Introduction
Syllabus
Learn the most important programming concepts in just weeks and get hands-on experience with Python. Don't let yourself be that data scientist who never quite understood what a class is. Give yourself a boost with this 6-session focused course. We'll cover everything that a modern programmer needs. From the very basics of variables, data structures, functions and classes to cutting edge of AI coding agents.
Session 1 — Python foundations
Setup: conda/venv, Jupyter vs scripts, VS Code, pip, project structure
Core syntax: variables, types, operators, strings, f-strings
Control flow: if/elif/else, for, while, break/continue
Functions basics: def, parameters, return values
Debugging basics: reading tracebacks, print vs debugger
Session 4 — Files, exceptions, and data I/O
Reading/writing text + CSV/JSON
Context managers: with open(...)
Error handling: try/except/else/finally, creating helpful errors
Basic logging (why you don’t spam prints in real projects)
Exercises: load a dataset, validate it, handle missing data
Session 3 — Functions deeper, modules, and clean code
Scope, LEGB, side effects vs pure functions
Default args, args-kwargs, type hints
Writing reusable modules + imports
Intro to classes, OOP design
Docstrings, naming, style (PEP 8), formatting tools
Session 2 — Data structures & iteration patterns
Lists, tuples, dicts, sets: when/why to use each
Indexing/slicing, mutability, copying semantics
Looping patterns: enumerate, zip, iterating dicts safely
Comprehensions (list/dict/set) + generator expressions
Sorting/key functions, lambda
Schedule & Location
A 6-session, in-person course in the heart of Budapest. Classes are held on Sundays, usually from 9am to 1-2pm.
Pricing
Introduction to Python - 150.000 HUF
Session 5 — NumPy essentials for statistics
Why NumPy: arrays vs lists, vectorization
Creating arrays, dtype, shape, reshape
Indexing/slicing, boolean masks
Broadcasting, axis logic
Core ops for stats: sum/mean/std, normalization, z-scores
Exercises: implement mean/variance/covariance from scratch & with NumPy
Session 6 — Pandas essentials + mini data workflow
Series/DataFrame mental model
Loading data, inspection, dtypes, missing values
Filtering, assign, apply vs vectorized ops
Groupby/aggregation, joins/merge basics
Simple EDA workflow + exporting results
Capstone mini-task: clean + analyze a small dataset end-to-end