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2021-03-04 11:01:03
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Development Programming Languages Object Oriented Programming

150+ Exercises - Object Oriented Programming in Python - OOP

Test your Python programming skills in object-oriented programming (OOP) and solve over 150 exercises! - Python
Rating: 4.7 out of 54.7 (15 ratings)
5,056 students
Created by Paweł Krakowiak
Last updated 2/2021
English
English
30-Day Money-Back Guarantee

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

20 sections • 161 lectures • 48m total length

  • Info
    00:12
  • Preview02:55
  • Preview03:47
  • Google Colab - Intro
    09:57
  • Preview04:07
  • Introduction to Spyder
    02:56
  • Anaconda installation - Linux
    04:39

  • A few words from the author
    00:20

  • Exercise 1
    1 question
  • Solution 1
    00:00

  • Exercise 1
    1 question
  • Solution 1
    00:01
  • Exercise 2
    1 question
  • Solution 2
    00:07
  • Exercise 3
    1 question
  • Solution 3
    00:06

  • Exercise 1
    1 question
  • Solution 1
    00:03
  • Exercise 2
    1 question
  • Solution 2
    00:01
  • Exercise 3
    1 question
  • Solution 3
    00:02
  • Exercise 4
    1 question
  • Solution 4
    00:04
  • Exercise 5
    1 question
  • Solution 5
    00:05

  • Exercise 1
    1 question
  • Solution 1
    00:06
  • Exercise 2
    1 question
  • Solution 2
    00:03

  • Exercise 1
    1 question
  • Solution 1
    00:00
  • Exercise 2
    1 question
  • Solution 2
    00:01
  • Exercise 3
    1 question
  • Solution 3
    00:01
  • Exercise 4
    1 question
  • Solution 4
    00:02
  • Exercise 5
    1 question
  • Solution 5
    00:02
  • Exercise 6
    1 question
  • Solution 6
    00:02
  • Exercise 7
    1 question
  • Solution 7
    00:02
  • Exercise 8
    1 question
  • Solution 8
    00:02
  • Exercise 9
    1 question
  • Solution 9
    00:01
  • Exercise 10
    1 question
  • Solution 10
    00:03
  • Exercise 11
    1 question
  • Solution 11
    00:05
  • Exercise 12
    1 question
  • Solution 12
    00:01
  • Exercise 13
    1 question
  • Solution 13
    00:02

  • Exercise 1
    1 question
  • Solution 1
    00:01
  • Exercise 2
    1 question
  • Solution 2
    00:02
  • Exercise 3
    1 question
  • Solution 3
    00:03
  • Exercise 4
    1 question
  • Solution 4
    00:04
  • Exercise 5
    1 question
  • Solution 5
    00:05
  • Exercise 6
    1 question
  • Solution 6
    00:05
  • Exercise 7
    1 question
  • Solution 7
    00:05
  • Exercise 8
    1 question
  • Solution 8
    00:04
  • Exercise 9
    1 question
  • Solution 9
    00:05
  • Exercise 10
    1 question
  • Solution 10
    00:03
  • Exercise 11
    1 question
  • Solution 11
    00:03

  • Exercise 1
    1 question
  • Solution 1
    00:03
  • Exercise 2
    1 question
  • Solution 2
    00:06
  • Exercise 3
    1 question
  • Solution 3
    00:10
  • Exercise 4
    1 question
  • Solution 4
    00:10
  • Exercise 5
    1 question
  • Solution 5
    00:03
  • Exercise 6
    1 question
  • Solution 6
    00:07
  • Exercise 7
    1 question
  • Solution 7
    00:09

  • Exercise 1
    1 question
  • Solution 1
    00:04
  • Exercise 2
    1 question
  • Solution 2
    00:06
  • Exercise 3
    1 question
  • Solution 3
    00:06
  • Exercise 4
    1 question
  • Solution 4
    00:04
  • Exercise 5
    1 question
  • Solution 5
    00:04
  • Exercise 6
    1 question
  • Solution 6
    00:06
  • Exercise 7
    1 question
  • Solution 7
    00:06
  • Exercise 8
    1 question
  • Solution 8
    00:09
  • Exercise 9
    1 question
  • Solution 9
    00:10

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

------------------------------------------------------------------------------

RECOMMENDED LEARNING PATH

------------------------------------------------------------------------------

  • 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

------------------------------------------------------------------------------

COURSE DESCRIPTION

------------------------------------------------------------------------------

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:

  • everyone who wants to learn by doing
  • everyone who wants to learn object-oriented programming
  • everyone who wants to improve Python programming skills
  • everyone who wants to prepare for an interview

Instructor

Paweł Krakowiak
Data Scientist, Securities Broker
Paweł Krakowiak
  • 4.6 Instructor Rating
  • 1,265 Reviews
  • 23,920 Students
  • 37 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.

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