In today’s finance industry, data-driven decision-making is essential. Financial Data Science with Python: An Integrated Approach to Analysis, Modeling, and Machine Learning bridges the gap between traditional finance and modern data science, offering a comprehensive guide for students, analysts, and professionals.
This book equips readers with the tools to analyze complex financial data, build predictive models, and apply machine learning techniques to real-world financial challenges.
Beginning with foundational Python concepts, the author covers essential topics like data structures, object-oriented programming, and key libraries such as NumPy and Pandas. The book advances into more complex areas, including financial data processing, time series analysis with ARIMA and GARCH models, and both supervised and unsupervised machine learning methods tailored to finance. Practical techniques like regression, classification, and clustering are explored in a financial context.
A key feature is the hands-on approach. Through real-world examples, projects, and exercises, readers will apply Python to tasks like risk assessment, market forecasting, and financial pattern recognition. All code examples are provided in Jupyter Notebooks, enhancing interactivity.
Whether you’re a student building foundational skills, a financial analyst enhancing technical expertise, or a professional staying competitive in a data-driven industry, this book offers the knowledge and tools to succeed in financial data science.
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Da: BargainBookStores, Grand Rapids, MI, U.S.A.
Paperback or Softback. Condizione: New. Financial Data Science with Python: An Integrated Approach to Analysis, Modeling, and Machine Learning. Book. Codice articolo BBS-9781637428252
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Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New. Codice articolo 49989193-n
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Da: California Books, Miami, FL, U.S.A.
Condizione: New. Codice articolo I-9781637428252
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Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition. Codice articolo 49989193
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Da: Rarewaves USA, OSWEGO, IL, U.S.A.
Paperback. Condizione: New. In today's finance industry, data-driven decision-making is essential. Financial Data Science with Python: An Integrated Approach to Analysis, Modeling, and Machine Learning bridges the gap between traditional finance and modern data science, offering a comprehensive guide for students, analysts, and professionals.This book equips readers with the tools to analyze complex financial data, build predictive models, and apply machine learning techniques to real-world financial challenges.Beginning with foundational Python concepts, the author covers essential topics like data structures, object-oriented programming, and key libraries such as NumPy and Pandas. The book advances into more complex areas, including financial data processing, time series analysis with ARIMA and GARCH models, and both supervised and unsupervised machine learning methods tailored to finance. Practical techniques like regression, classification, and clustering are explored in a financial context.A key feature is the hands-on approach. Through real-world examples, projects, and exercises, readers will apply Python to tasks like risk assessment, market forecasting, and financial pattern recognition. All code examples are provided in Jupyter Notebooks, enhancing interactivity.Whether you're a student building foundational skills, a financial analyst enhancing technical expertise, or a professional staying competitive in a data-driven industry, this book offers the knowledge and tools to succeed in financial data science. Codice articolo LU-9781637428252
Quantità: Più di 20 disponibili
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condizione: new. Paperback. In today's finance industry, data-driven decision-making is essential. Financial Data Science with Python: An Integrated Approach to Analysis, Modeling, and Machine Learning bridges the gap between traditional finance and modern data science, offering a comprehensive guide for students, analysts, and professionals.This book equips readers with the tools to analyze complex financial data, build predictive models, and apply machine learning techniques to real-world financial challenges.Beginning with foundational Python concepts, the author covers essential topics like data structures, object-oriented programming, and key libraries such as NumPy and Pandas. The book advances into more complex areas, including financial data processing, time series analysis with ARIMA and GARCH models, and both supervised and unsupervised machine learning methods tailored to finance. Practical techniques like regression, classification, and clustering are explored in a financial context.A key feature is the hands-on approach. Through real-world examples, projects, and exercises, readers will apply Python to tasks like risk assessment, market forecasting, and financial pattern recognition. All code examples are provided in Jupyter Notebooks, enhancing interactivity.Whether you're a student building foundational skills, a financial analyst enhancing technical expertise, or a professional staying competitive in a data-driven industry, this book offers the knowledge and tools to succeed in financial data science. Bridging traditional finance and modern data science, this guide harnesses Python to analyze complex financial data and build predictive models. It explores key topics from programming fundamentals and time series analysis to real-world applications like risk assessment and market forecasting. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781637428252
Quantità: 1 disponibili
Da: Rarewaves.com USA, London, LONDO, Regno Unito
Paperback. Condizione: New. In today's finance industry, data-driven decision-making is essential. Financial Data Science with Python: An Integrated Approach to Analysis, Modeling, and Machine Learning bridges the gap between traditional finance and modern data science, offering a comprehensive guide for students, analysts, and professionals.This book equips readers with the tools to analyze complex financial data, build predictive models, and apply machine learning techniques to real-world financial challenges.Beginning with foundational Python concepts, the author covers essential topics like data structures, object-oriented programming, and key libraries such as NumPy and Pandas. The book advances into more complex areas, including financial data processing, time series analysis with ARIMA and GARCH models, and both supervised and unsupervised machine learning methods tailored to finance. Practical techniques like regression, classification, and clustering are explored in a financial context.A key feature is the hands-on approach. Through real-world examples, projects, and exercises, readers will apply Python to tasks like risk assessment, market forecasting, and financial pattern recognition. All code examples are provided in Jupyter Notebooks, enhancing interactivity.Whether you're a student building foundational skills, a financial analyst enhancing technical expertise, or a professional staying competitive in a data-driven industry, this book offers the knowledge and tools to succeed in financial data science. Codice articolo LU-9781637428252
Quantità: Più di 20 disponibili
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo FW-9781637428252
Quantità: 15 disponibili
Da: Revaluation Books, Exeter, Regno Unito
Paperback. Condizione: Brand New. 276 pages. 6.00x0.69x9.00 inches. In Stock. This item is printed on demand. Codice articolo __1637428251
Quantità: 2 disponibili
Da: Revaluation Books, Exeter, Regno Unito
Paperback. Condizione: Brand New. 261 pages. 9.00x6.00x9.00 inches. In Stock. Codice articolo x-1637428251
Quantità: 2 disponibili