Название: Business Intelligence, Analytics, Data Science, and AI: A Managerial Perspective, 5th Edition
Автор: Ramesh Sharda, Dursun Delen, Efraim Turban
Издательство: Pearson Education, Inc.
Год: 2024
Страниц: 727
Язык: английский
Формат: pdf (true)
Размер: 39.2 MB

Business Intelligence, Analytics, Data Science, and AI is your guide to the business-related impact of Artificial Intelligence (AI), Data Science and analytics, designed to prepare you for a managerial role. The text's vignettes and cases feature modern companies and non-profit organizations and illustrate capabilities, costs and justifications of BI across various business units. With coverage of many data science/AI applications, you'll explore tools, then learn from various organizations' experiences employing such applications. Ample hands-on practice is provided, can be completed with a range of software, and will help you use analytics as a future manager.

The 5th Edition integrates the fully updated content of Analytics, Data Science, and Artificial Intelligence, 11/e and Business Intelligence, Analytics, and Data Science, 4/e into one textbook, strengthened by 4 new chapters that will equip you for today's analytics and AI tech, such as ChatGPT. Examples explore analytics in sports, gaming, agriculture and “data for good.”

Hadoop is an open-source framework for processing, storing, and analyzing massive amounts of distributed, unstructured data. Originally created by Doug Cutting at Yahoo!, Hadoop was inspired by MapReduce, a user-defined function developed by Google in the early 2000s for indexing the Web. It was designed to handle petabytes and exabytes of data distributed over multiple nodes in parallel. Hadoop clusters run on inexpensive commodity hardware so projects can scale-out without breaking the bank. Hadoop is now a project of the Apache Software Foundation, where hundreds of contributors continuously improve the core technology. Fundamental concept: rather than banging away at one huge block of data with a single machine, Hadoop breaks up Big Data into multiple parts so each part can be processed and analyzed at the same time.

A related new style of database called NoSQL (Not Only SQL) has emerged to, like Hadoop, process large volumes of multistructured data. However, whereas Hadoop is adept at supporting large-scale, batch-style historical analysis, NoSQL databases are aimed, for the most part (though there are some important exceptions), at serving up discrete data stored among large volumes of multistructured data to end-user and automated Big Data applications. This capability is sorely lacking from relational database technology, which simply can’t maintain needed application performance levels at a Big Data scale. In some cases, NoSQL and Hadoop work in conjunction. The aforementioned HBase, for example, is a popular NoSQL database modeled after Google BigTable that is often deployed on top of HDFS, the Hadoop Distributed File System, to provide low-latency, quick lookups in Hadoop.

Artificial Intelligence (AI) is making a re-entrance into the world of computing and in our lives, this time far stronger and much more promising than before. This unprecedented re-emergence and the new level of expectations can largely be attributed to Deep Learning and cognitive computing. These two latest buzzwords define the leading edge of AI and Machine Learning today. Evolving out of the traditional artificial neural networks (ANN), Deep Learning is changing the very foundation of how Machine Learning works. Thanks to large collections of data and improved computational resources, Deep Learning is making a profound impact on how computers can discover complex patterns using the self-extracted features from the data (as opposed to a data scientist providing the feature vector to the learning algorithm).

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