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What's life really like on a fly-in-fly-out (FIFO) mine? In 2012, after touring his comedy shows through Europe, stand-up comedian Xavier Toby was broke and decided to take a job on a remote minesite to pay the bills. In his memoir, Mining My Own Business, Xavier Toby is onsite somewhere in Australia working in admin to pay off his credit card debt. Damo, Pando, Jonno, Robbo, Donk, Jokka and Dale are just some of the other blokes earning a crust, attending endless safety briefings, swapping tall tales and 'missing' the missus out there in the middle of nowhere. With Xavier, FIFO is not life on hold - it is life in hilarious overdrive.
Practical guide for organization leaders, top-level executives. Industry experts explain in clear, understandable English. What data mining and predictive analytics are
Leverage Unstructured Data to Become More Competitive, Responsive, and Innovative In Mining the Talk, two leading-edge IBM researchers introduce a revolutionary new approach to unlocking the business value hidden in virtually any form of unstructured data–from word processing documents to websites, emails to instant messages. The authors review the business drivers that have made unstructured data so important–and explain why conventional methods for working with it are inadequate. Then, writing for business professionals–not just data mining specialists–they walk step-by-step through exploring your unstructured data, understanding it, and analyzing it effectively. Next, you’ll put IBM’s techniques to work in five key areas: learning from your customer interactions; hearing the voices of customers when they’re not talking to you; discovering the “collective consciousness” of your own organization; enhancing innovation; and spotting emerging trends. Whatever your organization, Mining the Talk offers you breakthrough opportunities to become more responsive, agile, and competitive. Identify your key information sources and what can be learned about them Discover the underlying structure inherent in your unstructured information Create flexible models that capture both domain knowledge and business objectives Create visual taxonomies: “pictures” of your data and its key interrelationships Combine structured and unstructured information to reveal hidden trends, patterns, and relationships Gain insights from “informal talk” by customers and employees Systematically leverage knowledge from technical literature, patents, and the Web Establish a sustainable process for creating continuing business value from unstructured data Preface xv Acknowledgements xx Chapter 1: Introduction 1 Chapter 2: Mining Customer Interactions 21 Chapter 3: Mining the Voice of the Customer 71 Chapter 4: Mining the Voice of the Employee 93 Chapter 5: Mining to Improve Innovation 111 Chapter 6: Mining to See the Future 133 Chapter 7: Future Applications 163 Appendix: The IBM Unstructured Information Modeler Users Manual 171
Aiming at building efficient radiology operations, this book walks the reader through the entire radiology workflow, from the moment that the examination is requested to the reporting of findings. Using their practical experience, the authors draw attention to the many elements that can go wrong at each step, and explain how critical analysis and objective metrics can be used to fix broken processes. Readers will learn how to measure the efficiency of their workflows, where to find relevant data, and how to use it in the most productive ways. The book also addresses how data can be turned into insightful operational information to produce organizational change. All aspects of radiology operations are considered including ordering, scheduling, protocols, checking-in, image acquisition, image interpretation, communication, and billing. The closing section provides a deeper dive into the advanced tools and techniques that are used to analyze operations, including queuing theory, process mining and artificial intelligence.
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R
Want to start the small business of your dreams? Want to breathe new life into the one you already have? Small Business For Dummies, 3rd Edition provides authoritative guidance on every aspect of starting and growing your business, from financing and budgeting to marketing, management and beyond. This completely practical, no-nonsense guide gives you expert advice on everything from generating ideas and locating start-up money to hiring the right people, balancing the books, and planning for growth. You’ll get plenty of help in ramping up your management skills, developing a marketing strategy, keeping your customers loyal, and much more. You’ll also find out to use the latest technology to improve your business’s performance at every level. Discover how to: Make sure that small-business ownership is for you Find your niche and time your start-up Turn your ideas into plans Determine your start-up costs Obtain financing with the best possible terms Decide whether or not to incorporate Make sense of financial statements Navigate legal and tax issues Buy an existing business Set up a home-based business Publicize your business and market your wares Keep your customers coming back for more Track cash flow, costs and profits Keep your business in business and growing You have the energy, drive, passion, and smarts to make your small business a huge success. Small Business For Dummies, 3rd Edition, provides the rest.
Learn How to Properly Use the Latest Analytics Approaches in Your Organization Computational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies. The book first covers core descriptive and inferential statistics for analytics. The author then enhances numerical statistical techniques with symbolic artificial intelligence (AI) and machine learning (ML) techniques for richer predictive and prescriptive analytics. With a special emphasis on methods that handle time and textual data, the text: Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks Embeds decision trees within influence diagrams Augments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks These approaches are not replacements of traditional statistics-based analytics; rather, in most cases, a generalized technique can be reduced to the underlying traditional base technique under very restrictive conditions. The book shows how these enriched techniques offer efficient solutions in areas, including customer segmentation, churn prediction, credit risk assessment, fraud detection, and advertising campaigns.
Marketing analysts use data mining techniques to gain a reliable understanding of customer buying habits and then use that information to develop new marketing campaigns and products. Visual mining tools introduce a world of possibilities to a much broader and non-technical audience to help them solve common business problems. Explains how to select the appropriate data sets for analysis, transform the data sets into usable formats, and verify that the sets are error-free Reviews how to choose the right model for the specific type of analysis project, how to analyze the model, and present the results for decision making Shows how to solve numerous business problems by applying various tools and techniques Companion Web site offers links to data visualization and visual data mining tools, and real-world success stories using visual data mining