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
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.
Kevin O’Leary shares invaluable secrets on entrepreneurship, business, money and life. Can you make millions just by “visualizing yourself rich” as some business prophets suggest? Don’t buy it, says Kevin O’Leary. If you want to be a successful entrepreneur and amass wealth, you’re going to have to work for it. But the good news is: with the right guidance, focus and perseverance, you can turn entrepreneurial vision into lucrative reality and have the personal freedom that only wealth can buy. Kevin O’Leary would know. The much-feared and revered Dragon on the immensely popular show Dragons’ Den (and Shark Tank in the U.S.) started his company in his basement with a $10,000 loan from his financially savvy mother. A few years later, Kevin sold that company for more than four billion dollars. In this compelling, candid and, above all else, brutally honest business memoir, Kevin provides engaging, practical advice and lessons that will give anyone a distinct competitive edge.
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.
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. 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 advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates
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
Data mining can be defined as the process of selection, explorationand modelling of large databases, in order to discover models andpatterns. The increasing availability of data in the currentinformation society has led to the need for valid tools for itsmodelling and analysis. Data mining and applied statistical methodsare the appropriate tools to extract such knowledge from data.Applications occur in many different fields, including statistics,computer science, machine learning, economics, marketing andfinance. This book is the first to describe applied data mining methodsin a consistent statistical framework, and then show how they canbe applied in practice. All the methods described are eithercomputational, or of a statistical modelling nature. Complexprobabilistic models and mathematical tools are not used, so thebook is accessible to a wide audience of students and industryprofessionals. The second half of the book consists of nine casestudies, taken from the author's own work in industry, thatdemonstrate how the methods described can be applied to realproblems. Provides a solid introduction to applied data mining methods ina consistent statistical framework Includes coverage of classical, multivariate and Bayesianstatistical methodology Includes many recent developments such as web mining,sequential Bayesian analysis and memory based reasoning Each statistical method described is illustrated with real lifeapplications Features a number of detailed case studies based on appliedprojects within industry Incorporates discussion on software used in data mining, withparticular emphasis on SAS Supported by a website featuring data sets, software andadditional material Includes an extensive bibliography and pointers to furtherreading within the text Author has many years experience teaching introductory andmultivariate statistics and data mining, and working on appliedprojects within industry A valuable resource for advanced undergraduate and graduatestudents of applied statistics, data mining, computer science andeconomics, as well as for professionals working in industry onprojects involving large volumes of data - such as in marketing orfinancial risk management.
