Data science using Python and R / (Record no. 12592)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 05577cam a2200865 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | on1089273491 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OCoLC |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240523125541.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr ||||||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190227t20192019njua ob 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER | |
| LC control number | 2019009632 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | DLC |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | DLC |
| Modifying agency | OCLCO |
| -- | N$T |
| -- | YDX |
| -- | EBLCP |
| -- | DG1 |
| -- | YDX |
| -- | UKMGB |
| -- | RECBK |
| -- | UKAHL |
| -- | OCLCF |
| -- | OCLCQ |
| -- | COO |
| -- | OCLCQ |
| -- | BRF |
| -- | OCLCO |
| -- | OCLCQ |
| -- | OCLCO |
| -- | OCLCL |
| -- | BCC |
| 015 ## - NATIONAL BIBLIOGRAPHY NUMBER | |
| National bibliography number | GBB956595 |
| Source | bnb |
| 016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER | |
| Record control number | 019327510 |
| Source | Uk |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781119526841 |
| Qualifying information | (electronic book) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1119526841 |
| Qualifying information | (electronic book) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781119526834 |
| Qualifying information | (electronic book) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1119526833 |
| Qualifying information | (electronic book) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781119526865 |
| Qualifying information | (electronic book) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1119526868 |
| Qualifying information | (electronic book) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9781119526810 |
| Qualifying information | (hardcover) |
| 029 1# - OTHER SYSTEM CONTROL NUMBER (OCLC) | |
| OCLC library identifier | AU@ |
| System control number | 000065306712 |
| 029 1# - OTHER SYSTEM CONTROL NUMBER (OCLC) | |
| OCLC library identifier | AU@ |
| System control number | 000066105039 |
| 029 1# - OTHER SYSTEM CONTROL NUMBER (OCLC) | |
| OCLC library identifier | CHNEW |
| System control number | 001050875 |
| 029 1# - OTHER SYSTEM CONTROL NUMBER (OCLC) | |
| OCLC library identifier | CHVBK |
| System control number | 567422283 |
| 029 1# - OTHER SYSTEM CONTROL NUMBER (OCLC) | |
| OCLC library identifier | UKMGB |
| System control number | 019327510 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1089273491 |
| 037 ## - SOURCE OF ACQUISITION | |
| Stock number | 9781119526841 |
| Source of stock number/acquisition | Wiley |
| 042 ## - AUTHENTICATION CODE | |
| Authentication code | pcc |
| 050 14 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.9.D343 |
| Item number | L376 2019 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 000000 |
| Source | bisacsh |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3/12 |
| Edition number | 23 |
| 049 ## - LOCAL HOLDINGS (OCLC) | |
| Holding library | MAIN |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Larose, Chantal D., |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Data science using Python and R / |
| Statement of responsibility, etc. | Chantal D. Larose, Daniel T. Larose. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Hoboken, NJ : |
| Name of producer, publisher, distributor, manufacturer | John Wiley & Sons, Inc, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2019. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | �2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (xvii, 238 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | n |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | nc |
| Source | rdacarrier |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Includes bibliographical references and index. |
| 588 0# - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Online resource; title from digital title page (viewed on April 03, 2019). |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world's two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist "the hottest job in America." Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naIve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Introduction to data science -- The basics of python and R -- Data preparation -- Exploratory data analysis -- Preparing to model the data -- Decision trees -- Model evaluation -- Na�ive Bayes classification -- Neural networks -- Clustering -- Regression modeling -- Dimension reduction -- Generalized linera models -- Association rules. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | John Wiley and Sons |
| Provenance (VM) [OBSOLETE] | Wiley Online Library: Complete oBooks |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Python (Computer program language) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | R (Computer program language) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Data structures (Computer science) |
| 650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Exploration de donn�ees (Informatique) |
| 650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Python (Langage de programmation) |
| 650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | R (Langage de programmation) |
| 650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Donn�ees volumineuses. |
| 650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Structures de donn�ees (Informatique) |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | COMPUTERS |
| General subdivision | General. |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Big data |
| Source of heading or term | fast |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Data mining |
| Source of heading or term | fast |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Data structures (Computer science) |
| Source of heading or term | fast |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Python (Computer program language) |
| Source of heading or term | fast |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | R (Computer program language) |
| Source of heading or term | fast |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Larose, Daniel T., |
| Relator term | author. |
| 758 ## - RESOURCE IDENTIFIER | |
| Relationship information | has work: |
| Label | Data science using Python and R (Text) |
| Real World Object URI | https://id.oclc.org/worldcat/entity/E39PCGPM8yFdBhpryTc7fG6KYd |
| Relationship | https://id.oclc.org/worldcat/ontology/hasWork |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Main entry heading | Larose, Chantal D. |
| Title | Data science using Python and R. |
| Place, publisher, and date of publication | Hoboken, NJ : John Wiley & Sons, Inc, 2019 |
| International Standard Book Number | 9781119526810 |
| Record control number | (DLC) 2019007280 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://onlinelibrary.wiley.com/doi/book/10.1002/9781119526865">https://onlinelibrary.wiley.com/doi/book/10.1002/9781119526865</a> |
| 938 ## - | |
| -- | Askews and Holts Library Services |
| -- | ASKH |
| -- | AH35311470 |
| 938 ## - | |
| -- | ProQuest Ebook Central |
| -- | EBLB |
| -- | EBL5741211 |
| 938 ## - | |
| -- | EBSCOhost |
| -- | EBSC |
| -- | 2091371 |
| 938 ## - | |
| -- | Recorded Books, LLC |
| -- | RECE |
| -- | rbeEB00758220 |
| 938 ## - | |
| -- | YBP Library Services |
| -- | YANK |
| -- | 16129616 |
| 938 ## - | |
| -- | YBP Library Services |
| -- | YANK |
| -- | 16142906 |
| 994 ## - | |
| -- | 92 |
| -- | INLUM |
No items available.