This textbook provides a practical, engineering-focused introduction to modern data science. The book makes data science accessible to engineering students while maintaining the rigor necessary for advanced learners and professional practitioners.
Data Science for Engineering: Principles, Tools, and Applications Across Disciplines delivers a systematic progression through essential data science competencies organized into four integrated parts. Part I establishes the foundations of engineering data science, covering the data science lifecycle, engineering data sources, preprocessing, data quality, and Python-based analysis. Part II develops the statistical foundations required for rigorous interpretation, including probability, statistical inference, and hypothesis testing. Part III focuses on feature engineering, feature selection, exploratory data analysis, and visualization techniques. Part IV presents the machine learning sequence for engineering applications, encompassing supervised learning, unsupervised learning, time series analysis, and deep learning methods such as convolutional neural networks and LSTM models. Throughout, concepts are developed with practical rigor and reinforced through realistic engineering datasets, worked examples, coding labs, and companion Jupyter notebooks. The book emphasizes not only how to apply data science methods but also when to use them, what assumptions they carry, and how to interpret their outputs responsibly in engineering decision-making contexts.
This book is suited for undergraduate and graduate engineering students across all disciplines who are seeking to develop data science competencies for modern engineering practice. It also serves professional engineers and applied scientists who need to integrate data-driven approaches into their work.
An Instructor's Solutions Manual is available to verified adopting instructors, along with downloadable Jupyter notebooks, datasets, and supplementary resources that support both teaching and self-directed learnin