Undergraduate Certificate in Intermediate Python: Debugging and Performance Optimization

This course is designed for undergraduate students with basic Python programming skills who wish to enhance their debugging and performance optimization abilities. It equips students with advanced techniques to identify and resolve complex bugs efficiently and optimize Python code for better performance and resource utilization.

Upon completion, students will gain proficiency in using debugging tools, understanding Python's memory management, and applying optimization strategies to improve the speed and efficiency of their programs.

1. 1. Introduction to Debugging Techniques: Learners will study basic debugging tools and methods, including print statements, breakpoints, and interactive debugging. They will gain skills in identifying and correcting common errors in Python code.
2. 2. Debugging Tools and Environments: This module covers advanced debugging tools and environments like PyCharm, debugging in Jupyter Notebooks, and using logging effectively. Learners will be able to use these tools to enhance their debugging workflow.
3. 3. Performance Profiling and Optimization: Learners will learn to use profiling tools to identify bottlenecks in their Python programs and optimize code for better performance. They will understand the importance of efficient algorithms and data structures.
4. 4. Python Performance Best Practices: This module focuses on best practices for writing high-performance Python code. Topics include performance considerations, memory management, and efficient use of Python libraries.
5. 5. Advanced Debugging Strategies: Learners will explore more complex debugging strategies such as race conditions, concurrency issues, and debugging in multi-threaded and multi-process environments.
6. 6. Optimization Techniques for Python Programs: This module covers various optimization techniques including code refactoring, algorithm optimization, and the use of just-in-time (JIT) compilers like Numba.
7. 7. Debugging and Optimizing Web Applications: Learners will apply debugging and optimization techniques to web applications built with Python frameworks like Flask or Django, focusing on improving user experience and performance.
8. 8. Debugging and Performance in Data Science Projects: This module teaches learners how to debug and optimize data science projects, including handling large datasets and optimizing data processing pipelines.
9. 9. Continuous Integration and Deployment for Python Projects: Learners will learn to set up and use continuous integration and deployment (CI/CD) tools to automate testing and deployment processes, ensuring high-quality code and efficient performance.
10. 10. Final Project – Debugging and Optimizing a Complex Python Application: In this capstone project, learners will apply all the skills learned in previous modules to debug and optimize a complex Python application, demonstrating their ability to work on real-world software development challenges.