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Titlebook: Apply Data Science; Introduction, Applic Thomas Barton,Christian Müller Book 2023 The Editor(s) (if applicable) and The Author(s), under ex

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發(fā)表于 2025-3-21 17:14:33 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Apply Data Science
期刊簡(jiǎn)稱Introduction, Applic
影響因子2023Thomas Barton,Christian Müller
視頻videohttp://file.papertrans.cn/161/160223/160223.mp4
發(fā)行地址Shows how data science can be used in practice.Provides an application-oriented introduction.Highlights the concrete operational benefit
圖書封面Titlebook: Apply Data Science; Introduction, Applic Thomas Barton,Christian Müller Book 2023 The Editor(s) (if applicable) and The Author(s), under ex
影響因子This book offers an introduction to the topic of data science based on the visual processing of data. It deals with ethical considerations in the digital transformation and presents a process framework for the evaluation of technologies. It also explains special features and findings on the failure of data science projects and presents recommendation systems in consideration of current developments. Machine learning functionality in business analytics tools is compared and the use of a process model for data science is shown..The integration of renewable energies using the example of photovoltaic systems, more efficient use of thermal energy, scientific literature evaluation, customer satisfaction in the automotive industry and a framework for the analysis of vehicle data serve as application examples for the concrete use of data science. The book offers important information that is just as relevant for practitioners as for students and teachers..
Pindex Book 2023
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Digital Ethics in Data-Driven Organizations and AI Ethics as Application Example of those technologies and provides principles for their responsible use. Data-driven organizations live from the value-creating data analysis for decision making. The ethically correct use of such technologies becomes a key factor thereby. This chapter introduces data-driven organizations, presents
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Integration of Renewable Energies—AI-Based Prediction Methods for Electricity Generation from Photovnts to consumers. The energy transition with the phase-out of nuclear power plants by 2022 and the expansion of renewable energies on the distribution grid level confronts low-voltage grids with challenges such as unmonitored overloads or voltage range violations. In addition, the volatility of load
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Machine Learning for Energy Management Optimizationization of energy management in residential buildings. This should be demonstrated using the example of the analysis of an air conditioning unit with active and passive heat recovery. It turns out that the intelligent, forward-looking control of such a system can save costs and resources. In a proje
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