. But since many of these factors are highly correlated, the effect of each one is usually less than suggested.In what promises to be a landmark book, "The Halo Effect" replaces mistaken thinking with a sharper understanding of what drives business success and failure. "The Halo Effect" is a guide for the thinking manager, a way to detect errors in business research and to reach a clearer understanding of what drives business success and failure.Skeptical, brilliant, iconoclastic, and mercifully free of business jargon, Rosenzweig's book is nevertheless dead serious, making his arguments about important issues in an unsparing and direct way that will appeal to a broad business audience. For managers who want to separate fact from fiction in the world of business, "The Halo Effect" is essential reading -- witty, often funny, and sharply argued, it's an antidote to so much of the conventional thinking that clutters business bookshelves.