Python Programming- From Novice to Pythonista E-Learning Training

Python continues to be one of the fastest-growing programming languages ​​in the market today. Because of its ease of use and numerous supporting frameworks, it is widely used in web development, writing scripts, automating tasks, data science, and even cybersecurity.

Course content

This learning path, with more than 95 hours of online content, is divided into the following four tracks:

Pythonista Track 1: Python Novice
Pythonista Track 2: Python Apprentice
Pythonista Track 3: Python Journeyman
Pythonista Track 4: Pythonista

Track 1: Python Novice

In this track of the Pythonista journey, the focus is getting started with Python, complex data types, conditional statements and loops, and first-class functions and lamdas.

Content:
E-learning courses

Getting Started with Python: Introduction

Course: 1 Hour, 30 Minutes

• Course Overview Python Introduction
• Install and Set up Anaconda on Windows for Python
• Run Jupyter notebooks on Windows for Python
• Install and Set up Anaconda on MacOS for Python
• Run Jupyter notebooks on MacOS for Python
• Using Python as a Calculator
• Working with Python Built-in Functions
• Introducing Python Variables to Store Values
• Working with Different Types of Variables in Python
• Assigning Values to Variables in Python
• Updating Variable Values in Python
• Working with Python Simple Data Types
• Creating Single-line and Multi-line Strings in Python
• Formatting Operations with Strings in Python
• Exercise: Python Jupyter Notebooks, Functions, & Variables

Complex Data Types in Python: Working with Lists & Tuples in Python

Course: 1 Hour, 39 Minutes

• Course Overview
• Introducing Lists
• Performing Simple List Operations
• Performing Useful List Operations
• Using Built-in Functions with Lists
• Perform Slicing Operations on Lists
• Using Step Size in Slicing Operations
• Working with Strings as a List of Characters
• Invoking Functions on Strings
• Perform Slicing Operations on Strings
• Introducing Tuples
• Understanding Tuple Immutability
• Introducing Other Complex Data Types
• Exercise: Lists, Tuples, Similar Yet Different

Complex Data Types in Python: Working with Dictionaries & Sets in Python

Course: 53 Minutes

• Course Overview
• Introducing Dictionaries
• Nesting Complex Data Types Within Dictionaries
• Invoking Functions on Dictionaries
• Introducing Sets
• Performing Set Operations
• Working with Nested Lists
• Performing List Conversions
• Exercise: Dictionaries and Sets

Complex Data Types in Python: Shallow & Deep Copies in Python

Course: 45 Minutes

• Course Overview
• Copying Strings
• Performing Shallow Copies of Lists
• Performing Deep Copies of Lists
• Creating Shallow and Deep Copies of Tuples
• Creating Shallow Copies of Dictionaries
• Creating Deep Copies of Dictionaries
• Creating Shallow and Deep Copies of Sets
• Exercise: Shallow and Deep Copies

Conditional Statements & Loops: If-else Control Structures in Python

Course: 1 Hour, 41 Minutes

• Course Overview
• Python Conditions
• If Statements with Primitive Datatypes
• If Statements with Complex Datatypes
• If-else Elif Statements
• Nested If-else Statements
• If-else Statements with Complex Datatypes
• Type Conversions with Primitive Datatypes
• Type Conversions with Complex Datatypes
• Type Conversions and Base Conversions
• Basic Programs - Part 1
• Basic Programs - Part 2
• Basic Programs - Part 3
• Basic Programs - Part 4
• Exercise: If-else Statements in Python

Conditional Statements & Loops: The Basics of for Loops in Python

Course: 1 Hour, 2 Minutes

• Course Overview
• Iterating over Elements in a List
• Iterating over Elements in a Tuple and Dictionary
• The else Block of a for Loop
• Nested Control Structures in a for Loop
• An Introduction to the range Function
• Setting Intervals in a Range
• Exploring the range Function
• Exercise: Basics of Python for Loops

Conditional Statements & Loops: Advanced Operations Using for Loops in Python

Course: 1 Hour, 6 Minutes

• Course Overview
• Introducing the break Statement
• The break Statement and the else block
• The continue Statement: Part 1
• The continue Statement: Part 2
• The pass Statement
• Introducing Comprehensions
• Applying Conditions in Comprehensions
• Exercise: Advanced Operations in for Loops

Conditional Statements & Loops: While Loops in Python

Course: 1 Hour, 20 Minutes

• Course Overview
• An Introduction to While Loops
• Basic While Loops - Part 1
• Basic While Loops - Part 2
• Single-line While Loops
• Evaluating Complex Data with While Loops - Part 1
• Evaluating Complex Data with While Loops - Part 2
• Exit a While Loop Using the Break Statement1
• Using the Pass Keyword in a While Loop
• The Continue Statement in a While Loop
• Exercise: While Loops in Python

Functions in Python: Introduction

Course: 2 Hours, 4 Minutes

• Course Overview
• Getting Started with Functions
• Working with Functions
• Functions as Objects
• Input Arguments - Invoking Functions
• Input Arguments - Referencing Global Variables
• Input Arguments - Using Positional Arguments
• Return Values - Functions
• Return Values - Multi-return Statements
• Return Values - Complex Data Types
• Keyword Arguments - Invoking Functions
• Keyword Arguments - Nuances
• Default Arguments
• Variable Length Arguments - *args Variable
• Variable Length Arguments - Combinations
• Variable Length Arguments - **kwargs Keyword6
• Exercise: Introduction to Functions in Python

Functions in Python: Gaining a Deeper Understanding of Python Functions

Course: 1 Hour, 27 Minutes

• Course Overview
• Global and Local Variables
• Argument Passing by Value
• Argument Passing by Reference
• Math and OS Modules
• Random and Datetime Modules
• Functions as Arguments - Input Arguments
• Functions as Arguments - Variable Arguments
• Functions as Return Values
• Lambdas - Define and Invoke
• Lambdas - Define, Invoke, and Discard
• Lambdas - Filter() Function
• Exercise: Definining Python Functions

Functions in Python: Working with Advanced Features of Python Functions

Course: 1 Hour, 27 Minutes

• Course Overview
• Recursion - Invoking Functions
• Recursion - Conditions
• Recursion - Calls
• Generator Functions
• Generators for Infinite Sequences
• Closures
• Closures and Local State
• Decorators - Code Modification
• Decorators - Customization
• Chaining Decorators
• Exercise: Advanced Features in Python Functions

Online Mentor

You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment

Estimated duration: 90 minutes

Practice Labs: Python Novice (estimated duration: 8 hours)

Practice novice Python development tasks such as formatting data types, implementing flow control and conditionals, copying containers, and performing loops with list comprehension methods. Then, test your skills by answering assessment questions after converting data types, working with global and local variables within functions, invoking functions with varying parameters and implementing recursive functions and closures. This lab provides access to tools typically used when developing with Python, including:

• Python, Anaconda
• Jupyter Notebook + JupyterHub
• Pandas
• NumPy
• SiPy
• Seaborn Library
• PyCharm IDE
• Spyder IDE
• MongoDB
• MySQL,
• VS Code

Track 2: Python Apprentice

In this track of the Pythonista journey, the focus is Python classes and inheritance and also data structures and algorithms.

Content:
E-learning courses

Advanced Python Topics: File Operations in Python

Course: 1 Hour, 12 Minutes

• Course Overview
• Opening a File in Python
• The Different Read Functions
• Writing to Files in Python
• The r+ and a+ Modes
• Reading JSON Data in Python
• Transforming Python Objects into JSON
• Parsing Different Forms of CSV files
• Transforming Python Objects into CSV
• CSV Dialects
• Course Summary

Advanced Python Topics: Exceptions & Command Line Arguments

Course: 1 Hour, 13 Minutes

• Course Overview
• The try and except Blocks
• Defining Custom Exception Handlers
• The Exception Hierarchy
• Chaining except Blocks
• Going from Jupyter Notebooks to Python Scripts
• Using the Python Shell
• An Introduction to Parsing Command Line Arguments
• Using Command Line Arguments in a Script
• Define Command-Line Arguments using argparse
• Course Summary

Advanced Python Topics: Python Modules & Virtual Environments

Course: 1 Hour

• Course Overview
• Importing Modules in Python
• Activating Virtual Environments
• Commonly Used Python Modules
• Packaging a Custom Module
• Installing and Using a Custom Module
• Creating Virtual Environments Using virtualenv
• Configuring Virtual Environments
• Course Summary

Advanced Python Topics: Migrating from Python 2 to Python 3

Course: 40 Minutes

• Course Overview
• Installing a Python 2 Kernel for Jupyter Notebook
• Differences Between Python 2 and Python 3 - Part 1
• Differences Between Python 2 and Python 3 - Part 2
• Using 2to3 to Identify Python 3 Compatibility
• Transforming Python 2 Scripts Using 2to3
• Course Summary

Python Classes and Inheritance: Introduction

Course: 50 Minutes

• Overview
• Introduction to Classes
• Classes as Blueprints
• Objects
• Inheritance
• Object-Oriented Programming
• Exercise: Introduction to Classes and Inheritance

Python Classes & Inheritance: Getting Started with Classes in Python

Course: 1 Hour, 35 Minutes

• Course Overview
• Classes as Custom Data Types
• Associating Attributes with Classes
• Initializing Class Objects
• Passing Arguments for Initialization
• Defining Additional Methods in Classes
• Introducing Class Variables
• Class Variables and Instance Variables
• Class Variable Memory Sharing
• Instance Variables
• Getters and Setters for Private Variables
• Making Variables Private
• Create a Classes to Represent a Student
• Parse Student Details from a Dictionary
• Exercise: Characteristics of Classes

Python Classes & Inheritance: Working with Inheritance in Python

Course: 1 Hour, 10 Minutes

• Course Overview
• Inheriting from the Object Base Class
• Modeling is-a Relationship Using Subclasses
• Invoking Base Class Methods from Subclasses
• Defining Implementations of Base Class Methods
• Superclass and Subclass Hierarchies
• Defining Methods in the Subclass
• Multiple Inheritance
• Multilevel Inheritance
• Polymorphism - Part 1
• Polymorphism - Part 2
• Exercise: Implementing Class Inheritance

Python Classes & Inheritance: Advanced Functionality Using Python Classes

Course: 1 Hour, 29 Minutes

• Course Overview
• The repr and str Special Methods
• The add Special Method
• The sub Special Method
• The mul Special Method
• Special Methods for Other Operations
• Built-in Functions and Custom Data Types
• Custom Iterators Using Special Methods
• Defining Properties on Classes
• Defining Properties Using Decorators
• Class Methods
• Static Methods
• Abstract Base Classes
• Exercise: Advanced Functionality in Classes

Data Structures & Algorithms in Python: Fundamental Data Structures

Course: 1 Hour, 20 Minutes

• Course Overview
• An Overview of Data Structures
• Measuring the Performance of Operations
• The Big O Notation
• An Introduction to Linked Lists
• Adding and Searching for Data in a Linked List
• Deleting Nodes from a Linked List
• Counting the Nodes in a Linked List
• An Introduction to Stacks
• Additional Stack Operations
• An Introduction to Queues
• Exercise: Fundamental Data Structures

Data Structures & Algorithms in Python: Implementing Data Structures

Course: 1 Hour, 30 Minutes

• Course Overview
• O(1) Operations
• O(n) Operations - Part 1
• O(n) Operations - Part 2
• O(n*n) Operations
• Python's Built-in Queue
• Defining a Custom Queue
• Use a Python List as a Stack
• Defining a Custom Stack
• Linked Lists: Defining Insert Operations
• Linked Lists: Search, Delete, and Reverse Operations
• Linked Lists: Testing the Functions
• Exercise: Implementing Data Structures in Python

Data Structures & Algorithms in Python: Sorting Algorithms

Course: 1 Hour, 16 Minutes

• Course Overview
• An Introduction to Sorting
• The Selection Sort
• The Bubble Sort - Part 1
• The Bubble Sort - Part 2
• The Insertion Sort
• The Shell Sort
• The Merge Sort
• The Quicksort - Part 1
• The Quicksort - Part 2
• Exercise: Sorting Algorithms

Data Structures & Algorithms in Python: Implementing Sorting Algorithms

Course: 1 Hour, 11 Minutes

• Course Overview
• The Selection Sort
• The Bubble Sort
• The Insertion Sort
• Coding the Shell Sort Algorithm
• Testing and Analyzing the Shell Sort Algorithm
• The Merge Sort
• Coding the Quicksort Algorithm
• Testing and Analyzing the Quicksort Algorithm
• Exercise: Implementing Sorting Algorithms

Data Structures & Algorithms in Python: Trees & Graphs

Course: 1 Hour, 34 Minutes

• Course Overview
• The Binary Search
• The Binary Search Tree
• BST: Insert and Lookup
• BST: Extreme Values, Max Depth, and Sum Path
• BST: Breadth First Traversal
• BST: Depth First Traversal - Pre-Order and In-Order
• BST: Depth First Traversal - Post-Order
• An Introduction to Graphs
• Graphs as an Adjacency Matrix
• Graphs as an Adjacency List and Set
• The Topological Sort
• Exercise: Trees and Graphs

Data Structures & Algorithms in Python: Implementing Trees & Graphs

Course: 1 Hour, 27 Minutes

• Course Overview
• Implementing a Binary Search
• Defining a Binary Search Tree
• Common Operations on a BST
• Breadth First Traversal of a BST
• Depth First Traversal of a BST
• Graphs: The Building Blocks
• Graphs: The Adjacency Set Representation
• Graphs: Testing the Adjacency Set
• Graphs: The Adjacency Matrix
• Graphs: A Breadth First Traversal
• Graphs: A Depth First Traversal
• Graphs: The Topological Sort
• Exercise: Implementing Trees and Graphs in Python

Online Mentor

You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment

Estimated duration: 90 minutes

Practice Labs: Python Apprentice (estimated duration: 8 hours)

Perform apprentice level Python development tasks such as file handling, implementing polymorphism, implementing special method names, as well as implementing an abstract class and using static methods. Then, test your skills by answering assessment questions after using a Python list as a stack, performing queue operations, implementing a graph as an adjacency matrix, and traversing a Binary Search Tree (BST).

Track 3: Python Journeyman

In this track of the Pythonista journey, the focus will be on Python Unit Testing, Python HTTP requests, Flask in Python, and Python concurrent programming.

Content:
E-learning courses

Python Unit Testing: An Introduction to Python's unittest Framework

Course: 50 Minutes

• Course Overview
• An Introduction to the unittest Framework
• Running Multiple Tests with unittest
• Naming the Test Function
• Selection of Specific Tests to Run
• The Assert Functions
• Skipping Tests
• Course Summary

Python Unit Testing: Advanced Python Testing Using the unittest Framework

Course: 1 Hour, 1 Minute

• Course Overview
• The Case for Fixtures
• setUp and tearDown Functions in a Test Script
• setUpClass & tearDownClass Functions in a Script
• Define and Run Test Suites
• Test Suites with makeSuite
• Install the PyCharm IDE
• Unit Tests with the unittest Framework Using PyCharm
• Unit Tests Testing Multiple Functions Using PyCharm
• Course Summary

Python Unit Testing: Testing Python Code Using pytest

Course: 1 Hour, 14 Minutes

• Course Overview
• An Introduction to pytest
• Running Multiple Tests using pytest
• Test Selection in pytest
• Halt Test Execution Based on Failures
• Test Markers and Skipping Tests
• Using the Python Debugger with pytest
• Parametrized Tests Using pytest
• Test Fixtures with pytest
• Setting the Scope for Test Fixtures
• The conftest.py File
• Course Summary

Python Unit Testing: Testing Python Code Using doctest

Course: 44 Minutes

• Course Overview
• The doctest Module
• The Placement of doctests
• An Executable Document
• Handle Unpredictable Outputs
• Test for Exceptions
• Handle Whitespace in the Output
• Course Summary

Python Requests: HTTP Requests with Python

Course: 1 Hour, 42 Minutes

• Course Overview
• Installing the Requests Package
• A Basic GET Request
• Exploring an HTTP Response Containing JSON Data
• Including Parameters in a GET Request
• A Basic POST Request
• A POST Request with Multiple Parameters
• The HEAD Request
• The PUT, OPTIONS, and DELETE Requests
• Working with Request and Response Headers
• Content Encoding and Binary Response Data
• Handling Responses in Different Formats
• HTTP Status Codes
• Redirects and Timeouts
• Exceptions
• Exercise: HTTP Requests with Python

Flask in Python: An Introduction to Web Frameworks & Flask

Course: 50 Minutes

• Course Overview
• The Fundamentals of Web Requests
• Web Frameworks
• The Flask Framework
• Flask Routes
• Flask Templates
• Flask Extensions
• Course Summary

Flask in Python: Building a Simple Web Site Using Flask

Course: 1 Hour, 30 Minutes

• Course Overview
• Installing Flask
• Creating a Basic Application on Flask
• Exploring Route Definitions
• Rendering HTML from Flask
• Using Boilerplate HTML for a Web Site
• Customizing the Appearance of a Web Site
• The url_for Function
• Defining a Base Jinja Template
• Inheriting the Base Jinja Template
• Pointing Multiple URLs to the Same Endpoint
• Course Summary

Flask in Python: User Interactions in Flask Applications

Course: 1 Hour, 21 Minutes

• Course Overview
• Handling HTML Errors in Flask
• Enabling POST Requests on a Route
• Logging Information for a Flask Application
• Message Flashing in a Flask Application
• Styling Flashed Messages
• Creating a Registration Page Using WTForms
• Creating a Login Page Using WTForms
• Incorporating the Registration and Login Pages
• Validating Form Data on Submission
• Testing Each of the Form Validators
• Course Summar

Flask in Python: User Authentication in a Flask Application

Course: 1 Hour, 34 Minutes

• Course Overview
• Introducing SQL Alchemy
• Creating Tables from Model Definitions
• Executing Queries Using SQLAlchemy Models
• Structuring a Flask Application for Maintenance
• Restructuring a Flask Application
• Creating Password Hashes Using Bcrypt
• Defining Custom Validators for Form Fields
• Enabling Users to Login to a Flask Application
• Allowing Users to Log Out of a Flask Application
• Displaying the Latest Reviews Submitted
• Testing Feedback Display Functionality
• Using Images in your Flask Web Site
• Course Summary

Python Concurrent Programming: Introduction to Concurrent Programming

Course: 1 Hour, 30 Minutes

• Course Overview
• Working with Multiple Tasks
• An Introduction to Multithreading
• Applications of Multithreading
• Multiprocessing
• Concurrent Programming
• Challenges with Concurrency
• Synchronization Using Locks
• Synchronization Using Semaphores
• Synchronization Using Events and Conditions
• Deadlocks
• Data Structures for Concurrent Tasks
• Thread and Process Pool
• Exercise: Introduction to Concurrent Programming

Python Concurrent Programming: Multithreading in Python

Course: 1 Hour, 42 Minutes

• Course Overview
• Creating a Thread
• Naming and Joining Threads
• Deriving the Thread Class
• Running Threads Concurrently
• Race Conditions
• Thread Synchronization with Locks
• Simulating a Deadlock
• Avoiding a Deadlock
• Semaphores - Part 1
• Semaphores - Part 2
• The Event Object
• The Condition Object
• Exercise: Multithreading in Python

Python Concurrent Programming: Multiprocessing in Python

Course: 1 Hour, 17 Minutes

• Course Overview
• An Introduction to Python Queues
• Multithreading with Python Queues
• Creating Processes
• Comparing Multiprocessing and Multithreading
• Multiprocessing Using Shared Memory
• Multiprocessing Using the Manager Class
• Synchronizing Concurrent Processes with Locks
• Inter-process Communication in Python
• Exercise: Multiprocessing in Python

Python Concurrent Programming: Asynchronous Executions in Python

Course: 1 Hour, 2 Minutes

• Course Overview
• Process Pools in Python - Part 1
• Process Pools in Python - Part 2
• Introducing the concurrent.futures module
• Threads vs. Processes for Network-bound Tasks
• Threads vs. Processes for CPU-bound Tasks
• Introducing the asyncio Module
• Concurrent Execution Using the asyncio Module
• Exercise: Asynchronous Executions in Python

Online Mentor

You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment

Estimated duration: 90 minutes

Practice Labs: Python Journeyman (estimated duration: 8 hours)

Perform journeyman level Python development tasks such as testing with pytest, making HTTP requests, serving HTTP requests with a Flask endpoint, and rendering a jinja template. Then, test your skills by answering assessment questions after using multithreading and multiprocessing with Python, processing data in a queue and creating and executing a coroutine with Asyncio.

Track 4: Pythonista

In this track of the Pythonista journey, the focus is unit testing, developing, and debugging using the PyCharm IDE, wrangling Excel data, network programing, and hashing and encryption algorithms.

Content:
E-learning courses

Introduction to Using PyCharm IDE

Course: 1 Hour, 17 Minutes

• Course Overview
• Installation of PyCharm
• Syntax Highlighting in PyCharm
• The Auto-complete Feature
• The Refactor Feature
• Debugging: Introducing Breakpoints
• Debugging: Stepping Into and Stepping Over
• Debugging: Conditional Breakpoints
• Debugging: Resume
• Python Package Installation from PyCharm
• Course Summary

Excel with Python: Working with Excel Spreadsheets from Python

Course: 1 Hour, 17 Minutes

• Course Overview
• Manipulating data with Microsoft Excel
• Manipulating Excel spreadsheets using openpyxl
• Accessing Data in openpyxl
• Manipulating Rows and Columns
• Writing to Files
• Manipulating Data in Files
• Freezing Rows and Columns
• Filtering Data
• Sorting Data
• Resizing Rows and Columns
• Merging Rows and Columns
• Course Summary

Excel with Python: Performing Advanced Operations

Course: 1 Hour, 30 Minutes

• Course Overview
• Working with Fonts and Styles
• Working with Borders and Colors
• Applying Number Formats
• Applying Conditional Formatting
• Using Advanced Conditional Formatting
• Working with Images
• Working with Formulae
• More Operations Using Formulae
• Using Absolute and Relative Cell References
• Programmatically Constructing Absolute References
• Using VLOOKUP
• Working with Named Ranges
• Working with Pivot Tables
• Using Pandas for Pivoting
• Leveraging Multi-level Indexing in Pandas5 MinutesCompletedActions
• Course Summary

Excel with Python: Constructing Data Visualizations

Course: 1 Hour, 1 Minute

• Course Overview
• Plotting Data with Line Charts
• Programmatically Constructing Line Charts
• Customizing Chart Appearance
• Customizing Chart Axes
• Using Line Styles
• Plotting Data with Bar Charts
• Programmatically Constructing Bar Charts
• Plotting Financial Data
• Plotting Bubble Charts
• Course Summary

Socket Programming in Python: Introduction

Course: 1 Hour, 3 Minutes

• Course Overview
• Introducing the socket Module
• Using Sockets in Client and Server Applications
• Using socket Objects in a with Block
• Setting Timeouts for Python Sockets
• Transferring Python Objects Over Sockets - Part 1
• Transferring Python Objects Over Sockets - Part 2
• Transferring Python Objects Over Sockets - Part 3
• Course Summary

Socket Programming in Python: Advanced Topics

Course: 1 Hour, 21 Minutes

• Course Overview
• Sending Large Text Files Using Sockets
• Receiving Large Text Files Using Sockets
• Transferring Image Files with Sockets
• Using Sockets to Build a Chat Application - Part 1
• Using Sockets to Build a Chat Application - Part 2
• Sockets in Blocking Mode
• Sockets in Non-Blocking Mode
• Using Python to Subscribe to RSS Feeds
• UDP Sockets in Python
• Course Summary

Python Design Patterns: Principles of Good Design

Course: 1 Hour, 35 Minutes

• Course Overview
• Design Patterns and Principles of Good Design
• The SOLID Principles of Good Design - I
• The SOLID Principles of Good Design - II
• Other Principles of Good Design
• The Principle of Single Responsibility
• Implementing the Principle of Single Responsibility
• The Open/Closed Principle
• Liskov's Substitution Principle
• The Interface Segregation Principle
• The Dependency Inversion Principle
• Types of Design Patterns
• Creational, Structural, and Design Patterns
• Course Summary

Python Design Patterns: Working with Creational Design Patterns

Course: 1 Hour, 50 Minutes

• Course Overview
• The Singleton Pattern
• Implementing the Singleton Pattern
• Pythonic Implementation of the Singleton Pattern
• The Global Object Pattern
• The Factory and Abstract Factory Patterns
• Implementing a Simple Factory Method
• Refactoring Code to Improve Design
• Applying the Factory Pattern to the Serializer
• Implementing the Abstract Factory Pattern
• The Builder Pattern
• Implementing the Builder Pattern
• The Object Pool Pattern
• Implementing the Object Pool Pattern
• Setting Up the Object Pool as a Singleton
• Course Summary

Python Design Patterns: Working with Structural Design Patterns

Course: 1 Hour, 27 Minutes

• Course Overview
• The Adapter Pattern
• The Adapter Pattern for Legacy Components
• Implementing the Adapter Pattern
• The Decorator Pattern
• Add Responsibilities Without the Decorator Pattern
• Implementing the Decorator Pattern
• The Façade Pattern
• Implementing the Façade Pattern
• The Proxy Pattern
• Implementing the Proxy Pattern
• The Flyweight Pattern
• Implementing the Flyweight Pattern
• Course Summary

Python Design Patterns: Working with Behavioral Design Patterns

Course: 1 Hour, 27 Minutes

• Course Overview
• The Strategy Pattern
• Implementing the Strategy Pattern
• The Chain of Responsibility Pattern
• Implementing the Chain of Responsibility Pattern
• The Observer Pattern
• Simple Implementation of the Observer Pattern
• Complex Implementation of the Observer Pattern
• The Command Pattern
• Implementing the Command Pattern
• The Iterator Pattern
• Implementing the Iterator Pattern
• Course Summary

Online Mentor

You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment

Estimated duration: 90 minutes

Practice Labs: Pythonista (estimated duration: 8 hours)

Perform development tasks expected of Pythonistas such as debugging with PyCharm, working with spreadsheet data and creating charts, and writing applications that can communicate using TPC sockets. Then, test your skills by answering assessment questions after working with Singleton, Observer and Factory design patterns and implementing iterators using special methods.