This book presents an introduction to data science analysis for railway track engineering and addresses common issues with the implementation of big data applications.  Emphasizing the theory and practical applications of big data and differential privacy, the book establishes the limitations, advantages, and disadvantages of these methods. Beginning with the foundations of data science, the author presents important applications and implementations and also highlights when a particular method and algorithm is most appropriate. For example, statistical and/or machine learning methods are appropriate for particular track monitoring data. The author presents numerous real-world examples throughout to illustrate how big data applications and predictive analytics are being used in railway engineering. Some examples include the Union Pacific Railroads use of big data approaches to improve operations by reducing train derailments, increasing velocity of shipments, and reducing emissions. Another example is the Bay Area Rapid Transits use of big data analytics to monitor and analyze train arrivals and departures, assess the flow of passengers based on ticket information, and provide an accurate projection of service schedules.  This book also features an overview of the software tools used to analyze the large amount of data obtained by railways, including popular open-source software framework, Apache Hadoop. This software supports applications with a computational algorithm called MapReduce and provides a distributed file system-Hadoop File Systems (HDFS) that stores data on computer nodes to facilitate rapid data transfers, aiding systems to continue uninterrupted operation.   

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