Applied Scientific Python

A hands-on, 4-day intensive course for scientists, engineers, and analysts who want to write efficient, testable, and deployable scientific code in Python.

---

Course Overview

This workshop bridges the gap between scientific scripts and engineering-quality software.

You'll learn how to:

• Write clean, idiomatic Python using functools, itertools, and the standard library
• Structure reusable scientific models using OOP and modules
• Manage experimental data with SQLAlchemy ORM
• Analyze and clean large datasets using pandas and NumPy
• Profile and optimize performance with Cython, multiprocessing, and vectorized techniques
• Debug and test with pytest, unittest, pdb, and traceback
• Build command-line tools using argparse and click
• Package and distribute Python tools (setup.py, metadata, editable installs)
• Automate testing and deployment using GitLab CI/CD pipelines

---

Course Structure

Each day includes short lectures, live walkthroughs, and hands-on tasks.

Day	Focus
1	Pythonic Code, Testing & Debugging
2	Object-Oriented Programming & CLI Tools
3	ORM, Performance Tuning & Pipelines
4	Packaging, CI/CD, and Deployment

---

Daily Breakdown

Day 1

AM – Python Refresher
Functions, generators, comprehensions, functools, itertools

• Tutorials: 01_recap.py, 02_intro_to_functools.py, 03_intro_to_itertools.py
• Tasks: tasks/01_recap_task.py, 02_functools_task.py, 03_itertools_task.py

PM – Testing & Debugging
pytest, unittest, traceback, pdb

• Tutorials: 01_intro_to_pytest.py, 02_intro_to_unittest.py, 03_debugging.py
• Tasks: tasks/01_pytest_task.py, 02_unittest_task.py, 03_debugging_tasks.py

---

Day 2

AM – OOP Essentials
OOP patterns, class composition, real-world abstractions

• Tutorials: 01_intro_to_oop.py, 02_intro_to_abstraction.py, 03_real_world_example_abstraction.py
• Tasks: tasks/01_oop_task.py, 02_abstraction_task.py, 03_inheritance_task.py

PM – Command-Line Interfaces
argparse, click, CLI tool packaging

• Tutorials: 01_python_and_cli.py, 02_using_cli_tools.py
• Tasks: tasks/01_cli_tools_task.py, 02_create_your_own.py

---

Day 3

AM – SQLAlchemy ORM
ORM modelling, relationships, querying patterns

• Tutorials: 01_intro_to_data_models.py, 02_intro_to_sqlalchemy.py, 03_intro_to_sqlalchemy_modelling.py

PM – Performance Tuning
Vectorization, multiprocessing, Cython, N+1 fixes

• Tutorials: 01_query_optermisation.py, 02_pandas_cleaning_task.py, 03_cython_multiprocessing.py
• Tasks: 01_find_the_optermisation_task.py, 02_using_pandas_task.py

---

Day 4

AM – Packaging & Versioning
setup.py, requirements, semantic versioning

• Tutorials: 01_setup_recap/, 02_versioning_strategies/, 03_installation_strategies/
• Tasks: 01_semantic_versioning_changelog.md, 02_installation_strategy_comparison.md

PM – CI/CD with GitLab
.gitlab-ci.yml, test automation, publishing

• Guides: 02_writing_effective_gitlab_ci.md, 03_twine_and_pypi_deployment.md, 04_automated_cli_jobs.md
• Tasks: 01_write_and_run_pytest_ci.md, 02_deploy_dry_run.md

Requirements

• Python 3.9+
• Git (with GitLab access)
• Code editor (VS Code, PyCharm, etc.)

Install dependencies:

pip install -r requirements.txt