Thinking data science: (Record no. 10314)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03314nam a22002057a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20251013173951.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251009b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783031023651 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | SAR |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Sarang, Poornachandra |
| 245 ## - TITLE STATEMENT | |
| Title | Thinking data science: |
| Remainder of title | a data science practitioner’s guide |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Cham |
| Name of publisher, distributor, etc. | Springer |
| Date of publication, distribution, etc. | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xx, 358 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 6288.15 |
| 490 ## - SERIES STATEMENT | |
| Series statement | The Springer Series in Applied Machine Learning (SSAML) |
| 500 ## - GENERAL NOTE | |
| General note | Table of contents:<br/>Front Matter<br/>Pages i-xx<br/>Download chapter PDF <br/>Data Science Process<br/>Poornachandra Sarang<br/>Pages 1-18<br/>Dimensionality Reduction<br/>Poornachandra Sarang<br/>Pages 19-52<br/>Part I<br/>Front Matter<br/>Pages 53-54<br/>Download chapter PDF <br/>Regression Analysis<br/>Poornachandra Sarang<br/>Pages 55-73<br/>Decision Tree<br/>Poornachandra Sarang<br/>Pages 75-96<br/>Ensemble: Bagging and Boosting<br/>Poornachandra Sarang<br/>Pages 97-129<br/>K-Nearest Neighbors<br/>Poornachandra Sarang<br/>Pages 131-141<br/>Naive Bayes<br/>Poornachandra Sarang<br/>Pages 143-152<br/>Support Vector Machines<br/>Poornachandra Sarang<br/>Pages 153-165<br/>Part II<br/>Front Matter<br/>Pages 167-170<br/>Download chapter PDF <br/>Centroid-Based Clustering<br/>Poornachandra Sarang<br/>Pages 171-183<br/>Connectivity-Based Clustering<br/>Poornachandra Sarang<br/>Pages 185-195<br/>Gaussian Mixture Model<br/>Poornachandra Sarang<br/>Pages 197-207<br/>Density-Based Clustering<br/>Poornachandra Sarang<br/>Pages 209-228<br/>BIRCH<br/>Poornachandra Sarang<br/>Pages 229-236<br/>CLARANS<br/>Poornachandra Sarang<br/>Pages 237-242<br/>Affinity Propagation Clustering<br/>Poornachandra Sarang<br/>Pages 243-250<br/>STING & CLIQUE<br/>Poornachandra Sarang<br/>Pages 251-257<br/>Part III<br/>Front Matter<br/>Pages 259-259<br/><br/>[https://link.springer.com/book/10.1007/978-3-031-02363-7] |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”.<br/><br/>The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.<br/><br/>(https://link.springer.com/book/10.1007/978-3-031-02363-7) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data science |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Book |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| 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 | IN32489 | 20-09-2025 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 10/05/2025 | Overseas Press India Private | 4087.30 | 006.31 SAR | 009020 | 10/05/2025 | 1 | 6288.15 | 10/05/2025 | Book |