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.
There are no prerequisites for this course, it is designed for beginners and intermediates.
Session 1 — Python foundations & installation
Setup: conda/venv, Jupyter vs scripts, pip, project structure
Core syntax: variables, types, operators, 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 and exceptions
Working with files (text,CSV,JSON, pickle)
Context managers: with open(...)
Error handling: try/except/else/finally, creating helpful errors
Basic logging
Take home exercise: automated data collection
Session 3 — Functions and style
Scope, LEGB, side effects vs pure functions
Default args, args-kwargs, type hints
Writing reusable modules + imports
Intro to classes, OOP design, classes vs functions
Docstrings, naming, style (PEP 8), formatting tools
Session 2 — Data structures
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.
Tentative schedule for the next course in 2026:
March 29th, April 12th, April 19th, April 26th, May 10th, May 17th
Pricing
Introduction to Python - 150.000 HUF
Session 5 — NumPy essentials
Why NumPy: arrays vs lists, vectorization
Indexing/slicing, boolean masks
Broadcasting, axis logic
Core operations: 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
Final task: data analysis pipeline