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Thinking data science = a data scien...

Sarang, P. G.

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Thinking data science = a data science practitioner's guide /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Thinking data science/ by Poornachandra Sarang.
Reminder of title:	a data science practitioner's guide /
Author:	Sarang, P. G.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xx, 358 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter. 1. Data Science Process -- Chapter. 2. Dimensionality Reduction - Creating Manageable Training Datasets -- Chapter. 3. Classical Algorithms - Over-view -- Chapter. 4. Regression Analysis -- Chapter. 5. Decision Tree -- Chapter. 6. Ensemble - Bagging and Boosting -- Chapter. 7. K-Nearest Neighbors -- Chapter. 8. Naive Bayes -- Chapter. 9. Support Vector Machines: A supervised learning algorithm for Classification and Regression -- Chapter. 10. Clustering Overview -- Chapter. 11. Centroid-based Clustering -- Chapter. 12. Connectivity-based Clustering -- Chapter. 13. Gaussian Mixture Model -- Chapter. 14. Density-based -- Chapter. 15 -- BIRCH -- Chapter. 16. CLARANS -- Chapter. 17. Affinity Propagation Clustering -- Chapter. 18. STING -- Chapter. 19. CLIQUE -- Chapter. 20. Artificial Neural Networks -- Chapter. 21. ANN-based Applications -- Chapter. 22. Automated Tools -- Chapter. 23. Data Scientist's Ultimate Workflow.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-031-02363-7
ISBN:	9783031023637

Thinking data science = a data science practitioner's guide /
Sarang, P. G.

Thinking data sciencea data science practitioner's guide /[electronic resource] :by Poornachandra Sarang. - Cham :Springer International Publishing :2023. - xx, 358 p. :ill., digital ;24 cm. - Springer series in applied machine learning,2520-1301. - Springer series in applied machine learning..

Chapter. 1. Data Science Process -- Chapter. 2. Dimensionality Reduction - Creating Manageable Training Datasets -- Chapter. 3. Classical Algorithms - Over-view -- Chapter. 4. Regression Analysis -- Chapter. 5. Decision Tree -- Chapter. 6. Ensemble - Bagging and Boosting -- Chapter. 7. K-Nearest Neighbors -- Chapter. 8. Naive Bayes -- Chapter. 9. Support Vector Machines: A supervised learning algorithm for Classification and Regression -- Chapter. 10. Clustering Overview -- Chapter. 11. Centroid-based Clustering -- Chapter. 12. Connectivity-based Clustering -- Chapter. 13. Gaussian Mixture Model -- Chapter. 14. Density-based -- Chapter. 15 -- BIRCH -- Chapter. 16. CLARANS -- Chapter. 17. Affinity Propagation Clustering -- Chapter. 18. STING -- Chapter. 19. CLIQUE -- Chapter. 20. Artificial Neural Networks -- Chapter. 21. ANN-based Applications -- Chapter. 22. Automated Tools -- Chapter. 23. Data Scientist's Ultimate Workflow.

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". 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.

ISBN: 9783031023637

Standard No.: 10.1007/978-3-031-02363-7doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .S27 2023

Dewey Class. No.: 006.31

Thinking data science = a data science practitioner's guide /
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520 $a 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". 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.
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