Course Index

PyPro-SCiDaS

An Initiation to Programming using Python (Init2Py)

📚 Course Index & Navigation

Python Proficiency for Scientific Computing and Data Science

🧑‍🏫 Instructor: Yaé Gaba	📘 Course: Init2Py	📅 Date: October 2025	🎓 Semester: Semester 1, 2025–2026
🏛️ AI Research and Innovation Nexus for Africa (AIRINA Labs), AI.Technipreneurs, Bénin & African Center for Advanced Studies (ACAS), Cameroon
✉️ yaeulrich.gaba@gmail.com   |   🔗 LinkedIn | 🌐 Website

Welcome to PyPro-SCiDaS

PyPro-SCiDaS (Python Proficiency for Scientific Computing and Data Science) is a structured, hands-on course designed to take you from zero programming experience to proficiency in Python for scientific computing and data science.

The course features 16 interactive Jupyter notebook lectures that build foundational skills progressively. You’ll start by navigating the terminal and writing your first Python script, then advance through object-oriented programming and comprehensions, before diving into data analysis with NumPy, Matplotlib, and Pandas.

Navigating the course: Click any lecture title below to open it directly. Each lecture includes navigation links to help you move through the course sequentially.

Quick Navigation

1. Part I — Foundations (Lectures 0–4)
2. Part II — Core Programming (Lectures 5–9)
3. Part III — Scientific Computing (Lectures 10–12)
4. Part IV — Advanced Python & Data Analysis (Lectures 13–17)

📗 Part I — Foundations

Getting started with the terminal, Python basics, and version control

🐚	#0	The Shell	Terminal, filesystem, pipes, grep, find
🐍	#1	Introduction to Python	Syntax, expressions, data types, print/input
📦	#2	Variables, Types, and Assignment	Dynamic typing, int/float/complex/bool, casting
📝	#3	Strings and Files	String methods, f-strings, file I/O, pathlib
💻	#4	Version Control with Git	init, add, commit, branches, merge, diff

📘 Part II — Core Programming

Data structures, control flow, functions, and error handling

📦	#5	Iterable Objects or Containers	Lists, tuples, dicts, sets, mutability
🔀	#6	Flow Control and Loops	if/elif/else, for, while, break/continue
⚙️	#7	Functions, Modules, and Packages	def, scope, *args/**kwargs, imports
🔄	#8	Recursion and Lambda	Recursive algorithms, lambda, map/filter
🐛	#9	Errors and Debugging	Exceptions, try/except, pdb, tracebacks

📙 Part III — Scientific Computing

Numerical arrays, visualization, and high-performance computing

📊	#10	NumPy — Part 1	Arrays, indexing, slicing, reshaping
📊	#11	NumPy — Part 2	Ufuncs, broadcasting, vectorization
🎨	#12	Matplotlib	Line/scatter/bar/histogram, subplots, LaTeX

📕 Part IV — Advanced Python & Data Analysis

Object-oriented design, Pythonic idioms, and real-world data analysis

🏗️	#13	Object-Oriented Programming	Classes, inheritance, encapsulation, polymorphism
💨	#14	List Comprehensions & Generators	List/dict/set comprehensions, generators, yield
🐼	#15	Pandas for Data Analysis	Series, DataFrames, CSV I/O, groupby, merge
🎀	#16	Decorators & Context Managers	Closures, @property, functools, with statement
📂	#17	Data Formats & APIs	JSON, CSV, YAML, TOML, requests, web APIs

🗺️ Learning Path

Part I
Foundations
Lectures 0–4

→

Part II
Core Programming
Lectures 5–9

→

Part III
Scientific Computing
Lectures 10–12

→

Part IV
Advanced Python
Lectures 13–17

Prerequisites & Setup

No prior programming experience is needed! This course is designed for absolute beginners. You will need:- Python 3.8+ installed on your system- Jupyter Notebook or JupyterLab- Later lectures also use NumPy, Matplotlib, and Pandasbash# Install everything you needpip install jupyter numpy matplotlib pandasTo get started, click on Lecture 0 above, or use the link below:

🚀 Start the Course — Lecture 0: The Shell

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PyPro-SCiDaS • Python Proficiency for Scientific Computing and Data Science

Built with ❤️ using Jupyter Notebooks

© 2025 Yaé Gaba — CC BY-NC 4.0