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161209518
Mathematical Methods in Data Science: Bridging Theory and Applications with Python (Cambridge Mathematical Textbooks)
Item #:
161209518
XPF 10139
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Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science.
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- Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.
| Publisher | Cambridge University Press |
| Publication date | 30 Oct. 2025 |
| Language | English |
| Print length | 582 pages |
| ISBN-10 | 1009509403 |
| ISBN-13 | 978-1009509404 |
| Item weight | 1.2 kg |
| Dimensions | 17.78 x 3.35 x 25.4 cm |
| Part of series | Cambridge Mathematical Textbooks |
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Mathematical Methods in Data Science: Bridging Theory and Applications with Python (Cambridge Mathematical Textbooks)
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Features & Benefits
- Comprehensive introduction to mathematical methods in data science.
- Focus on linear algebra, calculus, optimization, probability, and statistics.
- Hands-on learning with practical examples implemented in Python.
- Includes self-assessment quizzes and theoretical problems for skill development.
- Suitable for advanced undergraduates and beginning graduate students.
- Bridges the gap for mathematics majors into data science applications.
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