Data analytics: (Record no. 4499)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03292nam a22002297a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230117112317.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230117b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780367609504 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 001.42 |
| Item number | HUA |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Huang, Shuai |
| 245 ## - TITLE STATEMENT | |
| Title | Data analytics: |
| Remainder of title | a small data approach |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | CRC Press |
| Place of publication, distribution, etc. | Boco Raton |
| Date of publication, distribution, etc. | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xiv, 257 p. |
| 365 ## - TRADE PRICE | |
| Price type code | GBP |
| Price amount | 68.99 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Table of Contents<br/>1. INTRODUCTION<br/><br/>Who will benefit from this book<br/><br/>Overview of a Data Analytics Pipeline<br/><br/>Topics in a Nutshell<br/><br/>2. ABSTRACTION<br/><br/>Regression & tree models<br/><br/>Overview<br/><br/>Regression Models<br/><br/>Tree Models<br/><br/>Remarks<br/><br/>Exercises<br/><br/>3. RECOGNITION<br/><br/>Logistic regression & ranking<br/><br/>Overview<br/><br/>Logistic Regression Model<br/><br/>A Ranking Problem by Pairwise Comparison<br/><br/>Statistical Process Control using Decision Tree<br/><br/>Remarks<br/><br/>Exercise<br/><br/>4. RESONANCE<br/><br/>Bootstrap & random forests<br/><br/>Overview<br/><br/>How Bootstrap Works<br/><br/>Random Forests<br/><br/>Remarks<br/><br/>Exercises<br/><br/>5. LEARNING (I)<br/><br/>Cross validation & OOB<br/><br/>Overview<br/><br/>Cross-Validation<br/><br/>Out-of-bag error in Random Forest<br/><br/>Remarks<br/><br/>Exercises<br/><br/>6. DIAGNOSIS<br/><br/>Residuals & heterogeneity<br/><br/>Overview<br/><br/>Diagnosis in Regression<br/><br/>Diagnosis in Random Forests<br/><br/>Clustering<br/><br/>Remarks<br/><br/>Exercises<br/><br/>7. LEARNING (II)<br/><br/>SVM & ensemble Learning<br/><br/>Overview<br/><br/>Support Vector Machine<br/><br/>Ensemble Learning<br/><br/>Remarks<br/><br/>Exercises<br/><br/>data analytics<br/><br/>8. SCALABILITY<br/><br/>LASSO & PCA<br/><br/>Overview<br/><br/>LASSO<br/><br/>Principal Component Analysis<br/><br/>Remarks<br/><br/>Exercises<br/><br/>9. PRAGMATISM<br/><br/>Experience & experimental<br/><br/>Overview<br/><br/>Kernel Regression Model<br/><br/>Conditional Variance Regression Model<br/><br/>Remarks<br/><br/>Exercises<br/><br/>10. SYNTHESIS<br/><br/>Architecture & pipeline<br/><br/>Overview<br/><br/>Deep Learning<br/><br/>inTrees<br/><br/>Remarks<br/><br/>Exercises<br/><br/>CONCLUSION<br/><br/>APPENDIX: A BRIEF REVIEW OF BACKGROUND KNOWLEDGE<br/><br/>The normal distribution<br/><br/>Matrix operations<br/><br/>Optimization |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Data Analytics: A Small Data Approach is suitable for an introductory data analytics course to help students understand some main statistical learning models. It has many small datasets to guide students to work out pencil solutions of the models and then compare with results obtained from established R packages. Also, as data science practice is a process that should be told as a story, in this book there are many course materials about exploratory data analysis, residual analysis, and flowcharts to develop and validate models and data pipelines.<br/><br/>The main models covered in this book include linear regression, logistic regression, tree models and random forests, ensemble learning, sparse learning, principal component analysis, kernel methods including the support vector machine and kernel regression, and deep learning. Each chapter introduces two or three techniques. For each technique, the book highlights the intuition and rationale first, then shows how mathematics is used to articulate the intuition and formulate the learning problem. R is used to implement the techniques on both simulated and real-world dataset. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | R (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Quantitative research |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Deng, Houtao |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Bill No | Bill Date | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Accession Number | Date last seen | Date checked out | Copy number | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | 575/22-23 | 30-12-2022 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 01/17/2023 | T V Enterprises | 4540.63 | 1 | 001.42 HUA | 004210 | 09/04/2024 | 08/21/2024 | 1 | 6905.90 | 01/17/2023 | Book |