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Название: Econometric Python: Harnessing Data Science for Economic Analysis: The Science of Pythonomics in 2024
Автор: Hayden Van Der Post, Johann Strauss
Издательство: Reactive Publishing
Год: 2024
Страниц: 416
Язык: английский
Формат: pdf
Размер: 30.3 MB
In the rapidly evolving landscape of economics, "Econometric Python" emerges as a groundbreaking guide, perfectly blending the intricate world of econometrics with the dynamic capabilities of Python. This book is crafted for economists, data scientists, researchers, and students who aspire to revolutionize their approach to economic data analysis. At its center, "Econometric Python" serves as a beacon for those navigating the complexities of econometric models, offering a unique perspective on applying Python's powerful data science tools in economic research. The book starts with a fundamental introduction to Python, focusing on aspects most relevant to econometric analysis. This makes it an invaluable resource for both Python novices and seasoned programmers. As the narrative unfolds, readers are led through a series of progressively complex econometric techniques, all demonstrated with Python's state-of-the-art libraries such as Pandas, NumPy, and statsmodels. Each chapter is meticulously designed to balance theory and practice, providing in-depth explanations of econometric concepts, followed by practical coding examples.
Автор: Hayden Van Der Post, Johann Strauss
Издательство: Reactive Publishing
Год: 2024
Страниц: 416
Язык: английский
Формат: pdf
Размер: 30.3 MB
In the rapidly evolving landscape of economics, "Econometric Python" emerges as a groundbreaking guide, perfectly blending the intricate world of econometrics with the dynamic capabilities of Python. This book is crafted for economists, data scientists, researchers, and students who aspire to revolutionize their approach to economic data analysis. At its center, "Econometric Python" serves as a beacon for those navigating the complexities of econometric models, offering a unique perspective on applying Python's powerful data science tools in economic research. The book starts with a fundamental introduction to Python, focusing on aspects most relevant to econometric analysis. This makes it an invaluable resource for both Python novices and seasoned programmers. As the narrative unfolds, readers are led through a series of progressively complex econometric techniques, all demonstrated with Python's state-of-the-art libraries such as Pandas, NumPy, and statsmodels. Each chapter is meticulously designed to balance theory and practice, providing in-depth explanations of econometric concepts, followed by practical coding examples.