Data analytics: (Record no. 4499)

MARC details
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
Holdings
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 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   001.42 HUA 004210 01/17/2023 1 6905.90 01/17/2023 Book

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