Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started.Use R's default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing

    In Marketing Metrics, four leading researchers and consultants systematically introduce today's most powerful marketing metrics. The authors show how to use a "dashboard" of metrics to view market dynamics from various perspectives, maximize accuracy, and "triangulate" to optimal solutions. Their comprehensive coverage includes measurements of promotional strategy, advertising, and distribution; customer perceptions; market share; competitors' power; margins and profits; products and portfolios; customer profitability; sales forces and channels; pricing strategies; and more. You'll learn how and when to apply each metric, and understand tradeoffs and nuances that are critical to using them successfully. The authors also demonstrate how to use marketing metrics as leading indicators, identifying crucial new opportunities and challenges. For clarity and simplicity all calculations can be performed by hand, or with basic spreadsheet techniques. In coming years, few marketers will rise to senior executive levels without deep fluency in marketing metrics. This book is the fastest, easiest way to gain that fluency.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    According to Kaiser, successful arts organizations pursue strong programmatic marketing campaigns that compel people to buy tickets, enroll in classes, and so on—in short, to participate in the organization’s programs. Additionally, they create exciting activities that draw people to the organization as a whole. This institutional marketing creates a sense of enthusiasm that attracts donors, board members, and volunteers. Kaiser calls this group of external supporters the family. When this hidden engine is humming, staff, board, and audience members, artists, and donors feel confidence in the future. Resources are reinvested in more and better art, which is marketed aggressively; as a result, the “family” continues to grow, providing even more resources. This self-reinforcing cycle underlies the activities of all healthy arts organizations, and the theory behind it can be used as a diagnostic tool to reveal—and remedy—the problems of troubled ones. This book addresses each element of the cycle in the hope that more arts organizations around the globe—from orchestras, theaters, museums, opera companies, and classical and modern dance organizations to service organizations and other not-for-profit cultural institutions—will be able to sustain remarkable creativity, pay the bills, and have fun doing so!

    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.

    To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok.In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years—at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information (Alan Turing's role in breaking Germany's Enigma code during World War II), and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA de-coding to Homeland Security.Drawing on primary source material and interviews with statisticians and other scientists, The Theory That Would Not Die is the riveting account of how a seemingly simple theorem ignited one of the greatest controversies of all time.

    While most B2B startups obsess over scalability and tracking, Drift takes a different approach. In This Won't Scale, you'll find insider lessons and plays from the Drift Marketing team that have helped the business grow at a hypergrowth rate. It contains 41 plays organized into easy-to-read and reference chapters. Keep it on your desk, thumb through it when you're looking for inspiration and come back to it over time. You’ll discover not only Drift's abnormal approach, but also hear never-before-told stories and learn how to implement Drift's marketing plays into your own marketing strategy.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    As more business intelligence pros begin using Power Pivot, they find that they do not have the Excel skills to clean the data in Excel; Power Query solves this problem. This book shows how to use the Power Query tool to get difficult data sets into both Excel and Power Pivot, and is solely devoted to Power Query dashboarding and reporting.

    Instead of giving you the most broadly popular result, Google now tries to predict what you are most likely to click on. According to MoveOn.org board president Eli Pariser, Google's change in policy is symptomatic of the most significant shift to take place on the Web in recent years - the rise of personalization. In this groundbreaking investigation of the new hidden Web, Pariser uncovers how this growing trend threatens to control how we consume and share information as a society-and reveals what we can do about it.Though the phenomenon has gone largely undetected until now, personalized filters are sweeping the Web, creating individual universes of information for each of us. Facebook - the primary news source for an increasing number of Americans - prioritizes the links it believes will appeal to you so that if you are a liberal, you can expect to see only progressive links. Even an old-media bastion like "The Washington Post" devotes the top of its home page to a news feed with the links your Facebook friends are sharing. Behind the scenes a burgeoning industry of data companies is tracking your personal information to sell to advertisers, from your political leanings to the color you painted your living room to the hiking boots you just browsed on Zappos.In a personalized world, we will increasingly be typed and fed only news that is pleasant, familiar, and confirms our beliefs - and because these filters are invisible, we won't know what is being hidden from us. Our past interests will determine what we are exposed to in the future, leaving less room for the unexpected encounters that spark creativity, innovation, and the democratic exchange of ideas.While we all worry that the Internet is eroding privacy or shrinking our attention spans, Pariser uncovers a more pernicious and far-reaching trend on the Internet and shows how we can - and must - change course. With vivid detail and remarkable scope, The Filter Bubble reveals how personalization undermines the Internet's original purpose as an open platform for the spread of ideas and could leave us all in an isolated, echoing world.

    It takes several years for that—more than a book and a couple of days of reading can promise.  But this gives you a plan for success before you even start. Think of it as a road map for your first 90 days!Now, you can start a solid blog with the potential to make money WITHOUT a $1,000+ blogging education! Here’s a snapshot of what’s packed into this how-to guide: Popular guru promises exposed! I expose the truth about popular revenue streams and why NOT ALL monetization options are right for you despite guru promises! The 2M (+1) strategy to help you hit your first $1K blogging. How to find YOUR unique angle, so you can stand out from the pack and attract the right kind of readers. The smartest ways to make critical website pages sticky—Make these pages shout out “YES, you’re in the right place!” and understand what you need and don’t need to include. Why some bloggers make the leap and others don’t. (It has everything to do with what they don’t do!) 3 MUST-ANSWER questions that will shape your blog’s journey. How to create a strategic blog launch plan and my answer to the question “How many posts do you need before launching?” (No more confusion or stress. Just an actionable plan for results.) AND MORE! Imagine knowing exactly what you need to focus on despite all the distractions pulling you in a million directions.Imagine if in a mere year you accomplish more than you ever thought possible, feel a sense of satisfaction, and actually make progress toward this larger vision of what you want your blog and business to do for you.You don't flinch, get panicky, or try different tactics hoping one sticks. You have a plan of action and every decision you make for your blog is calculated and intentional. That’s the power of the process and the promise behind The Blog Startup!Intrigued yet? Then scroll to the top and click or tap “Buy Now.”

    This quick read is a must for new managers -- and also for senior managers who are seeking a framework to help newer managers avoid the common mistakes." - Dave Stachowiak, Host of the ‘Coaching for Leaders’ podcast Do you feel stressed and overwhelmed with tasks that you can’t keep up with? Are you struggling with the delegation of work to your employees?  Effective Delegation of Authority is a brief guide for new managers that will help you improve your delegation skills in simple steps.If you’re a manager or entrepreneur who leads three or more employees, then this book is for you.It’s a super-short book that’ll help you avoid the common mistakes that new managers make when delegating tasks.It includes a comprehensive step-by-step process that tells you exactly what to do before delegation, during delegation, and after delegation.You’ll also get immediately applicable tactics that you can implement straightway with your subordinates. Here’s a partial list of what’s covered:  How to determine what to delegate to your employees before starting the delegation process The method you should follow to decide who to delegate work to on your team The five traits that every task should have before you delegate it.  How to describe authority levels the right way before you delegate work How to avoid micromanaging your employees How to check in with your subordinates and give them meaningful feedback.  How to avoid being too prescriptive, while still giving your employees a good description of what they need to accomplish The most important thing you should do after you delegate a task to verify understanding Some examples of delegation to help you understand the concepts better A downloadable sample delegation template and one-page cheat sheet that you can use as quick reference guides The book is divided into three sections that will serve as your new manager checklist:   Section I: Before Delegation Step One: Determine What to Delegate Step Two: Determine Who to Delegate to Section II: During Delegation Step One: Explain the Task Clearly Step Two: Describe Goals, Not Actions Step Three: Give Clear Timelines

    Essentially, you make an initial guess, and then get more data to improve it. Bayes Theorem, or Bayes Rule, has a ton of real world applications, from estimating your risk of a heart attack to making recommendations on Netflix But It Isn't That Complicated This book is a short introduction to Bayes Theorem. It is only 15 pages long, and is intended to show you how Bayes Theorem works as quickly as possible. The examples are intentionally kept simple to focus solely on Bayes Theorem without requiring that the reader know complicated probability distributions. If you want to learn the basics of Bayes Theorem as quickly as possible, with some easy to duplicate examples, this is a good book for you.

    Think of the familiar map of the New York City subway system, or a diagram of the human brain. Successful visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding.This book examines the methods of two dozen visualization experts who approach their projects from a variety of perspectives -- as artists, designers, commentators, scientists, analysts, statisticians, and more. Together they demonstrate how visualization can help us make sense of the world.Explore the importance of storytelling with a simple visualization exerciseLearn how color conveys information that our brains recognize before we're fully aware of itDiscover how the books we buy and the people we associate with reveal clues to our deeper selvesRecognize a method to the madness of air travel with a visualization of civilian air trafficFind out how researchers investigate unknown phenomena, from initial sketches to published papers Contributors include:Nick Bilton, Michael E. Driscoll, Jonathan Feinberg, Danyel Fisher, Jessica Hagy, Gregor Hochmuth, Todd Holloway, Noah Iliinsky, Eddie Jabbour, Valdean Klump, Aaron Koblin, Robert Kosara, Valdis Krebs, JoAnn Kuchera-Morin et al., Andrew Odewahn, Adam Perer, Anders Persson, Maximilian Schich, Matthias Shapiro, Julie Steele, Moritz Stefaner, Jer Thorp, Fernanda Viegas, Martin Wattenberg, and Michael Young.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data