Data science for business : (Record no. 1450)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03173nam a2200301 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | EBL1323973 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | AU-PeEL |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200221101120.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 170802s2013 ch a||||s|||| 001 | eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781449374297 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MIUC |
| Language of cataloging | eng |
| Transcribing agency | MIUC |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 658.4038 |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.312 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 3213 |
| Personal name | Provost, Foster |
| 952 ## - Items | |
| Itemnumber | 1774 |
| 245 10 - TITLE STATEMENT | |
| Title | Data science for business : |
| Remainder of title | [what you need to know about data mining and data-analytic thinking] |
| Medium | [electronic resource] / |
| Statement of responsibility, etc. | Foster Provost and Tom Fawcett. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Beijing, etc. : |
| Name of publisher, distributor, etc. | O'Reilly, |
| Date of publication, distribution, etc. | 2013. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (xxi, 386 p.) : |
| Other physical details | ill. b&w and col. |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Includes bibliographical references and index. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Ch. 1. Introduction: Data-analytic thinking -- <br/>Ch. 2. Business problems and data science solutions -- <br/>Ch. 3. Introduction to predictive modelling: from correlation to supervised segmentation -- <br/>Ch. 4. Fitting a model to data -- <br/>Ch. 5. Overfitting and its avoidance -- <br/>Ch. 6. Similarity, neighbours, and clusters -- <br/>Ch. 7. Decision analytic thinking I: What is a good model? -- <br/>Ch. 8. Visualizing model performance -- <br/>Ch. 9. Evidence and probabilities -- <br/>Ch. 10. Representing and mining text -- <br/>Ch. 11. Decision analytic thinking II: Toward analytical engineering -- <br/>Ch. 12. Other data science tasks and techniques -- <br/>Ch. 13. Data science and business strategy -- <br/>Ch. 14. Conclusion -- <br/>Appendix A. Proposal review guide -- <br/>Appendix B. Another sample proposal. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. You'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization and how you can use it for competitive advantage; Treat data as a business asset that requires careful investment if you're to gain real value; Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way; Learn general concepts for actually extracting knowledge from data; Apply data science principles when interviewing data science job candidates. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 1863 |
| Topical term or geographic name entry element | Big data |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 794 |
| Topical term or geographic name entry element | Data mining |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 48 |
| Topical term or geographic name entry element | Business |
| General subdivision | Data processing |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 3214 |
| Personal name | Fawcett, Tom |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://ebookcentral.proquest.com/lib/miu/detail.action?docID=1323973 |
| Public note | Click here to view |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Electronic resources |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Date last seen | Price effective from | Koha item type | Public note |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Marbella International University Centre | Marbella International University Centre | 17/10/2018 | 658.4038 PRO dat | 17/10/2018 | 17/10/2018 | Electronic resources | E-book |