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

    Leading intelligence scholars Abram N. Shulsky and Gary J. Schmitt clearly explain such topics as the principles of collection, analysis, counterintelligence, and covert action, and their interrelationship with policymakers and democratic values. This new edition takes account of the expanding literature in the field of intelligence and deals with the consequences for intelligence of vast recent changes in telecommunication and computer technology the new “information age.” It also reflects the world’s strategic changes since the end of the Cold War. This landmark book provides a valuable framework for understanding today’s headlines, as well as the many developments likely to come in the real world of the spy.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    What's new in the Second Edition? 1. Fully compliant with the latest version of SPSS version 12 2. More coverage of advanced statistics including completely new coverage of non-parametric statistics. The book is 50 per cent longer than the First Edition. 3. Each section of each chapter now has a notation - 1,2 or 3 - referring to the intended level of study. This helps students navigate their way through the book and makes it user-friendly for students of ALL levels. 4. Has a 'how to use this book' section at the start of the text. 5. Characters in each chapter have defined roles - summarizing key points, to pose questions etc 6. Each chapter now has several examples for students to work through. Answers provided on the enclosed CD-ROM

    Complexity can be a terrible and overwhelming force, or it can be a delightful source of new energy and possibility-it all depends on your ability to escape the mindtraps we fall into and create new habits for a new world.

     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.

    Businesses across the world are having to face up to how they remain relevant in the choppy waters of the digital ocean. In an era where a YouTube star gets more daily impressions than Nike, Coca-Cola and Walmart combined, traditional marketing as we know it is dead. The End of Marketing revolutionizes the way brands, agencies and marketers should approach marketing. From how Donald Trump won the American presidency using social media and why Kim Kardashian is one of the world's biggest online brands, through to the impact of bots and automation, this book will teach you about new features and emerging platforms that will engage customers and employees. Discover bold content ideas, hear from some of the world's largest brands and content creators and find out how to build smarter paid-strategies, guaranteed to help you dominate your markets. The End of Marketing explains that no matter how easy it is to reach potential customers, the key relationship between brand and consumer still needs the human touch. Learn how to put 'social' back into social media and claim brand relevancy in a world where algorithms dominate, organic reach is dwindling and consumers don't want to be sold to, they want to be engaged.

    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

    In this, the fourth edition, Jonathan Baron retains the comprehensive attention to the key questions addressed in the previous editions - How should we think? What, if anything, keeps us from thinking that way? How can we improve our thinking and decision making? - and his expanded treatment of topics such as risk, utilitarianism, Baye's theorem, and moral thinking. With the student in mind, the fourth edition emphasizes the development of an understanding of the fundamental concepts in judgment and decision making. This book is essential reading for students and scholars in judgment and decision making and related fields, including psychology, economics, law, medicine, and business.

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

    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.

    A real life case study with sample project documents and diagrams is used to more practically explain these international tools, techniques, and lean principles to a broad range of practitioners, including: - Business analysts, systems analysts, developers and project managers - Entrepreneurs, product owners and product managers - Consultants, UX designers and marketing specialists - C-suite executives, investors and managers of companies of all sizes.

    For most of us, however, it doesn't come naturally and we haven't been taught how to do it well. Research shows a host of pitfalls trips us up when we try: We're quick to believe we understand a situation and jump to a flawed solution. We seek to confirm our hypotheses and ignore conflicting evidence. We view challenges incompletely through the frameworks we know instead of with a fresh pair of eyes. And when we communicate our recommendations, we forget our reasoning isn't obvious to our audience.How can we do it better? In Cracked It!, seasoned strategy professors and consultants Bernard Garrette, Corey Phelps and Olivier Sibony present a rigorous and practical four-step approach to overcome these pitfalls. Building on tried-and-tested (but rarely revealed) methods of top strategy consultants, research in cognitive psychology, and the latest advances in design thinking, they provide a step-by-step process and toolkit that will help readers tackle any challenging business problem. Using compelling stories and detailed case examples, the authors guide readers through each step in the process: from how to state, structure and then solve problems to how to sell the solutions. Written in an engaging style by a trio of experts with decades of experience researching, teaching and consulting on complex business problems, this book will be an indispensable manual for anyone interested in creating value by helping their organizations crack the problems that matter most.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    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.