| 000 -LEADER |
|---|
| fixed length control field |
03493cam a2200313 i 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20250421080606.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250220s2024 flua b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781032346748 (hardback) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781032350424 (paperback) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Canceled/invalid ISBN |
9781003324997 (ebook) |
| 037 ## - SOURCE OF ACQUISITION |
|---|
| Source of stock number/acquisition |
SBF2024 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
DLC |
| Modifying agency |
DLC |
| -- |
AE-ShU |
| Transcribing agency |
UKB |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA276.4 |
| Item number |
.S454 2024 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Shea, John M. |
| Titles and words associated with a name |
(Professor of electrical engineering), |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Foundations of data science with Python / |
| Statement of responsibility, etc. |
John M. Shea. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
First edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Boca Raton : |
| Name of publisher, distributor, etc. |
CRC Press, Taylor & Francis Group, |
| Date of publication, distribution, etc. |
2024. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiv, 488 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
27 cm. |
| 490 0# - SERIES STATEMENT |
|---|
| Series statement |
Chapman & Hall/CRC data science series |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references and index. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
First simulations, visualizations, and statistical tests -- First visualizations and statistical tests with real data -- Introduction to probability -- Null hypothesis tests -- Conditional probability, dependence, and independence -- Introduction to Bayesian methods -- Random variables -- Expected value, parameter estimation, and hypothesis tests on sample means -- Decision making with observations from continuous distributions -- Categorical data, tests for dependence, and goodness of fit for discrete distributions -- Multidimensional data : vector moments and linear regression -- Working with dependent data in multiple dimensions. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality. This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science"-- |
| Assigning source |
Provided by publisher. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Statistics |
| General subdivision |
Data processing, |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Probabilities |
| General subdivision |
Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Information visualization. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Python (Computer program language) |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Online version: |
| Main entry heading |
Shea, John Mark (Professor of electrical engineering). |
| Title |
Foundations of data science with Python |
| Edition |
First edition. |
| Place, publisher, and date of publication |
Boca Raton : CRC Press, Taylor & Francis Group, 2024 |
| International Standard Book Number |
9781003324997 |
| Record control number |
(DLC) 2023037554 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Books |
