Practical Data Science Engineering

Practical Data Science Engineering

Building Reusable Workflows and Pipelines in Python

Most data science books teach isolated techniques.

This book teaches how to build reusable systems.

Modern data science projects involve far more than training models. Real-world workflows require clean architecture, reproducible pipelines, modular preprocessing, reusable feature engineering, maintainable code organization, and scalable workflow design.

Practical Data Science Engineering bridges the gap between exploratory notebook experimentation and engineering-grade machine learning workflows.

Rather than relying on large monolithic notebooks filled with duplicated code, readers learn how to design modular, reusable workflow systems built from well-structured Python components.

Throughout the book, you will build a complete reusable workflow architecture for tabular machine learning projects using practical examples, reusable modules, and end-to-end pipeline integration.

What You Will Learn

• Build reusable preprocessing and feature engineering pipelines
• Design modular workflow architectures for machine learning projects
• Organize reusable data science code into maintainable systems
• Implement reproducible dataset splitting and transformation workflows
• Perform reusable exploratory data analysis (EDA)
• Develop scalable training and evaluation pipelines
• Structure workflow orchestration scripts and reusable modules
• Avoid common notebook-centric workflow problems
• Assemble a complete end-to-end reusable machine learning workflow

What Makes This Book Different

Many data science resources focus primarily on algorithms and mathematical theory.

This book focuses on engineering.

The emphasis is on designing workflows that are:

• reproducible
• maintainable
• modular
• scalable
• reusable

Readers move beyond isolated scripts and learn how practical machine learning systems are structured in real projects.

The book progressively transforms:

notebooks → reusable functions → modules → pipelines → workflow systems

By the end of the book, readers will have assembled a complete reusable workflow project that can serve as the foundation for larger machine learning systems and production-oriented architectures.

Who This Book Is For

This book is ideal for:

• Python users transitioning into data science
• data analysts seeking stronger engineering practices
• machine learning practitioners building reusable workflows
• developers interested in practical ML system design
• anyone who has outgrown notebook-only workflows

Some familiarity with Python and basic machine learning concepts is recommended.

Companion Resources Included

Readers gain access to:

• downloadable workflow projects
• reusable workflow templates
• chapter source code
• datasets and configuration files
• GitHub repositories
• companion workflow architecture resources

The complete reusable workflow system developed throughout the book is also available as a downloadable project repository.

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@book{Wei2026dataengineer,
  author    = {Wei, Shouke},
  title     = {Practical Data Science Engineering: Building Reusable Workflows and Pipelines in {Python}},
  edition   = {1st},
  publisher = {Deepsim Press},
  address   = {Abbotsford, BC},
  year      = {2026},
  doi       = {10.5281/zenodo.20693366},
  url       = {https://press.deepsim.ca},
  isbn      = {978-1-0677475-4-1},
  note      = {Also available in hardcover (978-1-0677475-2-7) and paperback (978-1-0677475-3-4) editions.}
}

Publication Details

• Author: Shouke Wei
• Publisher: Deepsim Press
• Series: Data Science Engeering in Python
• Format: PDF (Digital)
• Edition: First edition
• Print length: 446 pages
• Item Weight: PDF eBook-7.55 MB (7,920,449 bytes)
• Dimensions: 7.24 x 1.35 x 10.24 inches
• Language: English
• ISBN: 978-1-0677475-2-7 (Hardcover) | 978-1-0677475-3-4 (Paperback) | 978-1-0677475-4-1 (eBook)
• DOI: 10.5281/zenodo.20693366
• Publication date: 15/06/2026
• Book 1 of 3: Data Science Engeering in Pythpn

About the Author

Shouke Wei

0000-0002-4665-5366

Shouke Wei, PhD, is a researcher, scientist, and entrepreneur specializing in intelligent IoT systems, robotics, big data analytics, modeling and forecasting, early-warning systems, and edge computing. With academic and industry experience across Europe, North America, and Asia, Dr. Wei is recognized for bridging advanced theory with real-world, production-ready systems.

Dr. Wei earned his Ph.D. in Environmental and Resource Management from the Department of Environmental Informatics at Brandenburg University of Technology Cottbus–Senftenberg (Germany). He conducted postdoctoral research at the Swiss Federal Institute of Aquatic Science and Technology (Eawag), where he also served as a doctoral supervisor, and held research positions at the University of British Columbia (Canada). He has held distinguished and adjunct professorships at multiple institutions, including Yantai University, Ludong University, and Jining University. He has served as a graduate supervisor and distinguished professor in computer science, control engineering, and applied mathematics.

Dr. Wei’s work focuses on making advanced computational methods—particularly wavelet-based signal processing—accessible, practical, and impactful for researchers and practitioners worldwide. [More About the Author]