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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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What Stands Out
Product Details
| Publisher | Springer |
| Publication date | July 30, 2022 |
| Edition | Second Edition 2021 |
| Language | English |
| Print length | 622 pages |
| ISBN-10 | 1071614207 |
| ISBN-13 | 978-1071614204 |
| Item Weight | 7.6 ounces (215.46 grams) |
| Dimensions | 6 x 1.25 x 9 inches (15.2 x 3.2 x 22.9 cm) |
| Part of series | Springer Texts in Statistics |
Who Should Buy?
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Data Scientists
Ideal for data scientists seeking to gain practical statistical knowledge and apply it using R programming.
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Graduate Students
Suitable for graduate students in statistics or related fields who need an introduction to statistical learning concepts.
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Statistical Analysts
Helpful for statistical analysts aiming to deepen their understanding of statistical methods and their applications in R.
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Beginners in Statistics
Not suitable for complete beginners, as prior knowledge in statistics and R programming is recommended.
Product Description
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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Intelligence & Semantics Editorial Review
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) is a highly acclaimed textbook published by Springer, offering a comprehensive overview of statistical learning techniques, particularly suited for aspiring data scientists. The authors, Hastie and Tibshirani, have crafted an accessible resource that balances theoretical depth and practical application, including R and Python editions. The second edition introduces new topics such as survival analysis and neural networks, vital for modern data analytics. The book is noted for its vivid figures and code examples, though some readers have mentioned a desire for more content on nonlinear regression. Overall, it serves as a strong foundation for understanding various statistical methods and their applications.
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Pros
- Accessible to aspiring data scientists and statisticians
- Includes R and Python editions for broader use
- Contains new chapters on advanced statistical methods
- Well-balanced between theory and practical application
- Vivid figures and ample sample code enhance learning
Cons
- Font size in print may be smaller than expected
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