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A step-by-step guide for using Python to transform abstract mathematical concepts into effective, on-the-ground scripts that solve real-world business problems
Applied Math with Python: Solve Real-World Problems with Python-Based Solutions is a detailed, step-by-step guide for business professionals, analysts, and data scientists interested in using Python to perform crucial organizational tasks: optimizing inefficient supply chains, calculating probabilities, forecasting financial performance, mining customer data for new insights, and more.
Author, researcher, and Assistant Professor of Finance at the University of North Florida, Blake Rayfield uses practical examples and hands-on exercises to explain how to combine concepts from optimization, probability, statistics, and other branches of mathematics with the Python language to solve difficult, common business problems. You’ll discover how marketing managers can use Python to create useful customer segments, how to model revenue growth, and how to allocate limited resources in a product launch or expansion.
Inside the book:
Modular, plug-and-play strategies for solving hard problems in Python in situations where a spreadsheet is inadequate
Instructions for building effective, scalable Python scripts incorporating many of the most powerful Python libraries, including Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, and Plotly
Start-to-finish coverage for business professionals – from building a Python scripting environment on your local computer or in a cloud environment to designing, writing, testing, and running a functional script
Perfect for entrepreneurs, analysts, managers, and professionals working in AI, Data Science, and finance, Applied Math with Python is an expert guide for transforming abstract mathematical concepts into useful, repeatable, scalable solutions you can put to work immediately in your team and in your organization.
Instead of overwhelming you with theory and proofs, you’ll find actionable examples and Python applications tailored for business leaders, financial analysts, and data-driven decision-makers. The information presented in this book bridges the gap between understanding mathematical concepts and applying them effectively in real-world business scenarios. Even though GenAI has greatly enhanced how businesses analyze data, you still need to bridge the knowledge gap to make the most of GenAI’s capabilities. You will learn to optimize operations, forecast financial performance, and uncover other insights from complex data
