Reseña

Artificial intelligence and data science are at the forefront of technological innovation, driving advancements across various sectors, including healthcare, finance, and transportation. As these fields continue to expand, there is a growing need for systematic research methodologies that ensure the validity and reliability of findings. This book outlines a general approach to scientific research methodology tailored to the unique challenges and opportunities presented by AI and data science. Scientific research methodology is traditionally grounded in the principles of hypothesis testing, empirical observation, and reproducibility. In AI and data science, these principles are augmented by computational techniques such as machine learning algorithms, data mining, and statistical analysis. The theoretical foundations of this approach involve understanding the interplay between data-driven insights and theoretical models, ensuring that computational methods are applied in a scientifically rigorous manner