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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 (NLP), 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.
“Python for Accounting and Finance” 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.
In the swiftly evolving domains of business, accounting, and finance, harnessing the power of technology to enhance and expand upon traditional research methodologies has become increasingly vital. The advent of robust programming languages like Python has revolutionized the field of data analysis, enabling more sophisticated, nuanced, and efficient examination of complex data.
This book is predicated on the premise that Python has emerged as a programming language of choice in both academic research and applied research. Its open-source nature, combined with an extensive collection of libraries, gives it a distinct edge over many conventional, often proprietary, software programs. The book guides the reader through the comprehensive offerings of Python, from handling an array of data formats, including structured and unstructured data, to employing its advanced Machine Learning and Artificial Intelligence capabilities for predictive analytics.
The initial section of this book offers a solid foundation in Python, covering its fundamentals and key libraries, and regular expressions. The subsequent sections progressively delve into more specialized applications, beginning with data acquisition and cleaning, before moving on to exploratory data analysis and visualization, Natural Language Processing, Machine Learning, and predictive analytics. Each section is meticulously crafted, presenting a judicious blend of theoretical knowledge and practical applications.
One of the standout features of “Python for Accounting and Finance” is its focus on real-world case studies and practical examples, supplemented with ready-to-use codes for most of the activities involved for research in these disciplines. This approach enables readers to contextualize and apply their learning immediately. The book is also replete with exercises that provide hands-on experience, reinforcing the concepts and techniques presented.
Contents:
Part I. Introduction and Fundamentals
Part II. Data Acquisition and Cleaning
Part III. Exploratory Data Analysis and Visualization
Part IV. Natural Language Processing and Text Analysis
Part V. Machine Learning and Predictive Analytics
Part VI. Advanced Topics