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Record Nr. |
UNINA9910741366403321 |
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Autore |
Halkos George |
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Titolo |
Modeling Energy-Environment-Economy Interrelations |
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Pubbl/distr/stampa |
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Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2023 |
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Descrizione fisica |
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Soggetti |
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Research and information: general |
Physics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Understanding the interconnection between energy, the environment, and the economy is critical in contemporary society. The relationship between energy consumption, economic growth, and the environment has garnered significant attention from researchers and policymakers alike, and understanding the underlying causal connection between energy and the economy is crucial for modeling effective environmental and growth policies. Moreover, the emergence of new energy and environmental policies would create a plethora of further research opportunities in this evolving climate regime. For instance, several countries prioritize projects focused on energy efficiency, as they are considered low-risk, no-regret options that may yield economic benefits. In a nutshell, this Special Issue aims to address a gap in the existing literature by providing an in-depth analysis of the interplay between energy, the environment, and the economy, emphasizing the exploration of the potential environmental impacts of energy consumption and economic growth. |
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2. |
Record Nr. |
UNINA9911146600003321 |
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Autore |
Rogel-Salazar Jesus |
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Titolo |
Advanced data science and analytics with Python / / Jesús Rogel-Salazar |
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Pubbl/distr/stampa |
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ISBN |
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1-5231-4416-5 |
0-429-82231-6 |
0-429-44664-0 |
0-429-82232-4 |
9780429446641 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (424 pages) : illustrations |
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Collana |
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Chapman & Hall/CRC data mining & knowledge discovery series |
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Disciplina |
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Soggetti |
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Python (Computer program language) |
Databases |
Data mining |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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1.No Time To Lose: Time Series Analysis2.Speaking Naturally: Text and Natural Language Processing3.Let Us Get Social: Graph Theory and Social Network Analysis4.Thinking Deeply: Neural Networks and Deep Learning5.Here Is One I Made Earlier: Machine Learning Deployment |
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Sommario/riassunto |
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"Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader |
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can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences - in this case, literally to the users' fingertips in the form of an iPhone app. About the Author Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK." -- Publisher's description. |
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