Probability and Statistics for Data Science
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64 155 Ft
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Product details:
- Publisher Cambridge University Press
- Date of Publication 3 July 2025
- ISBN 9781009180085
- Binding Hardback
- No. of pages624 pages
- Size 254x178x33 mm
- Weight 1426 g
- Language English 684
Categories
Short description:
A self-contained introduction to probability and statistics for data science with examples involving real-world datasets.
MoreLong description:
This self-contained guide introduces two pillars of data science, probability theory, and statistics, side by side, in order to illuminate the connections between statistical techniques and the probabilistic concepts they are based on. The topics covered in the book include random variables, nonparametric and parametric models, correlation, estimation of population parameters, hypothesis testing, principal component analysis, and both linear and nonlinear methods for regression and classification. Examples throughout the book draw from real-world datasets to demonstrate concepts in practice and confront readers with fundamental challenges in data science, such as overfitting, the curse of dimensionality, and causal inference. Code in Python reproducing these examples is available on the book's website, along with videos, slides, and solutions to exercises. This accessible book is ideal for undergraduate and graduate students, data science practitioners, and others interested in the theoretical concepts underlying data science methods.
'Fernandez-Granda's Probability and Statistics for Data Science is a comprehensive yet approachable treatment of the fundamentals required of all aspiring Data Scientists-whether they be in academia, industry or elsewhere. The language is clear and precise, and it is one of the best-organized treatments of this material I have ever seen. With lucid examples and helpful exercises, it deserves to be the leading text for these topics among undergraduate and graduate students in this technical, fast-moving discipline. Instructors take note!' Arthur Spirling, Princeton University
Table of Contents:
Preface; Book Website; Introduction and Overview; 1. Probability; 2. Discrete variables; 3. Continuous variables; 4. Multiple discrete variables; 5. Multiple continuous variables; 6. Discrete and continuous variables; 7. Averaging; 8. Correlation; 9. Estimation of population parameters; 10. Hypothesis testing; 11. Principal component analysis and low-rank models; 12. Regression and classification; A. Datasets; References; Index.
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