Probability and statistics for data science : (Record no. 45247)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01849nam a2200205 i 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780367260934 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781138393295 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1138393290 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.5 |
| Item number | MAT |
| 100 1# - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Matloff, Norman S., |
| 245 10 - TITLE STATEMENT | |
| Title | Probability and statistics for data science : |
| Statement of responsibility, etc | Norman Matloff. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Boca Raton : |
| Name of publisher | CRC Press, Taylor & Francis Group, |
| Year of publication | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xxxii, 412 pages ; |
| Other physical details | illustrations ; 24 cm. |
| 490 ## - SERIES STATEMENT | |
| Series statement | Series in computer science and data analysis |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | <br/>1. 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 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Probabilities |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Mathematical statistics |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Reference Books |
| Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Reference | Main Library | Main Library | Reference | 31/10/2023 | Purchase | 25645.00 | 519.5 MAT | 016648 | Reference Books |