Thinking data science: a data science practitioner’s guide

By:

Sarang, Poornachandra

Material type: Text

TextSeries: The Springer Series in Applied Machine Learning (SSAML)Publication details: Cham Springer 2024Description: xx, 358 pISBN:

9783031023651

Subject(s):

Data science

DDC classification:

006.31 SAR

Summary: 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. (https://link.springer.com/book/10.1007/978-3-031-02363-7)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.31 SAR (Browse shelf(Opens below))	1	Available		009020

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Previous	006.31 RAM Machine learning using R:	006.31 RAO Machine learning in data science using Python	006.31 SAH Machine learning fundamentals: concepts, models, and applications	006.31 SAR Thinking data science: a data science practitioner’s guide	006.31 SHA Deep learning with Tensorflow JS projects	006.31 SIL Beginning with deep learning using TensorFlow: a beginner’s guide to TensorFlow and Keras for practicing deep learning principles and applications	006.31 SIN Deep learning through the prism of tensors	Next

Table of contents:
Front Matter
Pages i-xx
Download chapter PDF
Data Science Process
Poornachandra Sarang
Pages 1-18
Dimensionality Reduction
Poornachandra Sarang
Pages 19-52
Part I
Front Matter
Pages 53-54
Download chapter PDF
Regression Analysis
Poornachandra Sarang
Pages 55-73
Decision Tree
Poornachandra Sarang
Pages 75-96
Ensemble: Bagging and Boosting
Poornachandra Sarang
Pages 97-129
K-Nearest Neighbors
Poornachandra Sarang
Pages 131-141
Naive Bayes
Poornachandra Sarang
Pages 143-152
Support Vector Machines
Poornachandra Sarang
Pages 153-165
Part II
Front Matter
Pages 167-170
Download chapter PDF
Centroid-Based Clustering
Poornachandra Sarang
Pages 171-183
Connectivity-Based Clustering
Poornachandra Sarang
Pages 185-195
Gaussian Mixture Model
Poornachandra Sarang
Pages 197-207
Density-Based Clustering
Poornachandra Sarang
Pages 209-228
BIRCH
Poornachandra Sarang
Pages 229-236
CLARANS
Poornachandra Sarang
Pages 237-242
Affinity Propagation Clustering
Poornachandra Sarang
Pages 243-250
STING & CLIQUE
Poornachandra Sarang
Pages 251-257
Part III
Front Matter
Pages 259-259

[https://link.springer.com/book/10.1007/978-3-031-02363-7]

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.

(https://link.springer.com/book/10.1007/978-3-031-02363-7)

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