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Data Analytics in Accounting is designed to prepare your students with the necessary tools and skills they need to successfully perform data analytics. Using Isson's data analytics model, the IMPACT Cycle, the authors provide a conceptual framework to help students think through the steps needed to provide data-driven insights and recommendations. Integrated in each chapter, labs provide multiple datasets and tutorials to give students hands-on experience working with different types of data and the tools used to analyze it. Students will conduct data analysis using Excel, Access (including SQL), Tableau, IDEA, XBRL, and Weka. And with Connect, an easy-to-use homework and learning management solution that embeds learning science and award-winning adaptive tools to improve student outcomes, instructors receive an innovative course solution that includes cutting-edge content and assessment paired with assignments that help students build the skills they need to succeed.

New Softcover Book. Text Only Version 2.0. 2020 Daily Shipping!

Large data sets arriving at every increasing speeds require a new set of efficient data analysis techniques. Data analytics are becoming an essential component for every organization and technologies such as health care, financial trading, Internet of Things, Smart Cities or Cyber Physical Systems. However, these diverse application domains give rise to new research challenges. In this context, the book provides a broad picture on the concepts, techniques, applications, and open research directions in this area. In addition, it serves as a single source of reference for acquiring the knowledge on emerging Big Data Analytics technologies.

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May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.53

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[ No Hassle 30 Day Returns ][ Ships Daily ] [ Underlining/Highlighting: NONE ] [ Writing: NONE ] [ Edition: 3rd ] Publisher: FlatWorld Pub Date: 8/1/2020 Binding: Paperback Pages: 410 3rd edition.

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Paperback. This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. The new edition integrates themes such as Word Embeddings, Transformer Models, and generative AI among the contents and offers new exercises in addition. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens. This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce.Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data.Understand core concepts behind Hadoop and cluster computingUse design patterns and parallel analytical algorithms to create distributed data analysis jobsLearn about data management, mining, and warehousing in a distributed context using Apache Hive and HBaseUse Sqoop and Apache Flume to ingest data from relational databasesProgram complex Hadoop and Spark applications with Apache Pig and Spark DataFramesPerform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib

Good condition ex-library book with usual library markings and stickers.