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Nº de artículo:
125854189
Financial Data Analytics with Machine Learning, Optimization and Statistics (Wiley Finance)
87% of respondents would recommend this to a friend
Nº de artículo:
125854189
NIO 3009
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An essential introduction to data analytics and Machine Learning techniques in the business sector.
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What Stands Out
Comprehensive Approach
Integrates financial data analytics with machine learning, optimization, and statistics, providing a holistic perspective for financial professionals to enhance their analytical capabilities.
Practical Applications
Focuses on real-world applications and case studies, helping readers apply advanced techniques to solve financial problems and make data-driven decisions effectively.
Expert Insights
Authored by industry experts, offering valuable knowledge and best practices that equip readers with the tools needed to excel in the competitive finance landscape.
Detalles de producto
| Publisher | Wiley |
| Publication date | November 12, 2024 |
| Edition | 1st |
| Language | English |
| Print length | 816 pages |
| ISBN-10 | 1119863376 |
| ISBN-13 | 978-1119863373 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 7.1 x 2.4 x 9.7 inches (18 x 6.1 x 24.6 cm) |
Who Should Buy?
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Data Analysts
Ideal for data analysts seeking to enhance their skills in financial data using machine learning and statistics.
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Finance Students
Beneficial for finance students looking for practical applications of analytics and optimization in financial contexts.
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Investment Professionals
Useful for investment professionals wanting to leverage data-driven insights for informed decision-making and strategies.
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Beginners
Not suitable for beginners lacking foundational knowledge in finance or data science principles.
DESCRIPCIÓN DEL PRODUCTO
Financial Data Analytics with Machine Learning, Optimization and Statistics (Wiley Finance)
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Probability & Statistics Editorial Review
The "Financial Data Analytics with Machine Learning, Optimization and Statistics" serves as a valuable resource for both students and professionals interested in the intersection of finance and data analysis. The balance between theoretical knowledge and practical application is a highlight of this book, appealing to readers who want a comprehensive understanding of data analytics methodologies in financial contexts. Many reviews emphasize the book's effective integration of R and Python code, enhancing readers' ability to practically apply what they learn. This hands-on approach has received particular praise from college students studying for actuarial exams, as it fills a noticeable gap in existing study materials. However, there are some critiques regarding access to supplementary content, specifically the code provided by the authors, which some readers found challenging to obtain initially. On the technical side, while the book is well-structured, users noted that the grayscale illustrations could detract from understanding the material and wished for better visual representation. Despite these minor setbacks, the overall reception is overwhelmingly positive, with many declaring it an essential addition for anyone in quantitative disciplines or preparing for advanced exams. **
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ventajas
- Strong balance of theory and practical application.
- Comprehensive coverage of relevant topics in finance and data analytics.
- Effective use of R and Python for real-world coding practice.
- Highly recommended for actuarial students and professionals in quantitative careers.
Contras
- Difficulties accessing supplementary code at first.
Product Price History
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características y beneficios
- Comprehensive resource combining financial statistics and machine learning.
- Focuses on practical applications in finance and insurance industries.
- Equips readers to evaluate data quality for informed financial decisions.
- Incorporates Python and R for hands-on implementation of algorithms.
- Covers competencies for professional examinations in actuarial science.
- Ideal for students and professionals in quantitative finance and data science.
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