Python Metaclasses & Dataclasses: Theory & Use
What you'll learn
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- YOU WILL LEARN advanced object oriended principles in Python and concepts like metaclasses, advanced inheritance and dataclasses.
Requirements
- There are no prerequisites except basic knowledge of Python
Description
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2. The course gets updated every 6-12 months. Visit often to download new material!
3. Course Overview: This course focuses on mastering advanced object-oriented programming (OOP) in Python to build scalable, maintainable software systems. You will learn advanced modeling techniques and gain in-depth knowledge of metaclasses, including their role in optimizing code structure and supporting dynamic behavior. The course emphasizes hands-on projects that demonstrate real-world applications of metaclasses to improve code flexibility and maintainability. In addition, you will explore Python’s dataclasses module to simplify data management and eliminate repetitive boilerplate code. You’ll develop practical strategies for using both metaclasses and dataclasses in automating class creation and enforcing design patterns. The course also includes debugging practices to help you identify and resolve modeling issues effectively. By the end, you’ll be equipped with skills to write cleaner, more efficient, and more adaptable Python code. Understanding advanced OOP concepts in Python, including metaclasses and dataclasses, is crucial for anyone aiming to write robust, reusable, and efficient code in complex software systems. This course is ideal for intermediate to advanced Python developers, software engineers, data scientists, and students in computer science or related fields. It's also beneficial for professionals in technical roles—such as backend developers, automation engineers, or even energy modelers using Python—who need clean, scalable codebases. Mastery of these skills supports careers in software development, AI/ML engineering, DevOps, systems architecture, and fintech, as well as any domain where advanced Python usage is key. For aspiring professionals, this knowledge forms a competitive advantage in technical interviews and contributes to long-term career growth in software engineering and data-driven industries.
Who this course is for:
- Quantitative Developers expanding into economics with a focus on energy
- Energy Professionals interested in data‐driven methods
- Finance & Economics professionals looking for economics-related data science skills
- Data Scientists / Machine Learning Engineers applying skills in economics focused on energy
- Students & Researchers looking for practical projects
- Managers wanting to understand Data Science and Machine Learning applications in economics
- Operational researchers and economics/energy modellers interested in advancing their skills
Instructor
- 5.0 Instructor Rating
- 1,969 Reviews
- 10,871 Students
- 12 Courses
I hold a PhD in Energy Economics from Imperial College London with extensive experience in quantitative energy analysis.
My courses focus on practical applications of data science, optimization modeling, and machine learning techniques specifically tailored for the energy sector. Each course draws from my background in academic and industrial Research.
What makes my courses different? I break complex concepts into clear, digestible steps with real-world examples from actual energy projects. You'll develop immediately applicable skills through carefully structured lessons.
You'll never feel stuck—I personally respond to all questions within 24 hours, and support you throughout your learning journey.