Python for Financial Modeling: Practical Techniques from Data Analysis to Deep Learning (eBook, ePUB)
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Python for Financial Modeling: Practical Techniques from Data Analysis to Deep LearningThis comprehensive guide bridges the worlds of finance and data science, providing financial professionals with practical Python techniques for modern quantitative analysis. From data acquisition and preprocessing to advanced machine learning models, readers will learn how to implement powerful financial modeling tools using Python's robust ecosystem. The book covers essential topics including time series analysis, portfolio optimization, risk assessment, algorithmic trading strategies, and deep learning app...
Python for Financial Modeling: Practical Techniques from Data Analysis to Deep Learning
This comprehensive guide bridges the worlds of finance and data science, providing financial professionals with practical Python techniques for modern quantitative analysis. From data acquisition and preprocessing to advanced machine learning models, readers will learn how to implement powerful financial modeling tools using Python's robust ecosystem. The book covers essential topics including time series analysis, portfolio optimization, risk assessment, algorithmic trading strategies, and deep learning applications in finance. With a hands-on approach, readers will master libraries such as pandas, NumPy, scikit-learn, and TensorFlow while building real-world financial models. Whether you're a financial analyst, quantitative researcher, or aspiring data scientist, this book provides the practical skills needed to leverage Python's capabilities for data-driven financial decision-making. Each chapter includes executable code examples, case studies, and best practices to help readers immediately apply these techniques to their own financial projects.
This comprehensive guide bridges the worlds of finance and data science, providing financial professionals with practical Python techniques for modern quantitative analysis. From data acquisition and preprocessing to advanced machine learning models, readers will learn how to implement powerful financial modeling tools using Python's robust ecosystem. The book covers essential topics including time series analysis, portfolio optimization, risk assessment, algorithmic trading strategies, and deep learning applications in finance. With a hands-on approach, readers will master libraries such as pandas, NumPy, scikit-learn, and TensorFlow while building real-world financial models. Whether you're a financial analyst, quantitative researcher, or aspiring data scientist, this book provides the practical skills needed to leverage Python's capabilities for data-driven financial decision-making. Each chapter includes executable code examples, case studies, and best practices to help readers immediately apply these techniques to their own financial projects.
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