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

    Railway Transpor tations and its Development 2. Railway Terminology 3. Railway Track 4. Stresses in Railway Track 5. Traction and Tractive Resistances 6. Rails 7. Rail Joints and Welding of Rails 8. Creep of Rails 9. Sleepers 10. Track Fittings and Fastenings 11. Ballast 12. Subgrade and Embankments 13. Track Alignments 14. Surveying 15. Geometric Design of the Track 16. Points and Crossings 17. Track Junctions 18. Stations and Yards 19. Equipment in Station Yards 20. Signaling and Control Systems 21. Interlocking of Signals and Points 22. Construction and Renewal of Track 23. Track Drainage 24. Conventional Maintenance of Track (or Manual Maintenance) 25. Railway Track Standards 26. Safety in Railways 27. Underground Railways and Tunnelling. PART- II MODERNIZATION OF RAILWAY TRACK AND FUTURE TRENDS 28. Modern Developments in Railways 29. Development of High and Super High Speeds 30. Modernization of Track for High Speeds 31. Modern Methods of Track Maintenance PART- III RAILWAY ADMINISTRATION, ECONOMICS AND FINANCE 32. Administration of Indian Railways 33. Railway Expenses, Rates and Fares 34. Material Management.

    But what if you could have your computer do them for you?In "Automate the Boring Stuff with Python," you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to: Search for text in a file or across multiple filesCreate, update, move, and rename files and foldersSearch the Web and download online contentUpdate and format data in Excel spreadsheets of any sizeSplit, merge, watermark, and encrypt PDFsSend reminder emails and text notificationsFill out online formsStep-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks.Don't spend your time doing work a well-trained monkey could do. Even if you've never written a line of code, you can make your computer do the grunt work. Learn how in "Automate the Boring Stuff with Python.""

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    You'll learn how to incorporate existing data sources into semantically aware applications and publish rich semantic data. Each chapter walks you through a single piece of semantic technology and explains how you can use it to solve real problems. Whether you're writing a simple mashup or maintaining a high-performance enterprise solution,Programming the Semantic Web provides a standard, flexible approach for integrating and future-proofing systems and data. This book will help you:Learn how the Semantic Web allows new and unexpected uses of data to emergeUnderstand how semantic technologies promote data portability with a simple, abstract model for knowledge representationBecome familiar with semantic standards, such as the Resource Description Framework (RDF) and the Web Ontology Language (OWL)Make use of semantic programming techniques to both enrich and simplify current web applications

    If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. You'll learn how to:Determine which data sources to use for collecting information Assess data quality and distinguish signal from noise Build basic data models to illuminate patterns, and assimilate new information into the models Cope with ambiguous information Design experiments to test hypotheses and draw conclusions Use segmentation to organize your data within discrete market groups Visualize data distributions to reveal new relationships and persuade others Predict the future with sampling and probability models Clean your data to make it useful Communicate the results of your analysis to your audience Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Data Analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

    It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpayd�n offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpayd�n, author of a popular textbook on machine learning, explains that as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.

    Internet giants, leading retailers, and other firms are voraciously gathering data with little oversight from anyone.In Las Vegas, no company knows the value of data better than Caesars Entertainment. Many thousands of enthusiastic clients pour through the ever-open doors of their casinos. The secret to the company’s success lies in their one unrivaled asset: they know their clients intimately by tracking the activities of the overwhelming majority of gamblers. They know exactly what games they like to play, what foods they enjoy for breakfast, when they prefer to visit, who their favorite hostess might be, and exactly how to keep them coming back for more.Caesars’ dogged data-gathering methods have been so successful that they have grown to become the world’s largest casino operator, and have inspired companies of all kinds to ramp up their own data mining in the hopes of boosting their targeted marketing efforts. Some do this themselves. Some rely on data brokers. Others clearly enter a moral gray zone that should make American consumers deeply uncomfortable.We live in an age when our personal information is harvested and aggregated whether we like it or not. And it is growing ever more difficult for those businesses that choose not to engage in more intrusive data gathering to compete with those that do. Tanner’s timely warning resounds: Yes, there are many benefits to the free flow of all this data, but there is a dark, unregulated, and destructive netherworld as well.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    . ." and "The data bear this out. . . ." But the field of statistics is not just about data. Statistics is the entire process involved in gathering evidence to answer questions about the world, in cases where that evidence happens to be numerical data. Statistics For Dummies is for everyone who wants to sort through and evaluate the incredible amount of statistical information that comes to them on a daily basis. (You know the stuff: charts, graphs, tables, as well as headlines that talk about the results of the latest poll, survey, experiment, or other scientific study.) This book arms you with the ability to decipher and make important decisions about statistical results, being ever aware of the ways in which people can mislead you with statistics. Get the inside scoop on number-crunching nuances, plus insight into how you canDetermine the odds Calculate a standard score Find the margin of error Recognize the impact of polls Establish criteria for a good survey Make informed decisions about experiments This down-to-earth reference is chock-full of real examples from real sources that are relevant to your everyday life: from the latest medical breakthroughs, crime studies, and population trends to surveys on Internet dating, cell phone use, and the worst cars of the millennium. Statistics For Dummies departs from traditional statistics texts, references, supplement books, and study guides in the following ways:Practical and intuitive explanations of statistical concepts, ideas, techniques, formulas, and calculations. Clear and concise step-by-step procedures that intuitively explain how to work through statistics problems. Upfront and honest answers to your questions like, "What does this really mean?" and "When and how I will ever use this?" Chances are, Statistics For Dummies will be your No. 1 resource for discovering how numerical data figures into your corner of the universe.

    This causes real problems for everyday users who need to make sense of all the information available, and for software engineers who want to make their text-based applications more useful and user-friendly. Whether building a search engine for a corporate website, automatically organizing email, or extracting important nuggets of information from the news, dealing with unstructured text can be daunting.Taming Text is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. It explores how to automatically organize text, using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. This book gives examples illustrating each of these topics, as well as the foundations upon which they are built.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

    Doron Swade, technology historian and assistant director of London's Science Museum, investigates the troubles that plagued 19th-century knowledge engineers in The Difference Engine: Charles Babbage and the Quest to Build the First Computer.The author is in a unique position to appreciate the technical difficulties of the time, as he led a team that built a working model of a Difference Engine, using contemporary materials, in time for Babbage's 1991 bicentenary. The meat of the book is comprised of the story of the first computing machine design as gathered from the technical notes and drawings curated by Swade. Though Babbage certainly had problems translating his ideas into brass, the reader also comes to understand his fruitless, drawn-out arguments with his funders. Swade had it comparatively easy, though his depictions of the frustrating search for money and then working out how best to build the enormous machine in the late 1980s are delightful.It is difficult--maybe impossible--to draw a clear, unbroken line of influence from Babbage to any modern computer researchers, but his importance both as the first pioneer and as a symbol of the joys and sorrows of computing is unquestioned. Swade clearly respects his subject deeply, all the more so for having tried to bring the great old man's ideas to life. The Difference Engine is lovingly comprehensive and will thrill readers looking for a more technical examination of Babbage's career. --Rob Lightner

    Despite his grim and bulky appearance, he introduced himself politely, in a soft-spoken voice whilst clutching a Bible in one of his large hands. She invited him in. The moment he stepped into her home, he lunged forwards, wrapping his over-sized fingers around her throat and forced her to the ground. She couldn’t scream. He had learned the dangers of a scream. She slowly slipped into darkness. Given what would follow, it was probably a kindness. The ‘Gorilla Killer’, Earle Nelson, roamed over 7,000 miles of North America undetected, whilst satisfying his deranged desires. During a span of almost two years, he choked the life out of more than twenty unsuspecting women, subjected their bodies to the most unspeakable acts, and seemingly enjoyed the process. The concept of Serial Killers were largely unknown to the North American public in the 1920s but the local authorities and press were fast becoming aware of the devastating and horrific reality that unfolded before their eyes. Nelson would eventually become the first real ‘superstar criminal’ who everyone had heard of and talked about. Before Bundy and BTK, there was Earle Nelson. Gorilla Killer is a chilling account of Earle Nelson, the first known American serial sex murderer. Ryan Green’s riveting narrative draws the reader into the real-live horror experienced by the victims and has all the elements of a classic thriller. CAUTION: This book contains descriptive accounts of abuse and violence. If you are especially sensitive to this material, it might be advisable not to read any further

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data