MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02928nam a22004337a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
IIITD |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240602020003.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
231213b xxu||||| |||| 00| 0 eng d |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2023276388 |
| 015 ## - NATIONAL BIBLIOGRAPHY NUMBER |
|---|
| National bibliography number |
GBC290257 |
| Source |
bnb |
| 016 ## - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER |
|---|
| Record control number |
020621576 |
| Source |
Uk |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781098102937 |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(OCoLC)on1328015167 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UKMGB |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
UKMGB |
| Modifying agency |
FIE |
| -- |
OCLCF |
| -- |
UAP |
| -- |
NVC |
| -- |
JRZ |
| -- |
OCL |
| -- |
VNVGU |
| -- |
IIITD |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
lccopycat |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.9.D343 |
| Item number |
N54 2022 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.310 |
| Edition number |
23 |
| Item number |
NIE-E |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Nield, Thomas |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Essential math for data science : |
| Remainder of title |
take control of your data with fundamental linear algebra, probability, and statistics |
| Statement of responsibility, etc |
by Thomas Nield |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Mumbai : |
| Name of publisher, distributor, etc |
Shroff Publishers, |
| Date of publication, distribution, etc |
©2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiv, 332 p. : |
| Dimensions |
24 cm. |
| Other physical details |
ill. ; |
| 500 ## - GENERAL NOTE |
|---|
| General note |
This book includes index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
1. Basic math and calculus review |
| -- |
2. Probability |
| -- |
3. Descriptive and inferential statistics |
| -- |
4. Linear algebra |
| -- |
5. Linear regression |
| -- |
6. Logistic regression and classification |
| -- |
7. Neural networks |
| -- |
8. Career advice and the path forward |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to: Recognize the nuances and pitfalls of probability math Master statistics and hypothesis testing (and avoid common pitfalls) Discover practical applications of probability, statistics, calculus, and machine learning Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added Perform calculus derivatives and integrals completely from scratch in Python Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks -- |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining |
| General subdivision |
Mathematics. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| General subdivision |
Mathematics. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Mathematical statistics. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Probabilities. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer science |
| General subdivision |
Mathematics. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer science |
| General subdivision |
Mathematics. |
| Source of heading or term |
fast |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining |
| General subdivision |
Mathematics. |
| Source of heading or term |
fast |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Mathematical statistics. |
| Source of heading or term |
fast |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Probabilities. |
| Source of heading or term |
fast |
| 655 #7 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Handbooks and manuals. |
| Source of term |
fast |
| 655 #7 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Handbooks and manuals. |
| Source of term |
lcgft |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
7 |
| b |
cbc |
| c |
copycat |
| d |
2 |
| e |
ncip |
| f |
20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |
| Koha issues (borrowed), all copies |
3 |
