It's great to have theory and evidenced-based research at your disposal, but what will you do when somebody asks you to make your dashboard 'cooler' by adding packed bubbles and donut charts?The expert authors have a combined 30-plus years of hands-on experience helping people in hundreds of organizations build effective visualizations. They have fought many 'best practices' battles and having endured bring an uncommon empathy to help you, the reader of this book, survive and thrive in the data visualization world.A well-designed dashboard can point out risks, opportunities, and more; but common challenges and misconceptions can make your dashboard useless at best, and misleading at worst. The Big Book of Dashboards gives you the tools, guidance, and models you need to produce great dashboards that inform, enlighten, and engage.

    The New York Times bestseller that introduced the business world to a future that s already here -- now in paperback with a new chapter about Long Tail Marketing and a new epilogue.Winner of the Gerald Loeb Award for Best Business Book of the Year.In the most important business book since The Tipping Point, Chris Anderson shows how the future of commerce and culture isn t in hits, the high-volume head of a traditional demand curve, but in what used to be regarded as misses -- the endlessly long tail of that same curve.

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks. Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES - Exploring R, RStudio, and R packages - Using R for math: variable types, vectors, calling functions, and more - Exploiting data structures, including data.frames, matrices, and lists - Creating attractive, intuitive statistical graphics - Writing user-defined functions - Controlling program flow with if, ifelse, and complex checks - Improving program efficiency with group manipulations - Combining and reshaping multiple datasets - Manipulating strings using R's facilities and regular expressions - Creating normal, binomial, and Poisson probability distributions - Programming basic statistics: mean, standard deviation, and t-tests - Building linear, generalized linear, and nonlinear models - Assessing the quality of models and variable selection - Preventing overfitting, using the Elastic Net and Bayesian methods - Analyzing univariate and multivariate time series data - Grouping data via K-means and hierarchical clustering - Preparing reports, slideshows, and web pages with knitr - Building reusable R packages with devtools and Rcpp - Getting involved with the R global community

    Drawing on Lopp's management experiences at Apple, Netscape, Symantec, and Borland, this book is full of stories based on companies in the Silicon Valley where people have been known to yell at each other. It is a place full of dysfunctional bright people who are in an incredible hurry to find the next big thing so they can strike it rich and then do it all over again. Among these people are managers, a strange breed of people who through a mystical organizational ritual have been given power over your future and your bank account.Whether you're an aspiring manager, a current manager, or just wondering what the heck a manager does all day, there is a story in this book that will speak to you.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

     Featuring a new foreword by Ariel Emanuel and Patrick WhitesellMark H. McCormack, one of the most successful entrepreneurs in American business, is widely credited as the founder of the modern-day sports marketing industry. On a handshake with Arnold Palmer and less than a thousand dollars, he started International Management Group and, over a four-decade period, built the company into a multimillion-dollar enterprise with offices in more than forty countries.To this day, McCormack's business classic remains a must-read for executives and managers at every level. Relating his proven method of "applied people sense" in key chapters on sales, negotiation, reading others and yourself, and executive time management, McCormack presents powerful real-world guidance on- the secret life of a deal - management philosophies that don't work (and one that does) - the key to running a meeting--and how to attend one - the positive use of negative reinforcement - proven ways to observe aggressively and take the edge - and much more Praise for What They Don't Teach You at Harvard Business School "Incisive, intelligent, and witty, What They Don't Teach You at Harvard Business School is a sure winner--like the author himself. Reading it has taught me a lot."--Rupert Murdoch, executive chairman, News Corp, chairman and CEO, 21st Century Fox "Clear, concise, and informative . . . Like a good mentor, this book will be a valuable aid throughout your business career."--Herbert J. Siegel, chairman, Chris-Craft Industries, Inc."Mark McCormack describes the approach I have personally seen him adopt, which has not only contributed to the growth of his business, but mine as well."--Arnold Palmer"There have been what we love to call dynasties in every sport. IMG has been different. What this one brilliant man, Mark McCormack, created is the only dynasty ever over all sport."--Frank Deford, senior contributing writer, Sports Illustrated

    Crossing the Chasm has become the bible for bringing cutting-edge products to progressively larger markets. This edition provides new insights into the realities of high-tech marketing, with special emphasis on the Internet. It's essential reading for anyone with a stake in the world's most exciting marketplace.

    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.

    That’s a mistake, Tim Harford says in The Data Detective. We shouldn’t be suspicious of statistics—we need to understand what they mean and how they can improve our lives: they are, at heart, human behavior seen through the prism of numbers and are often “the only way of grasping much of what is going on around us.” If we can toss aside our fears and learn to approach them clearly—understanding how our own preconceptions lead us astray—statistics can point to ways we can live better and work smarter.As “perhaps the best popular economics writer in the world” (New Statesman), Tim Harford is an expert at taking complicated ideas and untangling them for millions of readers. In The Data Detective, he uses new research in science and psychology to set out ten strategies for using statistics to erase our biases and replace them with new ideas that use virtues like patience, curiosity, and good sense to better understand ourselves and the world. As a result, The Data Detective is a big-idea book about statistics and human behavior that is fresh, unexpected, and insightful.

    By 1993, the computer industry had changed so rapidly the company was on its way to losing $16 billion and IBM was on a watch list for extinction -- victimized by its own lumbering size, an insular corporate culture, and the PC era IBM had itself helped invent.Then Lou Gerstner was brought in to run IBM. Almost everyone watching the rapid demise of this American icon presumed Gerstner had joined IBM to preside over its continued dissolution into a confederation of autonomous business units. This strategy, well underway when he arrived, would have effectively eliminated the corporation that had invented many of the industry's most important technologies.Instead, Gerstner took hold of the company and demanded the managers work together to re-establish IBM's mission as a customer-focused provider of computing solutions. Moving ahead of his critics, Gerstner made the hold decision to keep the company together, slash prices on his core product to keep the company competitive, and almost defiantly announced, "The last thing IBM needs right now is a vision."Who Says Elephants Can't Dance? tells the story of IBM's competitive and cultural transformation. In his own words, Gerstner offers a blow-by-blow account of his arrival at the company and his campaign to rebuild the leadership team and give the workforce a renewed sense of purpose. In the process, Gerstner defined a strategy for the computing giant and remade the ossified culture bred by the company's own success.The first-hand story of an extraordinary turnaround, a unique case study in managing a crisis, and a thoughtful reflection on the computer industry and the principles of leadership, Who Says Elephants Can't Dance? sums up Lou Gerstner's historic business achievement. Taking readers deep into the world of IBM's CEO, Gerstner recounts the high-level meetings and explains the pressure-filled, no-turning-back decisions that had to be made. He also offers his hard-won conclusions about the essence of what makes a great company run.In the history of modern business, many companies have gone from being industry leaders to the verge of extinction. Through the heroic efforts of a new management team, some of those companies have even succeeded in resuscitating themselves and living on in the shadow of their former stature. But only one company has been at the pinnacle of an industry, fallen to near collapse, and then, beyond anyone's expectations, returned to set the agenda. That company is IBM.Lou Gerstener, Jr., served as chairman and chief executive officer of IBM from April 1993 to March 2002, when he retired as CEO. He remained chairman of the board through the end of 2002. Before joining IBM, Mr. Gerstner served for four years as chairman and CEO of RJR Nabisco, Inc. This was preceded by an eleven-year career at the American Express Company, where he was president of the parent company and chairman and CEO of its largest subsidiary. Prior to that, Mr. Gerstner was a director of the management consulting firm of McKinsey & Co., Inc. He received a bachelor's degree in engineering from Dartmouth College and an MBA from Harvard Business School.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    Cracking the PM Interview is a comprehensive book about landing a product management role in a startup or bigger tech company. Learn how the ambiguously-named "PM" (product manager / program manager) role varies across companies, what experience you need, how to make your existing experience translate, what a great PM resume and cover letter look like, and finally, how to master the interview: estimation questions, behavioral questions, case questions, product questions, technical questions, and the super important "pitch."

    They are typically used to categorize something based on other data that you have. The purpose of this book is to help you understand how Random Forests work, as well as the different options that you have when using them to analyze a problem. Additionally, since Decision Trees are a fundamental part of Random Forests, this book explains how they work. This book is focused on understanding Random Forests at the conceptual level. Knowing how they work, why they work the way that they do, and what options are available to improve results. This book covers how Random Forests work in an intuitive way, and also explains the equations behind many of the functions, but it only has a small amount of actual code (in python). This book is focused on giving examples and providing analogies for the most fundamental aspects of how random forests and decision trees work. The reason is that those are easy to understand and they stick with you. There are also some really interesting aspects of random forests, such as information gain, feature importances, or out of bag error, that simply cannot be well covered without diving into the equations of how they work. For those the focus is providing the information in a straight forward and easy to understand way.