Advanced Data Science Systems Engineering
Building Reproducible, Scalable, and Production-Ready Systems in Python
Most data science books teach how to build models.
Few teach how to build systems.
After a machine learning workflow works successfully on a laptop, a new set of challenges emerges:
- How do you make experiments reproducible?
- How do you manage models, artifacts, and configurations?
- How do you test and debug data science workflows?
- How do you optimize performance as datasets grow?
- How do you transform reusable workflows into maintainable frameworks?
Advanced Data Science Systems Engineering focuses on the engineering practices that bridge the gap between experimentation and production-ready systems.
Building upon the workflow foundation established in Practical Data Science Engineering, this book shows how to design, organize, test, optimize, and package data science systems that remain reliable as projects become larger and more complex.
Through practical examples and reusable implementations, readers learn how to:
- build reproducible and configuration-driven workflows
- manage models, datasets, and artifacts systematically
- implement logging, testing, and debugging infrastructure
- optimize performance using vectorization, profiling, and scalable processing techniques
- compare pandas and polars workflows for modern data engineering tasks
- design maintainable project architectures
- package reusable components into professional Python libraries
- transform workflow projects into a reusable framework
Throughout the book, readers progressively develop dskit, a reusable data science framework that demonstrates how individual workflow components can evolve into a structured, installable, and extensible toolkit.
Unlike books that focus exclusively on algorithms or theory, this volume emphasizes practical systems engineering principles:
- reproducibility
- maintainability
- modularity
- scalability
- software engineering best practices for data science
Whether you are a data scientist, machine learning practitioner, analyst, researcher, or Python developer, this book provides the tools and architectural patterns needed to move beyond isolated notebooks and build systems that others can run, extend, and trust.
By the end of the book, you will not only understand how modern data science systems are engineered—you will have built one yourself.
What You Will Learn
✓ Reproducible workflow design and experiment management
✓ Configuration-driven data science systems
✓ Model persistence and artifact management
✓ Logging, testing, and debugging workflows
✓ Performance optimization and scalable processing
✓ Packaging and distributing Python data science tools
✓ Framework architecture and toolkit development
✓ Building the dskit data science framework
Who This Book Is For
- Data scientists seeking stronger engineering practices
- Machine learning practitioners building production workflows
- Python developers working with data-intensive applications
- Analysts transitioning from notebooks to reusable systems
- Readers of Practical Data Science Engineering who want to continue their journey from workflows to frameworks
Buy Print CopyCitation:
Wei, Shouke. 2026. Advanced Data Science Systems Engineering: Building Reproducible, Scalable, and Production-Ready Systems in Python. 1st ed. Abbotsford, BC: Deepsim Press. https://doi.org/10.5281/zenodo.20787832.
@book{Wei2026advanceddataengineer,
author = {Wei, Shouke},
title = {Advanced Data Science Systems Engineering: Building Reproducible, Scalable, and Production-Ready Systems in {Python}},
edition = {1st},
publisher = {Deepsim Press},
address = {Abbotsford, BC},
year = {2026},
doi = {10.5281/zenodo.20787832},
url = {https://press.deepsim.ca},
isbn = {978-1-0677475-7-2},
note = {Also available in hardcover (978-1-0677475-5-8) and paperback (978-1-0677475-6-5) editions.}
}Publication Details
- Author: Shouke Wei
- Publisher: Deepsim Press
- Series: Data Science Engeering in Python
- Format: PDF (Digital)
- Edition: First edition
- Print length: 498 pages
- Item Weight: PDF eBook-7.79 MB (8,176,899 bytes)
- Dimensions: 7.24 x 10.24 inches
- Language: English
- ISBN: 978-1-0677475-5-8 (Hardcover) | 978-1-0677475-6-5 (Paperback) | 978-1-0677475-7-2 (eBook)
- DOI: 10.5281/zenodo.20787832
- Publication date: 22/06/2026
- Book 2 of 3: Data Science Engeering in Pythpn
About the Author
Shouke Wei
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]
