MARC details
| 000 -LEADER |
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| fixed length control field |
05193cam a2200469 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
22152014 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240902141634.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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m |o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr_||||||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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210721s2021 flu ob 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
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| LC control number |
2021019164 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781000462937 |
| Qualifying information |
(epub) |
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| International Standard Book Number |
9781003159834 |
| Qualifying information |
(ebook) |
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| Canceled/invalid ISBN |
9780367748456 |
| Qualifying information |
(hardback) |
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| Canceled/invalid ISBN |
9780367748432 |
| Qualifying information |
(paperback) |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Transcribing agency |
DLC |
| Description conventions |
rda |
| Modifying agency |
DLC |
| 042 ## - AUTHENTICATION CODE |
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| Authentication code |
pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA276.4 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
519.50285 AGR |
| Edition number |
23 |
| Item number |
24722 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Agresti, Alan, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Foundations of statistics for data scientists : |
| Remainder of title |
with R and Python / |
| Statement of responsibility, etc. |
Alan Agresti and Maria Kateri. |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
1st edition. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
Boca Raton : |
| Name of producer, publisher, distributor, manufacturer |
CRC Press, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2021. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 online resource |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
computer |
| Media type code |
c |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
online resource |
| Carrier type code |
cr |
| Source |
rdacarrier |
| 490 0# - SERIES STATEMENT |
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| Series statement |
Chapman & Hall/CRC texts in statistical science |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
"Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises. Alan Agresti, Distinguished Professor Emeritus at the University of Florida, is the author of seven books, including Categorical Data Analysis (Wiley) and Statistics: The Art and Science of Learning from Data (Pearson), and has presented short courses in 35 countries. His awards include an honorary doctorate from De Montfort University (UK) and the Statistician of the Year from the American Statistical Association (Chicago chapter). Maria Kateri, Professor of Statistics and Data Science at the RWTH Aachen University, authored the monograph Contingency Table Analysis: Methods and Implementation Using R (Birkhäuser/Springer) and a textbook on mathematics for economists (in German). She has a long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, and Business Administration and Engineering. "The main goal of this textbook is to present foundational statistical methods and theory that are relevant in the field of data science. The authors depart from the typical approaches taken by many conventional mathematical statistics textbooks by placing more emphasis on providing the students with intuitive and practical interpretations of those methods with the aid of R programming codes...I find its particular strength to be its intuitive presentation of statistical theory and methods without getting bogged down in mathematical details that are perhaps less useful to the practitioners" (Mintaek Lee, Boise State University) "The aspects of this manuscript that I find appealing: 1. The use of real data. 2. The use of R but with the option to use Python. 3. A good mix of theory and practice. 4. The text is well-written with good exercises. 5. The coverage of topics (e.g. Bayesian methods and clustering) that are not usually part of a course in statistics at the level of this book." (Jason M. Graham, University of Scranton)"-- |
| Assigning source |
Provided by publisher. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Description based on print version record and CIP data provided by publisher. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Mathematical analysis |
| General subdivision |
Statistical methods. |
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| Topical term or geographic name entry element |
Quantitative research |
| General subdivision |
Statistical methods. |
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| Topical term or geographic name entry element |
R (Computer program language) |
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| Topical term or geographic name entry element |
Python (Computer program language) |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Kateri, Maria, |
| Relator term |
author. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| Main entry heading |
Agresti, Alan. |
| Title |
Foundations of statistics for data scientists |
| Edition |
1st edition. |
| Place, publisher, and date of publication |
Boca Raton : CRC Press, 2021 |
| International Standard Book Number |
9780367748456 |
| Record control number |
(DLC) 2021019163 |
| 852 ## - LOCATION |
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| Accession No. |
10004000024722 |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
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| a |
7 |
| b |
cbc |
| c |
orignew |
| d |
1 |
| e |
ecip |
| f |
20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |
| Suppress in OPAC |
No |
