Paperback : HK$559.00
Probability and Statistics for Data Science: Math + R + Data covers "math stat"-distributions, expected value, estimation etc.-but takes the phrase "Data Science" in the title quite seriously:
* Real datasets are used extensively.
* All data analysis is supported by R coding.
* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.
* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."
* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.
Prerequisites are calculus, some matrix algebra, and some experience in programming.
Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
Show moreProbability and Statistics for Data Science: Math + R + Data covers "math stat"-distributions, expected value, estimation etc.-but takes the phrase "Data Science" in the title quite seriously:
* Real datasets are used extensively.
* All data analysis is supported by R coding.
* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.
* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."
* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.
Prerequisites are calculus, some matrix algebra, and some experience in programming.
Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
Show more1. Basic Probability Models. 2. Discrete Random Variables. 3. Discrete Parametric Distribution Families. 4. Introduction to Discrete Markov Chains. 5. Continuous Probability Models. 6. The Family of Normal Distributions. 7. The Family of Exponential Distributions. 8. Random Vectors and Multivariate Distributions. 9. Statistics: Prologue. 10. Introduction to Confidence Intervals. 11. Introduction to Significance Tests. 12. General Statistical Estimation and Inference 13. Predictive Modeling
Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
"I quite like this book. I believe that the book describes itself
quite well when it says: Mathematically correct yet highly
intuitive…This book would be great for a class that one takes
before one takes my statistical learning class. I often run into
beginning graduate Data Science students whose background is not
math (e.g., CS or Business) and they are not ready…The book fills
an important niche, in that it provides a self-contained
introduction to material that is useful for a higher-level
statistical learning course. I think that it compares well with
competing books, particularly in that it takes a more "Data
Science" and "example driven" approach than more classical
books."
~Randy Paffenroth, Worchester Polytechnic Institute"This text by
Matloff (Univ. of California, Davis) affords an excellent
introduction to statistics for the data science student…Its
examples are often drawn from data science applications such as
hidden Markov models and remote sensing, to name a few… All the
models and concepts are explained well in precise mathematical
terms (not presented as formal proofs), to help students gain an
intuitive understanding."
~CHOICE
|
Ask a Question About this Product More... |
|
