Data Science: Theory and Practice

This book aims to collect and consolidate innovative, high-quality research contributions on data science applications for solving real-world problems. In addition, it provides insights into recent advances in data science, including original scientific contributions such as theoretical foundations, models, experimental research, and case studies for developing or applying data science techniques to address problems across several domains. This book includes 24 chapters related to the application of data science tools and techniques like deep learning methods, vision transformers, Large Language Model-based tools, genetic algorithms, semantic technologies, simulated annealing-based optimization, to mention just a few, for solving real-world problems like analysis and prediction of residential solar clean energy generation, career guidance forecasting, didactic instrumentation design for higher education, detection of suicidal ideation on social media, classification of wet and dry of age-related macular degeneration in fundus images, rate of penetration prediction in oil well drilling, forecast sheep milk production, and preservation of traditional medicine.