Data Science : An Introduction to Statistics and Machine Learning / Matthias Plaue

By:

Plaue, Matthias

Material type: Text

9783662678817

Subject(s):

DDC classification:

005.7 PlaD

Summary: This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.

List(s) this item appears in: New Arrivals 01-15 October 2025, Vol. 06, Issue 28

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	Indian Institute of Technology Tirupati General Stacks	Computer Science	005.7 PlaD (11476) (Browse shelf(Opens below))	Copy 1	Checked out	07/02/2026	11476

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Previous	005.7 LeeB (11829) Big Data, Cloud Computing and Data Science Engineering /	005.7 MahD (11541) Data Science and Big Data Computing : Frameworks and Methodologies /	005.7 MarD (11520) Data Science Ethics : Concepts, Techniques, and Cautionary Tales /	005.7 PlaD (11476) Data Science : An Introduction to Statistics and Machine Learning /	005.7 PlaD (12018) Data Science : An Introduction to Statistics and Machine Learning /	005.7 SHI/M Mathematical Foundations of Big Data Analytics /	005.7 SHI/M Mathematical Foundations of Big Data Analytics /	Next

This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.

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