MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04192cam a2200337 i 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
20815019 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240813151036.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
190118s2019 nyua 000 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2018052621 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781260452778 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
1260452778 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Transcribing agency |
DLC |
| Description conventions |
rda |
| Modifying agency |
DLC |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
HD30.23 |
| Item number |
.T324 2019 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
658.4033 TAD-M |
| 084 ## - OTHER CLASSIFICATION NUMBER |
|---|
| Classification number |
BUS000000 |
| Number source |
bisacsh |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Taddy, Matt |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Business data science : |
| Remainder of title |
combining machine learning and economics to optimize, automate, and accelerate business decisions / |
| Statement of responsibility, etc. |
Matt Taddy |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
New York |
| Name of publisher, distributor, etc. |
McGraw Hill |
| Date of publication, distribution, etc. |
2019 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
331 p. |
| 365 ## - TRADE PRICE |
|---|
| Price type code |
INR |
| Price amount |
3366.00. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Summary: Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you'll find the information, insight, and tools you need to flourish in today's data-driven economy. You'll learn how to: " se the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling" Understand how use ML tools in real world business problems, where causation matters more that correlation" Solve data science programs by scripting in the R programming language Today's business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It's about the exciting things being done around Big Data to run a flourishing business. It's about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you'll find the information, insight, and tools you need to flourish in today's data-driven economy. You'll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling. Understand how use ML tools in real world business problems, where causation matters more that correlation. data science programs by scripting in the R programming language Today's business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It's about the exciting things being done around Big Data to run a flourishing business. It's about the precepts, principals, and best practices that you need know for best-in-class business data science"-- |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"Combining machine learning and economics to optimize, automate, and accelerate business decisions"-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Decision making |
| General subdivision |
Econometric models. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
BUSINESS & ECONOMICS / General. |
| Source of heading or term |
bisacsh |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| 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 |
| 955 ## - COPY-LEVEL INFORMATION (RLIN) |
|---|
| Book number/undivided call number, CCAL (RLIN) |
rm13 2019-01-18 |
| Copy information and material description, CCAL + MDES (RLIN) |
rm13 2019-01-18 ONIX (telework) |
| Classification number, CCAL (RLIN) |
xn11 2020-03-06 1 copy rec'd., to CIP ver. |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) |
|---|
| Withdrawn status |
|
