Epilogue

This book was primarily targeted at data scientists early in their careers. It was assumed that readers of this book have knowledge of linear algebra and the basics of statistics, differential equations, fundamental numerical algorithms, data types, and data structures. Having said that, it must be realized that transforming a business problem into a mathematical formulation is an art.

While exploring the world of data science, it is important to understand the relevance of classical mathematical models and how they can be utilized along with ML models to solve business problems, often complex ones. Hybrid (blended) models enable better decision-making and become particularly essential for high-stake decisions in sensitive domains. Mathematical optimization typically elevates an ML model for the best interpretation of the connection between decision variables and relevant data and business objectives and of the optimal solution to the business problem. Nevertheless, simpler...