Advanced Python: Performance & Production for Research
Master high-performance Python with parallel processing, optimization techniques, and production practices. Learn testing, profiling, and deployment for large-scale research applications.
Workshop Description
This intensive 3-day workshop is designed for experienced Python programmers ready to tackle high-performance computing and production-level research applications. You'll master advanced Python techniques including performance optimization, parallel processing, and software engineering practices essential for large-scale research projects.
You'll dive deep into Python's advanced features including decorators, context managers, metaclasses, and memory optimization. The workshop covers parallel computing, profiling and optimization techniques, testing strategies, and deployment practices that transform research code from scripts into robust, maintainable software.
By the end of this workshop, you'll be equipped to build high-performance research applications, optimize computational workflows, implement comprehensive testing suites, and deploy Python applications for collaborative research. You'll understand when and how to scale Python solutions and integrate with other languages and systems.
Instructor
Course Fee
$399.0
Maximum Seats
20
Duration
3 half-days
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Time
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A comprehensive 3-day deep dive into advanced Python programming for research computing
Format: Each day is approximately 4 hours with intensive coding exercises and real-world projects
Prerequisites: Solid intermediate Python knowledge required - OOP, functions, data structures, and familiarity with Python libraries. Equivalent to our Intermediate Python course
What you'll learn: Performance optimization, parallel computing, advanced Python features, testing frameworks, profiling tools, and production deployment strategies
Materials provided: Advanced code templates, performance benchmarking tools, testing frameworks setup, and deployment examples. Lifetime access to all materials
Day 1: Advanced Python Features & Performance
Master Python's advanced features and optimization techniques
Advanced Functions & Decorators (1 hr): Function factories, closures, advanced decorators with parameters, and practical applications in research workflows
Context Managers & Generators (1 hr): Creating custom context managers, generator functions and expressions, memory-efficient data processing, and the yield keyword
Memory Management & Optimization (1 hr): Understanding Python's memory model, profiling memory usage, optimization strategies, and working with large datasets efficiently
Metaclasses & Advanced OOP (1 hr): Metaclass basics, class factories, advanced inheritance patterns, and designing flexible research frameworks
Day 2: Parallel Computing & Concurrency
Harness multiple cores and optimize computational performance
Threading vs Multiprocessing (1 hr): Understanding the GIL, when to use threads vs processes, and practical examples with research computations
Multiprocessing Deep Dive (1.5 hrs): Process pools, sharing data between processes, parallel data processing, and scaling computational research workflows
Async Programming Basics (1 hr): Understanding async/await, asyncio fundamentals, and asynchronous data collection from multiple sources
Performance Profiling (30 min): Using cProfile, line_profiler, and memory_profiler to identify bottlenecks and optimize research code
Day 3: Testing, Deployment & Production Practices
Build robust, maintainable research software
Testing Frameworks (1.5 hrs): Unit testing with pytest, test-driven development, mocking external dependencies, and testing research algorithms and data pipelines
Code Quality & Documentation (45 min): Type hints, docstring standards, linting with pylint/flake8, and continuous integration basics for research projects
Deployment Strategies (1 hr): Virtual environments, Docker basics for reproducible research, creating Python packages, and sharing research tools
Integration & Scaling (45 min): Interfacing with other languages (C/C++, R), database connections, and when to move beyond Python for performance-critical research
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