Overview
- Covers multivariate analysis and computational techniques for data analytics using Python
- Provides a step-by-step practical approach to learning using 100 tutorials and 50 worked-out exercises
- Is useful for programmers, statisticians, and practicing data analytics application professionals
Part of the book series: Transactions on Computer Systems and Networks (TCSN)
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Keywords
- Python
- Multi Variate Analysis
- Data Mining
- Business Analytics
- Computational Techniques
- Artificial Intelligence
- Big Data
About this book
Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensiveintroduction 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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Multivariate Analysis and Machine Learning Techniques
Book Subtitle: Feature Analysis in Data Science Using Python
Authors: Srikrishnan Sundararajan
Series Title: Transactions on Computer Systems and Networks
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
Hardcover ISBN: 978-981-99-0352-8Due: 06 September 2024
Softcover ISBN: 978-981-99-0355-9Due: 06 September 2024
eBook ISBN: 978-981-99-0353-5Due: 06 September 2024
Series ISSN: 2730-7484
Series E-ISSN: 2730-7492
Edition Number: 1
Number of Pages: XVII, 475
Number of Illustrations: 411 b/w illustrations, 138 illustrations in colour