Applied Data Analytics – Principles and Applications

Applied Data Analytics – Principles and Applications is a focused, technically grounded ebook for readers who want more than surface-level data analysis. Johnson I. Agbinya approaches the subject through the core mathematical and algorithmic ideas that make modern analytics work, then connects those ideas to practical applications across data-heavy domains.

From Foundations to Applications

The book moves through a sequence of important methods used in analysis and prediction, including Markov chains, hidden Markov models, Kalman filters, genetic algorithms, and computational graphs. That mix gives the volume a distinctly applied character: it is not just about definitions, but about how these techniques are used to model, estimate, optimize, and interpret real data.

A Strong Fit for Technical Readers

This is the kind of title that will appeal to readers who already have, or want, a serious interest in data analytics, signal processing, and computational methods. The presentation is tutorial in tone, with an emphasis on usage and interpretation rather than abstract theory alone. That makes it especially useful for students, researchers, and practitioners who want a structured way into the subject.

Why It Stands Out

Because the book combines core analytics methods with application-minded explanations, it can serve as a bridge between mathematical foundations and practical implementation. Readers looking for a digitally convenient reference on the logic behind common analytical techniques will find a clear, method-driven text here.

About This Edition

• By Johnson I. Agbinya
• Published by River Publishers
• Part of the River Publishers Series in Signal, Image and Speech Processing
• English-language technical ebook

If you are building a collection around data science fundamentals, applied machine learning, or signal-processing-adjacent analytics, this title earns a serious look.