Python for Data Science

Self-taught coursework and practice as I learn Python fundamentals and OOP, on my way toward Python/ML for data science — part of a broader path that started with Data Analyst fundamentals (SQL, Excel, Tableau). This is ongoing coursework, not polished end-to-end projects — code here reflects active learning, not finished production work.

Structure

Folder	Contents	Status
Basic/	Lectures 1-7 — variables, control flow, functions, file I/O, with practice files per lecture	✅ Complete
Practice_Questions/	11 standalone practice problems	🟡 Ongoing
Python-OOP/	Lectures 8-9 on OOP, plus applied projects: Bank, Box_Area, School_System, Student_Grade, Vehicle_System	✅ Complete
notes/	Reference notes — Python fundamentals, OOP concepts, zip/slicing	✅ Complete

What I learned

Coming into Python with a Java background, most of the friction wasn't logic — it was syntax habits I had to unlearn:

# Java instinct
if (x > 5) {
    doSomething();
}

# Python
if x > 5:
    do_something()

• No parentheses around conditions. Java's if (x > 5) muscle memory kept showing up; Python just wants if x > 5:.
• Indentation is the block structure, not {}. Spacing isn't a style choice — get it wrong and the code doesn't run.
• No semicolons. Small thing, but it took a while to stop typing them out of habit.

Beyond syntax, working through File I/O and then OOP — where classes translate differently than Java's — was where things actually started clicking: seeing the same OOP concepts (encapsulation, classes/objects) expressed with Python's simpler syntax.