Probability and Statistics for Data Science by Norman Matloff
This text is designed for a one-semester junior/senior/graduate-level calculus-based course on probability and statistics, aimed specifically at data science students (including computer science). In addition to calculus, the text assumes basic knowledge of matrix algebra and rudimentary computer programming.-
Introduction to NFL Analytics with R
- Deep Learning and Statistics
- Introduction to Data Science
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DevOps for Data Science
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Data Science in Healthcare
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What's the Question?
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Test-Driven Data Analysis
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Textual and Contextual Data Analysis
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Advanced Basketball Data Science
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Introduction to Classifier Performance Analysis with R
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Research Software Engineering
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Getting (more out of) Graphics
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JavaScript for Data Science
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Supervised Machine Learning for Text Analysis in R
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Massive Graph Analytics
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An Introduction to IoT Analytics
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Deep-Learning-Assisted Statistical Methods with Examples in R
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Natural Language Processing in the Real World
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Predictive Modelling for Football Analytics
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Models Demystified
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Cybersecurity Analytics
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Big Data Analytics
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Basketball Data Science
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Why Data Science Projects Fail
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Mathematical Engineering of Deep Learning
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Data Science for Water Utilities
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Real World AI Ethics for Data Scientists
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Data Science and Analytics Strategy
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Hands-On Data Science for Librarians
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Data Science
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Practitioner's Guide to Data Science
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Practitioner’s Guide to Data Science
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Spatial Statistics for Data Science
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Data Science in Practice
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The Data Preparation Journey
"I quite like this bookI 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
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.
| SKU | Unavailable |
| ISBN 13 | 9781138393295 |
| ISBN 10 | 1138393290 |
| Title | Probability and Statistics for Data Science |
| Author | Norman Matloff |
| Series | Chapman And Hall Crc Data Science Series |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Taylor & Francis Ltd |
| Year published | 2019-06-20 |
| Number of pages | 412 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |