Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Data Science for Engineering: Principles, Tools, and Applications Across Disciplines

Data Science for Engineering: Principles, Tools, and Applications Across Disciplines

Benachour, Yassine

Hardcover

DatabasesGeneral Computers

PREORDER - Expected ship date December 28, 2026

ISBN10: 1041276257
ISBN13: 9781041276258
Publisher: CRC Press
Published: Dec 28 2026
Pages: 416
Language: English

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.

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