About the Book
Advanced Data Science Programming presents a comprehensive guide to the modern Data Science workflow, covering every stage from raw data processing to real-world model deployment [1]. As one of the most transformative fields in both academia and industry, Data Science bridges the gap between raw data and actionable insights, enabling data-driven decision-making, process optimization, and the development of intelligent systems [2].
Intro
This book begins with advanced programming practices and modular code design, progresses through data integration, wrangling, and feature engineering, and culminates in predictive modeling, interactive visualization, deployment, and monitoring [3]. Each chapter reflects the natural flow of a Data Science project, ensuring both conceptual depth and practical relevance [4]. Key Topics:
- Advanced Programming: Functional programming, modularization, and object-oriented design for Data Science projects [5].
- Data Acquisition & Preparation: API integration, advanced wrangling, and powerful feature engineering strategies [6].
- Modeling & Evaluation: Building robust predictive models, applying validation techniques, and interpreting results through visualization [7].
- Deployment & MLOps: Model packaging, workflow automation, monitoring performance, and implementing scalable production solutions [8].
By combining theoretical foundations, practical examples, and best practices, this book equips graduate students, researchers, and professional practitioners with the skills and mindset necessary to move beyond the basics, manage complex projects, and bring models into impactful real-world applications.
Overview of the Course
The Figure 1 presents a visual overview of this book, outlining the structure of advanced topics in Data Science programming and their interconnections. It provides readers with a roadmap to navigate the material, from advanced coding practices and data integration to modeling, visualization, deployment, and monitoring. This structure highlights how each concept contributes to the overall Data Science process, enabling readers to connect theory with practical applications in real-world decision-making contexts [9].
References
[1]
Géron, A., Hands-on machine learning with scikit-learn, keras, and TensorFlow, O’Reilly Media, 2023
[2]
Wickham, H. and Grolemund, G., R for data science: Import, tidy, transform, visualize, and model data, O’Reilly Media, 2016
[3]
Müller, A. C. and Guido, S., Introduction to machine learning with python: A guide for data scientists, O’Reilly Media, 2016
[4]
Chollet, F., Deep learning with python, Manning Publications, 2021
[5]
Raschka, S., Liu, Y., and Mirjalili, V., Machine learning with PyTorch and scikit-learn: Develop machine learning and deep learning models with python, Packt Publishing Ltd, 2022
[6]
Rocklin, M., Data science at scale with python and dask, O’Reilly Media, 2020
[7]
Breck, E., Cai, S., Nielsen, E., Salib, M., and Sculley, D., The ML test score: A rubric for ML production readiness and technical debt reduction, Proceedings of the IEEE Big Data Conference, 2017
[8]
Hummer, W., MLOps: Continuous delivery and automation pipelines in machine learning, O’Reilly Media, 2021
[9]
James, G., Witten, D., Hastie, T., and Tibshirani, R., An introduction to statistical learning with applications in r, Springer, 2021