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Item #:
59936510
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Item #:
59936510
GHS 861
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
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What Stands Out
Comprehensive Coverage
Offers an extensive introduction to statistical learning techniques, integrating theoretical foundations and practical applications, making it ideal for both beginners and experienced practitioners.
R Integration
Incorporates R programming for hands-on data analysis, empowering readers to apply learned concepts directly in real-world scenarios, enhancing understanding of statistical methods.
Updated Content
The second edition includes updated examples and new applications, ensuring relevance and alignment with current trends in data science, preparing readers for contemporary challenges.
Product Details
- Provides an accessible overview of statistical learning for analyzing complex data sets in various fields
- Covers important modeling and prediction techniques with real-world applications
- Includes tutorials on implementing analyses and methods in R, a popular statistical software platform
- Written by authors known for their work in statistics and machine learning
- Targets statisticians and non-statisticians interested in using statistical learning techniques
- Features new chapters on deep learning, survival analysis, and multiple testing in the second edition
| 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?
-
Data Scientists
Ideal for data scientists seeking to gain practical statistical knowledge and apply it using R programming.
-
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
Product Price History
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GHS 861
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Features & Benefits
- Accessible overview of Statistical Learning
- Important modeling and prediction techniques covered
- Real-world examples to illustrate the methods
- Includes tutorials on implementing analyses and methods using R
- Suitable for statisticians and non-statisticians
- New chapters on deep learning, survival analysis and multiple testing in 2nd edition
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