Python for Accounting and Finance
eBook - ePub

Python for Accounting and Finance

An Integrative Approach to Using Python for Research

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Python for Accounting and Finance

An Integrative Approach to Using Python for Research

About this book

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 Pythonprogramming 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.

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Yes, you can access Python for Accounting and Finance by Sunil Kumar in PDF and/or ePUB format, as well as other popular books in Business & Financial Engineering. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover
  2. Front Matter
  3. Part I. Introduction and Fundamentals
  4. Part II. Data Acquisition and Cleaning
  5. Part III. Exploratory Data Analysis and Visualization
  6. Part IV. Natural Language Processing and Text Analysis
  7. Part V. Machine Learning and Predictive Analytics
  8. Part VI. Advanced Topics
  9. Back Matter