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Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition
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What Stands Out
Product Details
| Publisher | Packt Publishing |
| Publication date | April 30, 2019 |
| Edition | 2nd ed. |
| Language | English |
| Print length | 426 pages |
| ISBN-10 | 1789346460 |
| ISBN-13 | 978-1789346466 |
| Item Weight | 1.77 pounds (800 grams) |
| Dimensions | 7.5 x 0.96 x 9.25 inches (19.1 x 2.4 x 23.5 cm) |
Who Should Buy?
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Finance Professionals
Ideal for finance professionals seeking to enhance their analytical skills using advanced Python applications for financial data.
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Data Scientists
Great for data scientists looking to apply statistical methods and machine learning techniques in financial contexts.
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Students and Learners
Beneficial for students studying finance or data analysis, providing practical Python skills in finance applications.
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Beginners in Python
Not suitable for beginners as it assumes prior knowledge of Python programming and financial concepts.
Product Description
Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition
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Python Editorial Review
Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition is a comprehensive resource for those looking to delve into financial computing through Python. While the book covers essential financial concepts and mathematics, some readers feel it lacks depth in coding examples and practical applications, particularly in utilizing Jupyter Notebook effectively. Furthermore, critical API coding and database access details are reportedly overlooked, leaving a gap for beginners who might need more guidance. Despite its challenges, the book provides a solid foundation for understanding finance in a programming context, appealing to those who are willing to seek additional resources to complement their learning.
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Pros
- Covers essential financial concepts and mathematics
- Good introduction to Python for finance
- Detailed explanations of statistical applications
- Encourages learning of Jupyter Notebook
- Useful for advanced users wanting to explore finance
Cons
- Missing practical coding examples and API details
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