Python for Accounting and Finance: An Integrative Approach to Using Python for Research

This book is a comprehensive guide to the application of Python in accounting, finance, and other business disciplines. This book is more than a Python tutorial; it is an integrative approach to using Python for practical research in these fields. The book begins with an introduction to Python and its key libraries. It then covers real-world applications of Python, covering data acquisition, cleaning, exploratory data analysis, visualization, and advanced topics like natural language processing, machine learning, predictive analytics, and deep learning. What sets this book apart is its unique blend of theoretical knowledge and real-world examples, supplemented with ready-to-use code. It doesn’t stop at the syntax; it shows how to apply Python to tackle actual analytical problems.

The book uses case studies to illustrate how Python can enhance traditional research methods in accounting and finance, not only allowing the reader to gain a firm understanding of Python programming but also equipping them with the skills to apply Python to accounting, finance, and broader business research. Whether you are a PhD student, a professor, an industry professional, or a financial researcher, this book provides the key to unlocking the full potential of Python in research.

Sunil Kumar, Ph.D., C.P.A., currently serves as an assistant professor of Accounting at the Mario J. Gabelli School of Business, Roger Williams University, USA. He is a seasoned academic with a diversified research portfolio. His innovative research approach is deeply rooted in the application of technology, distinguishing him in the academic field of accounting and finance. His command over Python and other advanced computational tools allows him to integrate complex technological processes into his research, enabling a nuanced and comprehensive understanding of accounting and finance phenomena. He is currently involved in several projects that use machine learning and deep learning approaches for predictive modeling in financial contexts, further emphasizing his commitment to leveraging technology in research.