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Overview: 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. A related new style of database called NoSQL (Not Only SQL) has emerged to, like Hadoop, process large volumes of multistructured data. 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).
Genre: Non-Fiction > Tech & Devices
Contents:
An Overview of Business Intelligence, Analytics, Data Science, and AI
Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications
Descriptive Analytics I: Nature of Data, Big Data, and Statistical Modeling
Descriptive Analytics II: Business Intelligence Data Warehousing, and Visualization
Predictive Analytics I: Data Mining Process, Methods, and Algorithms
Predictive Analytics II: Text, Web, and Social Media Analytics
Deep Learning and Cognitive Computing
Prescriptive Analytics: Optimization and Simulation
Landscape of Business Analytics Tools
AI-Based Trends in Analytics and Data Science
Ethical, Privacy, and Managerial Considerations in Analytics
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