The first of the two volumes, the book highlights the multidisciplinary nature of data science in the fields of computer science, statistics, physics and economics. It meticulously guides its readers through the data science workflow, covering data collection, preparation, storage, analysis, management and visualization. It highlights specific techniques and algorithms used in each of the above-mentioned stages and offers explanations of major learning mechanisms: dimensionality reduction, classification, clustering and outlier analysis. Additionally, it sheds light on the modern field of deep learning and unfolds the complexity of its mechanism with explanation. Case studies showcase the practical applications and successes of data science across various domains.
The first of the two volumes, the book highlights the multidisciplinary nature of data science in the fields of computer science, statistics, physics and economics. It meticulously guides its readers through the data science workflow, covering data collection, preparation, storage, analysis, management and visualization. It highlights specific techniques and algorithms used in each of the above-mentioned stages and offers explanations of major learning mechanisms: dimensionality reduction, classification, clustering and outlier analysis. Additionally, it sheds light on the modern field of deep learning and unfolds the complexity of its mechanism with explanation. Case studies showcase the practical applications and successes of data science across various domains.

Data Science and Cases in Sustainability: Pattern Recognition and Machine Learning
406
Data Science and Cases in Sustainability: Pattern Recognition and Machine Learning
406Hardcover
Product Details
ISBN-13: | 9789819683611 |
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Publisher: | Springer Nature Singapore |
Publication date: | 08/17/2025 |
Series: | Mathematics for Sustainable Developments |
Pages: | 406 |
Product dimensions: | 6.10(w) x 9.25(h) x (d) |