Data Science for Mathematicians (Record no. 1433394)

MARC details
000 -LEADER
fixed length control field 01912nam a22002537a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250702095413.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250702b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 978103294561
040 ## - CATALOGING SOURCE
Original cataloging agency CSL
Transcribing agency CSL
041 ## - LANGUAGE CODE
Source of code eng
Language code of text/sound track or separate title eng
084 ## - COLON CLASSIFICATION NUMBER
Classification number B28 R1
Assigning agency CSL
245 ## - TITLE STATEMENT
Title Data Science for Mathematicians
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New york :
Name of publisher, distributor, etc. CRC Press/Taylor & Francis,
Date of publication, distribution, etc. 2021.
300 ## - PHYSICAL DESCRIPTION
Extent xv,528p.
Other physical details : ill.
Dimensions ; 24 cm.
490 ## - SERIES STATEMENT
Series statement CRC Press/ Chapman and Hall Handbooks in Mathematics Series
500 ## - GENERAL NOTE
General note Includes bibliography and index.
520 ## - SUMMARY, ETC.
Summary, etc. Data Science for Mathematicians presents the experience and insight of mathematicians who have retrained themselves as data scientists. Readers with a mathematical background and some computing expe-rience can use this book as a pathway to teaching in a data science program or starting research in the field.Students of data science, as they learn techniques and best practices, often ask why the techniques work and how they became best practices. A. mathematical mindset is uniquely qualified to answer those why questions, and thus mathematicians' involvement makes the teaching of data science more robust.The next generation of data scientists will be trained by faculty in the related disciplines of statistics, computer science, and mathematics. While pure mathematicians may be unfamiliar with data science, they have powerful skills that, if deepened in the ways set out in this book, would prepare them not only to teach the next generation of data scientists, but also to answer compelling questions with data. Gaining such power has reinvigorated the careers of many mathematicians
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Linear algebra.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Clustering.
9 (RLIN) 733203
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine Learning.
9 (RLIN) 480917
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Dimensionality Reduction.
9 (RLIN) 459955
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Carter, Nathan
Relator term editor.
9 (RLIN) 815135
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Colon Classification (CC)
Koha item type Textual
Classification part B28 R1
Suppress in OPAC No
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Total Checkouts Full call number Barcode Date last seen Date last checked out Price effective from Koha item type
    Colon Classification (CC)     Central Science Library Central Science Library 2024-10-23 Classic Book Service 1 B28 R1 SL1655956 2025-11-21 2025-11-11 2025-07-02 Textual
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