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Multivariate analysis and machine learning techniques = feature analysis in data science using Python /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multivariate analysis and machine learning techniques/ by Srikrishnan Sundararajan.
Reminder of title:	feature analysis in data science using Python /
Author:	Sundararajan, Srikrishnan.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xxvi, 435 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Multivariate analysis - Data processing. -
Online resource:	https://doi.org/10.1007/978-981-99-0353-5
ISBN:	9789819903535

Multivariate analysis and machine learning techniques = feature analysis in data science using Python /
Sundararajan, Srikrishnan.

Multivariate analysis and machine learning techniquesfeature analysis in data science using Python /[electronic resource] :by Srikrishnan Sundararajan. - Singapore :Springer Nature Singapore :2025. - xxvi, 435 p. :ill., digital ;24 cm. - Transactions on computer systems and networks,2730-7492. - Transactions on computer systems and networks..

Chapter 1: Introduction -- Chapter 2: Python for Data Analytics - A Quick Tour -- Chapter 3: Probability -- Chapter 4: Statistical Concepts -- Chapter 5: Correlation and Regression -- Chapter 6: Classification -- Chapter 7: Factor Analysis -- Chapter 8: Cluster Analysis -- Chapter 9: Survival Analysis -- Chapter 10: Computational Techniques -- Chapter 11: Machine Learning.

This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques - probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning. Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensive introduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications. The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.

ISBN: 9789819903535

Standard No.: 10.1007/978-981-99-0353-5doiSubjects--Topical Terms:

785988
Multivariate analysis
--Data processing.

LC Class. No.: QA278

Dewey Class. No.: 519.535028

Multivariate analysis and machine learning techniques = feature analysis in data science using Python /
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520 $a This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques - probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning. Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensive introduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications. The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.
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