Udemy

Python - Complete Python, Django, Data Science and ML Guide

Enroll Now
  • 2,511 Students
  • Updated 6/2025
  • Certificate Available
4.4
(287 Ratings)
CTgoodjobs selects quality courses to enhance professionals' competitiveness. By purchasing courses through links on our site, we may receive an affiliate commission.

Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Language
English
Taught by
Bogdan Stashchuk | 300K Students Worldwide | MBA, PhD
Certificate
  • Available
  • *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4.4
(287 Ratings)

Course Overview

Python - Complete Python, Django, Data Science and ML Guide

Learn the most popular Python programming language including Django, Pygame, Jupyter, Data Science and Machine Learning

Python is the easiest programming language in the world. But at the same time, Python is a powerful tool with which you can solve a huge range of different tasks, from file processing to machine learning, data processing, game creation and web application development .

Thus, having learned Python, you can choose a profession from a wide range of vacancies, or you can use Python to create your own applications and solve your own problems.

This course includes many practical tasks, as well as tasks for self-fulfillment.

Python is an object oriented programming language.

Python is also a language with a huge amount of features, but in order to be able to code in Python, you need to UNDERSTAND the key concepts of Python. And that's what I'm going to focus on with you in this course.

Before writing code and running examples, you will receive from me explanations and answers to questions WHY and WHY , and only after that HOW  to write code.

I will not waste your time and therefore I have created the most effective course structure. All the examples that I will explain and run are written by me before the course, but you will write and run the code yourself.

All video lectures in this course are over 50 hours long , but expect to spend around 500 hours to master all the topics of the course, including self-completion of all practical tasks.

In this course you will learn following key topics:


  1. Foundational Python Programming: Learn the fundamental concepts of Python programming, from data types, functions, and variables to control structures like loops and conditional statements.

  2. Object-Oriented Programming (OOP): Dive into the principles of OOP, understanding classes, objects, inheritance, encapsulation, and polymorphism, and discover how to leverage them for efficient code organization.

  3. File Handling and Modules: Explore file manipulation techniques, from working with directories and files using the os module to using external modules, enabling code reuse, and managing packages with PIP.

  4. Web Development with Django: Get an introduction to web development using Django, covering MVC architecture, URL routing, model creation, and interacting with databases to build dynamic web applications.

  5. API Development: Learn to create RESTful APIs using Django and handle API requests and responses, including authentication, authorization, and versioning.

  6. Game Development with Pygame: Enter the world of game development with Pygame, creating interactive games by working with graphics, animations, and user input.

  7. Data Manipulation with NumPy and Pandas: Discover data analysis and manipulation using NumPy and Pandas, covering array operations, dataframes, and handling real-world data sets.

  8. Error Handling: Understand error handling mechanisms in Python ensuring robust and reliable code.

  9. Package Management and Virtual Environments: Master package management using PIP, create virtual environments to isolate projects, and manage dependencies effectively.

  10. Visualization and Machine Learning: Explore data visualization with Matplotlib, and dip your toes into machine learning concepts with Scikit-Learn, covering model creation, evaluation, and prediction.

Why it's important: This course provides a comprehensive foundation in Python programming, from basic syntax to advanced topics like OOP, web and game development, data manipulation, and more. Understanding these concepts is crucial for building versatile applications, performing data analysis, and even stepping into machine learning, ensuring you're equipped for a wide range of programming tasks and projects.


After completing this course, you can safely say that you KNOW Python and CAN use the most popular Python functions.

As any of my courses this course comes with 30-days money back guarantee. No questions asked!

Course Content

  • 92 section(s)
  • 470 lecture(s)
  • Section 1 Introduction to Python
  • Section 2 Installing and Using PyCharm IDE
  • Section 3 Course and Project Files
  • Section 4 Basic Concepts in Python
  • Section 5 Introduction to Functions and Built-in Functions in Python
  • Section 6 Code Formatting and PEP8
  • Section 7 Comments
  • Section 8 Expressions and Instructions
  • Section 9 Variables
  • Section 10 Data Types and Structures
  • Section 11 Strings
  • Section 12 String Concatenation
  • Section 13 Numeric Types
  • Section 14 Boolean Type
  • Section 15 Magic Methods
  • Section 16 Lists
  • Section 17 Dictionaries
  • Section 18 Tuples
  • Section 19 Sets
  • Section 20 Ranges
  • Section 21 Working with Sequences
  • Section 22 Modifying Objects in Python
  • Section 23 Functions
  • Section 24 Function Arguments
  • Section 25 Args and kwargs in Functions
  • Section 26 Default Function Parameters
  • Section 27 Docstrings
  • Section 28 Callback Functions
  • Section 29 Global and Local Variables
  • Section 30 Operators
  • Section 31 Falsy and Truthy Values
  • Section 32 Logical and Comparison Operators
  • Section 33 Lambda Functions
  • Section 34 Error Handling
  • Section 35 Sequence Unpacking
  • Section 36 Unpacking Dictionaries
  • Section 37 Conditional Statements
  • Section 38 Ternary Operator
  • Section 39 For-In Loop
  • Section 40 While Loop
  • Section 41 For-In Expression (Comprehensions)
  • Section 42 Generators
  • Section 43 Decorator Functions
  • Section 44 Objects and Classes
  • Section 45 Instance and Class Methods
  • Section 46 Magic Methods in Classes
  • Section 47 Classes Extension
  • Section 48 Classes on Practice
  • Section 49 Key Principles in Object-Oriented Programming
  • Section 50 Modules
  • Section 51 Built-in Modules
  • Section 52 What is __name__ and __main__
  • Section 53 JavaScript Object Notation (JSON)
  • Section 54 Working with Files
  • Section 55 Working with Zip Archives
  • Section 56 Working with CSV Files
  • Section 57 Working with Dates and Times
  • Section 58 Generating Random Sequences and Passwords
  • Section 59 Math Module and Recursive Functions
  • Section 60 Regular Expressions
  • Section 61 Sending Emails
  • Section 62 Working with SQLite Database
  • Section 63 Other Built-in Modules
  • Section 64 Virtual Environments
  • Section 65 Pipenv for Virtual Environments Management
  • Section 66 Introduction to the Django Web Framework
  • Section 67 Creating a Django Project
  • Section 68 Creating a Django Application
  • Section 69 Database and Migrations in Django
  • Section 70 Creating Templates in Django
  • Section 71 Extending Other Templates in Django
  • Section 72 Creating Multiple Routes and View Functions
  • Section 73 Routing Between Pages in Django
  • Section 74 Creating an API Django Application
  • Section 75 Managing Authentication for API Requests
  • Section 76 Django Project Refactoring and Admin Settings
  • Section 77 Creating Games with Pygame
  • Section 78 Creating a Shooter Game with Pygame
  • Section 79 Interaction of the Elements in the Pygame
  • Section 80 Game Refactoring using Classes and OOP
  • Section 81 Jupyter Notebook
  • Section 82 Jupyter Lab
  • Section 83 NumPy - Creating Arrays
  • Section 84 NumPy - Random Values
  • Section 85 NumPy - Examples
  • Section 86 Pandas - Working with DataFrames and Series
  • Section 87 Pandas - Random Data and Working with CSV
  • Section 88 Pandas - Analysing CSV-Loaded DataFrames
  • Section 89 Matplotlib - Creating Charts
  • Section 90 Scikit-learn - Machine Learning
  • Section 91 Machine Learning Model for Real Data
  • Section 92 Making Machine Learning Model More Real

What You’ll Learn

  • You will learn the basic principles of Python and learn all the main functions that are used most often in real projects and are in demand the most
  • You will practice all the examples that I will show throughout the course using the Python interpreter, Visual Studio Code with Code Runner
  • You will master all the necessary packages for Data Science and Machine Learning such as NumPy, Pandas, Matplotlib and Scikit-learn using Jupyter Notebook
  • You will learn the basic functionality of Python, ranging from variables, lists, dictionaries, to classes, loops, modules, and creating virtual environments
  • In addition, you will learn how to use functional and object-oriented approaches in Python programming.


Reviews

  • A
    Alexey Serdtse
    5.0

    Great explanation of the subject, returned especially for this author to refresh some of the topics.

  • A
    Amos Tumelo Kama
    5.0

    He is so far making the learning simple and understandable

  • V
    Vishwas Vilas Kulkarni
    4.5

    Teaching is excellent and I am very satisfied with the way everything is explained.

  • R
    Ross Shehov
    5.0

    Great structure and explanations so far

Start FollowingSee all

We use cookies to enhance your experience on our website. Please read and confirm your agreement to our Privacy Policy and Terms and Conditions before continue to browse our website.

Read and Agreed