Mathematical modeling is the art of transforming a business problem into a clear mathematical formulation. For a given problem or use case, the algorithmic implementation of a model helps optimize parameters and generate better insights and comprehension to enable decision-making. A mathematical model complements a machine learning model and supports high-stake decisions in sensitive domains such as medicine, for example.

There are three focal topics to help you understand mathematical modeling:

• Areas where a mathematical model is useful – for example, control engineering and signal processing
• Tested Python-based mathematical tools – for example, graph theory and MCMC
• Underlying algorithms of mathematical optimization

I will provide concepts of mathematical modeling and various approaches to modeling through this book. I will guide you in choosing the optimal technique and best-suited algorithm to solve a business problem using Python, based on two main sources of information:

• My experience from the past 5 years as a data scientist and application developer for businesses
• My academic research (at different stages of maturity) across science disciplines for a decade

As a data professional, I believe mathematical models (equation-driven) with objectives and constraints in a problem are as relevant as (data-driven) machine learning models. In some cases, the right combination of both yields the best solutions.