Data Science for Engineers

Engineering problems rarely arrive neatly labeled as “data science.” They begin with measurements, uncertainty, and a question worth answering. Data Science for Engineers builds a structured bridge from those ingredients to the ideas behind machine learning and artificial intelligence, with engineering applications giving the concepts practical context.

Raghunathan Rengaswamy and Resmi Suresh combine conceptual explanations with mathematical foundations, examples, and coverage of contemporary machine-learning methods. The result is a textbook for readers who want to understand not only how data-science techniques are used, but also the mathematical ideas that support them.

Build the foundations before the models

The book opens with core questions about data science, machine learning, and AI: what learning means, how it happens, and how machine-learning systems make decisions. It then introduces classification, function approximation, feature engineering, and a framework for approaching data-science problems. These early chapters help place algorithms within a wider process of defining, developing, and assessing a solution.

The mathematics behind data science

Rather than treating mathematics as a detour, the text connects it directly to data and learning. Dedicated chapters explore linear algebra, optimization, and statistical foundations, including matrices, projections, eigenvalues, singular value decomposition, objective functions, gradient-based methods, probability, and uncertainty. This mathematical thread helps readers see how the tools relate to the problems they are meant to address.

Examples rooted in engineering problems

Illustrative applications include predicting material properties, identifying equipment failures, detecting credit-card fraud, and recognizing objects. Examples make it easier to connect abstract methods with the kinds of prediction and classification tasks that arise across different disciplines. Later chapters turn to function-approximation and classification methods, bringing the book’s conceptual and mathematical groundwork into focus.

For engineering students and learners

The authors identify undergraduate and senior undergraduate students across engineering fields as the primary readership. Students taking courses in data science, machine learning, or artificial intelligence can use the book for a structured introduction; readers with an interest in engineering applications may also value its emphasis on the mathematics and reasoning behind the methods.

For anyone looking to connect engineering questions with data-driven methods, this textbook offers a thoughtful place to begin: concepts first, mathematical grounding alongside them, and practical examples to show where the ideas can lead.