150+ Exercises - Object Oriented Programming in Python - OOP
What you'll learn
- solve 150+ exercises in Python object-oriented programming - OOP
- namespaces and scopes (local, enclosing, global, built-in)
- LEGB rule
- use of *args and **kwargs
- classes and objects
- visibility of variables (public, protected, private)
- class attributes
- instance attributes
- decorator @property
- computed attributes
- class method, decorator @classmethod
- static method, decorator @staticmethod
- special methods: __new__(), __init__(), __repr__(), __str__(), __len__() and many more
- single inheritance, multiple inheritance
- MRO - Method Resolution Order
- super()
- abstract classes
- the ABC class and the @abstractmethod decorator
- work with documentation and Stack Overflow
- guaranteed instructor support
Course content
- 00:12Info
- Preview02:55
- Preview03:47
- 09:57Google Colab - Intro
- Preview04:07
- 02:56Introduction to Spyder
- 04:39Anaconda installation - Linux
Requirements
- completed 200+ Exercises - Programming in Python - from A to Z
- completed 210+ Exercises - Python Standard Libraries - from A to Z
- basic knowledge of OOP
Description
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RECOMMENDED LEARNING PATH
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200+ Exercises - Programming in Python - from A to Z
210+ Exercises - Python Standard Libraries - from A to Z
150+ Exercises - Object Oriented Programming in Python - OOP
100+ Exercises - Unit tests in Python - unittest framework
100+ Exercises - Python Programming - Data Science - NumPy
100+ Exercises - Python Programming - Data Science - Pandas
100+ Exercises - Python - Data Science - scikit-learn
250+ Exercises - Data Science Bootcamp in Python
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COURSE DESCRIPTION
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150+ Exercises - Object Oriented Programming in Python - OOP
Welcome to the course 150+ Exercises - Object Oriented Programming in Python - OOP, where you can test your Python programming skills in object-oriented programming (OOP) and complete over 150 exercises!
The course is designed for people who have basic knowledge in Python and OOP concepts. It consists of over 150 exercises with solutions. The course is focused on practical learning.
This is a great test for people who are learning the Python language and OOP and are looking for new challenges. Exercises are also a good test before the interview. If you're wondering if it's worth taking a step towards Python, don't hesitate any longer and take the challenge today!
Who this course is for:
- students who want to learn object-oriented programming
- students who want to improve Python programming skills
- students preparing for an interview
Instructor
![Paweł Krakowiak](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.6 Instructor Rating
- 1,231 Reviews
- 23,817 Students
- 35 Courses
EN
Data Scientist/Python Developer/Securities Broker
Founder at e-smartdata[.]org.
A big fan of new technologies, especially in the areas of artificial intelligence, big data and cloud solutions.
A graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science in the Big Data specialization.
A graduate of Master's Degree in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science of the University of Lodz.
Stockbroker license holder with experience in teaching at a university.
Lecturer at the GPW Foundation (technical analysis, behavioral finance and portfolio management).
The main areas of interest are artificial intelligence, machine learning, deep learning and financial markets.
PL
Data Scientist, Securities Broker
Założyciel platformy e-smartdata[.]org
Miłośnik nowych technologii, szczególnie w obszarze sztucznej inteligencji, big data oraz rozwiązań chmurowych.
Absolwent podyplomowych studiów na Polsko-Japońskiej Akademii Technik Komputerowych na kierunku Informatyka, spec. Big Data.
Absolwent studiów magisterskich z matematyki finansowej i aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego.
Od 2015 roku posiadacz licencji maklera papierów wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego.
Wykładowca w Fundacji GPW prowadzący szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych.
Z doświadczeniem w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką.
Główne obszary zainteresowań to sztuczna inteligencja, uczenie maszynowe, uczenie głębokie i rynki finansowe.