Buch, Englisch, 358 Seiten, Format (B × H): 174 mm x 246 mm
A Mind-Mapping Approach
Buch, Englisch, 358 Seiten, Format (B × H): 174 mm x 246 mm
ISBN: 978-1-041-10814-6
Verlag: Taylor & Francis Ltd
Python for Accounting and Finance: A Mind-Mapping Approach is an innovative textbook written for accounting and finance students and professionals with no prior coding experience. It introduces Python programming through a mind mapping approach that presents complex concepts in a clear and structured way to support understanding and retention.
The book is organised into four parts: Python Basics, Data Analysis and Visualisation, Automation, and Machine Learning. It places programming within practical accounting and finance contexts so that learners can see the direct application of coding in their field. The textbook uses mind maps as a core instructional method. These visual diagrams show programming concepts and their connections, helping learners to understand, organise and recall information, particularly those who learn through visual representation.
Enhanced with Google Colab notebooks, this book creates a highly supportive learning experience, equipping students of accounting and finance with essential programming skills for their speciality.
Zielgruppe
Postgraduate and Undergraduate Advanced
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
- Wirtschaftswissenschaften Betriebswirtschaft Unternehmensfinanzen Betriebliches Rechnungswesen
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
Weitere Infos & Material
1. Introduction to Python and Mind Mapping Approach 2. Fundamentals of Python 3. Control Structures 4. Data Structures and Python Tools 5. Working with DataFrames 6. Exploratory Data Analysis 7. Time Series and Panel Data Analysis 8. Data Visualisation Techniques 9. Automating Tasks 10. Web Scraping and Accessing Cryptocurrency Data 11. Introduction to Machine Learning 12. Supervised Learning 13. Unsupervised Learning 14. Advanced Machine Learning and Financial Modelling




