Python Mastery: Devops, Automation, And Real-World Use Cases

From Coding Fundamentals to Cloud Automation, GitHub Integration, CI/CD Automation with Jenkins and Game Development

What you’ll learn
Python coding practice
About Data Types in Python
Regular Expression (regex) in Python
How to identify and apply keywords in code
How to declare and assign values to variables.
The purpose and function of the return statement in functions.
Python Functions: Definition and Usage
Utilizing Modules in Function Design
Organizing Your Code Using Python Packages
The use of the sys module to access command line arguments
The different types of operators in Python, including arithmetic, comparison, logical, and assignment operators.
DevOps Use Cases with if..else..elif
For/While Loops in Python: Syntax and Usage
Real-time Use Case: Lists and Exception Handling in Python
Python Script for Handling Exceptions Using Try Statements
Integrating Python with GitHub Through the GitHub API
Cloning a GitHub Repository with Python
Performing operations on various AWS services like S3, EC2 using Boto3 Python Module
Launching an EC2 Instance using Boto3 Python Module
CI/CD Automation with Jenkins and Python
Integrate GitHub Webhooks With Jenkins
Develop Games in Python with PyCharm and ChatGPT

Requirements
Basic Computer Skills: Familiarity with using a computer and navigating the internet.
No Prior Programming Experience Required: This course is designed for beginners, so no previous programming knowledge is necessary.
Basic Knowledge of DevOps is required

Description
Python for DevOps means using Python to make DevOps tasks easier and more efficient. DevOps is all about automating tasks like building, testing, and deploying software quickly, as well as managing servers and infrastructure. Python is great for this because it’s simple to learn, and it has many tools and libraries that help automate these tasks. For example, you can use Python to automatically create servers, manage cloud services, and set up continuous integration and delivery (CI/CD) pipelines. Python helps DevOps teams work faster and more effectively by reducing manual work and making processes smoother.Course Outline:Section1: Introduction-> Introduction-> An overview of Python-> About Shell Scripting-> Python vs. Shell Scripting-> When to Use Python vs. Shell ScriptingSection2: How to Begin Practicing Python Coding-> Begin Python Coding Practice-> Visual Studio Code – Python Coding Practice-> PyCharm – IDEs-> Codespaces – Online Coding PlatformSection3: Python Data Types-> About Data Types in Python-> Lab – String Data Type-> Lab – Integer Data Type-> Lab – Float Data Type-> Lab – len(), Length of a string-> Lab – String upper(), lower()-> Lab – String replace()-> Lab – String split()-> Lab – Print specific object in split()-> About List in Python-> Lab – List Data Type-> Lab – Add and Modify in a List Data Type (Mutable)-> About Tuples in Python-> Lab – Tuples in Python-> About Sets in Python-> Lab – Sets in Python-> Dictionary in Python-> Lab – Dictionary in Python-> Use Cases in DevOps-> Boolean Data Types-> Lab – Boolean in Python-> Coding Exercise 1:Create a Python script to add two integers-> Coding Exercise 2:Create a python script to perform operation of floating-point-> Coding Exercise 3:Write a python script to determine the length of a string-> Coding Exercise 4:Create a python script to convert a string to uppercase and lowercase-> Coding Exercise 5:Create a Simple python Script to replace a substring within a string-> Coding Exercise 6:Create a Simple python script to Split the text of a string-> Coding Exercise 7:Create a python script to define a Variable and assign it a list of five integersSection4: Regular Expression (regex) in Python-> Overview of Regular Expressions in Python-> Lab – Using re. match() to Match Patterns at the Start of a String-> Lab – Using re. search() to Find Matches Anywhere in a String-> Lab – Using re. findall() to Search for All Matches in a String-> Regex Use Cases from a DevOps Perspective-> Coding Exercise 8:Regular Expression in python-> Section5: Mastering Keywords in Python-> Overview of Keywords in Python-> Common Python keywords-> Mastering Control Flow Keywords – if, else, for, and break-> Lab: Mastering Control Flow Keywords – continue, def, return, class, import etc.Section6: Working with Variables in Python-> Overview of Variables with Example-> Lab: Working with Float Variables in Python-> Lab: Defining Lists as Variables in Python-> Lab: Working with Dictionary Variables in Python-> Python Variables: Local vs Global Scope-> Lab: Working with Local Variables in Python-> Lab: Working with Global Variables in PythonSection7: Return Statement in Python-> Return Statement: An Overview with Syntax-> Lab: Creating Functions That Return Values-> Lab: Functions That Return Multiple Values-> Lab: Function for Identifying Even and Odd ValuesSection8: Python Functions: Definition and Usage-> Introduction to Functions in Python-> Advantages of functions in Python-> Lab: Functions with Parameters-> Lab: Functions with Return Value-> Lab: Designing Functions for Basic Arithmetic Operations-> Comparing Scripts: Using Functions vs. Not Using FunctionsSection9: Utilizing Modules in Function Design-> Introduction to Python Modules-> An Overview of Built-in Modules-> An Overview of User-defined Modules-> Lab: Essential Built-in Modules in Python-> Lab: OS and Math Modules-> Lab: Building Your Own ModulesSection 10: Python Packages: Organizing Your Code-> Introduction to Python Packages-> Key Concepts of Packages-> Advantages of Using Packages-> Lab: Creating Package Structures and Modules-> Importing Modules for Easier Access using __init__. py-> Creating a Main Python File to Utilize Your Package-> Importing Functions from a PackageSection 11: Command Line Arguments in Python-> Command Line Arguments with Practical Examples-> Lab: Script to Add Two Numbers (No Command Line Arguments)-> Lab: Working with sys. argv for Command Line Arguments-> Lab: Passing Multiple Arguments to Python Scripts-> Lab: Pass Arguments to Add Two Numbers-> Lab: Conditional Arithmetic via Script Arguments-> Lab: Conditional Arithmetic Using Script ArgumentsSection 12: Operators in Python: Concepts and Examples-> The Basics of Arithmetic Operators in Python-> Lab: Exploring Comparison (Relational) Operators-> Lab: Comparison Operators (=, >, >=, ==)-> Logical Operators: and, or, not-> Lab: Using ‘and’ , ‘or’ for Logical Operations-> Lab: Using ‘not’ for Logical Operations-> Assignment Operators in Python-> Lab: Understanding Different Assignment Operators-> Membership Operators: ‘in’ and ‘not in’-> Lab: Using ‘not in’ Membership Operators-> Operators in DevOps: Practical Use Cases-> Use Cases for Operators in the DevOps WorkflowSection 13: Conditional Statements in Python-> Understanding ‘if’ statement in Python-> Understanding ‘else’..’elif’ statement in Python-> Lab: Implementing if..else Statements-> Lab: DevOps Use Cases with if..else..elifSection 14: Understanding Loops in Python-> For Loops in Python: Syntax and Usage-> While Loops in Python: Syntax and Usage-> Lab: Implementing For Loops-> Lab: Printing Ranges and Strings with For Loops-> Lab: Implementing Infinite While Loops-> Lab: Exploring Break Statements in PythonSection 15: Real-time Use Case: Lists and Exception Handling in Python-> Introduction to the Real-Time Project-> Lab: User Input for List Creation-> Lab: Understanding split function text.split()-> Lab: Identify modules and their functions-> Lab: Utilize a for loop to list files-> Exception Handling with Try Statement-> Lab: Python Script for Handling Exceptions Using Try Statements-> Lab: Handling Error – FileNotFoundError-> Lab: Handling Known Error – PermissionErrorSection 16: Integrating Python with GitHub-> Integrating Python with GitHub Through the GitHub API-> Lab: Install PyGithub and Generate a GitHub Access Token-> Lab: Retrieve User Login and Public Repos with Python-> Lab: Retrieve GitHub Account Repository List-> Lab: Create a New Repository with PythonSection 17: Cloning a GitHub Repository with Python-> Clone a Repository Using the Subprocess Module-> Lab: Clone a Repository with the Subprocess Module-> Lab: Using GitPython Library-> Lab: Handle Git Errors with Exception HandlingSection 18: Boto3 Python Module-> Introduction to the Boto3 Python Module-> Lab: Install Boto3 and Create an AWS User Account-> Lab: Configure GitHub Access from Codespaces via AWS CLI-> Lab: List All Buckets with Boto3-1-> Lab: List All Buckets with Boto3-2-> Lab: List All Buckets with Boto3-3-> Lab: Upload a File to a Bucket Using Boto3-> Lab: Download a File to a Bucket Using Boto3Section 19: Launching an EC2 Instance using Boto3 Python Module-> Project Overview-> Setting Up a User Account in AWS with IAM-> Set Up AWS CLI in Codespaces-> Begin Python Script: Import Boto3-> Include EC2 Attributes in Python Script-> Add Tag Specifications in Python Script-> Debug the Python Script Before Execution-> Access the EC2 Instance Launched via Python Script-> Update the Python Script to Add 20GB EBS Volume-> Run Python Script to Confirm EBS Volume-> Update Python Script to Include UserData-> Update Python Script to Include Apache Package-> Run the Revised Python Script and Validate-> Access the Apache ServerSection 20: CI/CD Automation with Jenkins and Python-> Project Overview-> Set Up a GitHub Repository for Your Project-> Write the Source Code in Python and Push to GitHub Repository-> Provision a Jenkins Server Instance in AWS-> Connect to the Jenkins Server and Install Java-> Install the Jenkins Package on the Server-> Set Up Jenkins Configuration-> Install Necessary Plugins on the Jenkins Server-> Add GitHub Credentials to the Jenkins Server-> First Stage of the Pipeline: Checkout the Project-> Build the Job-> Add a Stage for Installing Python Dependencies-> Add a Stage to Execute the Python Script-> Setting Up a User Account in AWS with IAM-> Create Access Keys for Jenkins Credentials-> Set Up Access Keys in Jenkins Pipeline-> Export AWS Credentials in Jenkins Pipeline-> Build the Job and Verify EC2 Instance Creation in AWS-> Access the Web Server Using URLSection 21: Integrate GitHub Webhooks With Jenkins-> Setup GitHub Webhooks in Jenkins-> GitHub hook trigger for GITScm polling-> Add Jenkins Webhook to GitHub Repository-> Test the Webhook by Editing the Python ScriptSection 22: Develop Games in Python with PyCharm and ChatGPT

Overview
Section 1: Introduction

Lecture 1 Introduction

Lecture 2 An overview of Python

Lecture 3 About Shell Scripting

Lecture 4 Python vs. Shell Scripting

Lecture 5 When to Use Python vs. Shell Scripting

Section 2: How to Begin Practicing Python Coding

Lecture 6 Begin Python Coding Practice

Lecture 7 Visual Studio Code – Python Coding Practice

Lecture 8 PyCharm – IDEs

Lecture 9 Codespaces – Online Coding Platform

Section 3: Python Data Types

Lecture 10 About Data Types in Python

Lecture 11 Lab – String Data Type

Lecture 12 Lab – Integer Data Type

Lecture 13 Lab – Float Data Type

Lecture 14 Lab – len(), Length of a string

Lecture 15 Lab – String upper(), lower()

Lecture 16 Lab – String replace()

Lecture 17 Lab – String split()

Lecture 18 Lab – Print specific object in split()

Lecture 19 About List in Python

Lecture 20 Lab – List Data Type

Lecture 21 Lab – Add and Modify in a List Data Type (Mutable)

Lecture 22 About Tuples in Python

Lecture 23 Lab – Tuples in Python

Lecture 24 About Sets in Python

Lecture 25 Lab – Sets in Python

Lecture 26 Dictionary in Python

Lecture 27 Lab – Dictionary in Python

Lecture 28 Use Cases in DevOps

Lecture 29 Boolean Data Types

Lecture 30 Lab – Boolean in Python

Section 4: Regular Expression (regex) in Python

Lecture 31 Overview of Regular Expressions in Python

Lecture 32 Lab – Using re.match() to Match Patterns at the Start of a String

Lecture 33 Lab – Using re.search() to Find Matches Anywhere in a String

Lecture 34 Lab – Using re.findall() to Search for All Matches in a String

Lecture 35 Regex Use Cases from a DevOps Perspective

Section 5: Mastering Keywords in Python

Lecture 36 Overview of Keywords in Python

Lecture 37 Common Python keywords

Lecture 38 Mastering Control Flow Keywords – if, else, for, and break

Lecture 39 Lab: Mastering Control Flow Keywords – continue, def, return, class, import etc.

Section 6: Working with Variables in Python

Lecture 40 Overview of Variables with Example

Lecture 41 Lab: Working with Float Variables in Python

Lecture 42 Lab: Defining Lists as Variables in Python

Lecture 43 Lab: Working with Dictionary Variables in Python

Lecture 44 Python Variables: Local vs Global Scope

Lecture 45 Lab: Working with Local Variables in Python

Lecture 46 Lab: Working with Global Variables in Python

Section 7: Return Statement in Python

Lecture 47 Return Statement: An Overview with Syntax

Lecture 48 Lab: Creating Functions That Return Values

Lecture 49 Lab: Functions That Return Multiple Values

Lecture 50 Lab: Function for Identifying Even and Odd Values

Section 8: Python Functions: Definition and Usage

Lecture 51 Introduction to Functions in Python

Lecture 52 Advantages of functions in Python

Lecture 53 Lab: Functions with Parameters

Lecture 54 Lab: Functions with Return Value

Lecture 55 Lab: Designing Functions for Basic Arithmetic Operations

Lecture 56 Comparing Scripts: Using Functions vs. Not Using Functions

Section 9: Utilizing Modules in Function Design

Lecture 57 Introduction to Python Modules

Lecture 58 An Overview of Built-in Modules

Lecture 59 An Overview of User-defined Modules

Lecture 60 Lab: Essential Built-in Modules in Python

Lecture 61 Lab: OS and Math Modules

Lecture 62 Lab: Building Your Own Modules

Section 10: Python Packages: Organizing Your Code

Lecture 63 Introduction to Python Packages

Lecture 64 Key Concepts of Packages

Lecture 65 Advantages of Using Packages

Lecture 66 Lab: Creating Package Structures and Modules

Lecture 67 Importing Modules for Easier Access using __init__.py

Lecture 68 Creating a Main Python File to Utilize Your Package

Lecture 69 Importing Functions from a Package

Section 11: Command Line Arguments in Python

Lecture 70 Command Line Arguments with Practical Examples

Lecture 71 Lab: Script to Add Two Numbers (No Command Line Arguments)

Lecture 72 Lab: Working with sys.argv for Command Line Arguments

Lecture 73 Lab: Passing Multiple Arguments to Python Scripts

Lecture 74 Lab: Pass Arguments to Add Two Numbers

Lecture 75 Lab: Conditional Arithmetic via Script Arguments

Lecture 76 Lab: Conditional Arithmetic Using Script Arguments

Section 12: Operators in Python: Concepts and Examples

Lecture 77 The Basics of Arithmetic Operators in Python

Lecture 78 Lab: Exploring Comparison (Relational) Operators

Lecture 79 Lab: Comparison Operators (=, >, >=, ==)

Lecture 80 Logical Operators: and, or, not

Lecture 81 Lab: Using ‘and’ , ‘or’ for Logical Operations

Lecture 82 Lab: Using ‘not’ for Logical Operations

Lecture 83 Assignment Operators in Python

Lecture 84 Lab: Understanding Different Assignment Operators

Lecture 85 Membership Operators: ‘in’ and ‘not in’

Lecture 86 Lab: Using ‘not in’ Membership Operators

Lecture 87 Operators in DevOps: Practical Use Cases

Lecture 88 Use Cases for Operators in the DevOps Workflow

Section 13: Conditional Statements in Python

Lecture 89 Understanding ‘if’ statement in Python

Lecture 90 Understanding ‘else’..’elif’ statement in Python

Lecture 91 Lab: Implementing if..else Statements

Lecture 92 Lab: DevOps Use Cases with if..else..elif

Section 14: Understanding Loops in Python

Lecture 93 For Loops in Python: Syntax and Usage

Lecture 94 While Loops in Python: Syntax and Usage

Lecture 95 Lab: Implementing For Loops

Lecture 96 Lab: Printing Ranges and Strings with For Loops

Lecture 97 Lab: Implementing Infinite While Loops

Lecture 98 Lab: Exploring Break Statements in Python

Section 15: Real-time Use Case: Lists and Exception Handling in Python

Lecture 99 Introduction to the Real-Time Project

Lecture 100 Lab: User Input for List Creation

Lecture 101 Lab: Understanding split function text.split()

Lecture 102 Lab: Identify modules and their functions

Lecture 103 Lab: Utilize a for loop to list files

Lecture 104 Exception Handling with Try Statement

Lecture 105 Lab: Python Script for Handling Exceptions Using Try Statements

Lecture 106 Lab: Handling Error – FileNotFoundError

Lecture 107 Lab: Handling Known Error – PermissionError

Section 16: Integrating Python with GitHub

Lecture 108 Integrating Python with GitHub Through the GitHub API

Lecture 109 Lab: Install PyGithub and Generate a GitHub Access Token

Lecture 110 Lab: Retrieve User Login and Public Repos with Python

Lecture 111 Lab: Retrieve GitHub Account Repository List

Lecture 112 Lab: Create a New Repository with Python

Section 17: Cloning a GitHub Repository with Python

Lecture 113 Clone a Repository Using the Subprocess Module

Lecture 114 Lab: Clone a Repository with the Subprocess Module

Lecture 115 Lab: Using GitPython Library

Lecture 116 Lab: Handle Git Errors with Exception Handling

Section 18: Boto3 Python Module

Lecture 117 Introduction to the Boto3 Python Module

Lecture 118 Lab: Install Boto3 and Create an AWS User Account

Lecture 119 Lab: Configure GitHub Access from Codespaces via AWS CLI

Lecture 120 Lab: List All Buckets with Boto3-1

Lecture 121 Lab: List All Buckets with Boto3-2

Lecture 122 Lab: List All Buckets with Boto3-3

Lecture 123 Lab: Upload a File to a Bucket Using Boto3

Lecture 124 Lab: Download a File to a Bucket Using Boto3

Section 19: Launch EC2 Instance with Boto3 Python Module

Lecture 125 Project Overview

Lecture 126 Setting Up a User Account in AWS with IAM

Lecture 127 Set Up AWS CLI in Codespaces

Lecture 128 Begin Python Script: Import Boto3

Lecture 129 Include EC2 Attributes in Python Script

Lecture 130 Add Tag Specifications in Python Script

Lecture 131 Debug the Python Script Before Execution

Lecture 132 Access the EC2 Instance Launched via Python Script

Lecture 133 Update the Python Script to Add 20GB EBS Volume

Lecture 134 Run Python Script to Confirm EBS Volume

Lecture 135 Update Python Script to Include UserData

Lecture 136 Update Python Script to Include Apache Package

Lecture 137 Run the Revised Python Script and Validate

Lecture 138 Access the Apache Server

Section 20: CI/CD Automation with Jenkins and Python

Lecture 139 Project Overview

Lecture 140 Set Up a GitHub Repository for Your Project

Lecture 141 Write the Source Code in Python and Push to GitHub Repository

Lecture 142 Provision a Jenkins Server Instance in AWS

Lecture 143 Connect to the Jenkins Server and Install Java

Lecture 144 Install the Jenkins Package on the Server

Lecture 145 Set Up Jenkins Configuration

Lecture 146 Install Necessary Plugins on the Jenkins Server

Lecture 147 Add GitHub Credentials to the Jenkins Server

Lecture 148 First Stage of the Pipeline: Checkout the Project

Lecture 149 Build the Job

Lecture 150 Add a Stage for Installing Python Dependencies

Lecture 151 Add a Stage to Execute the Python Script

Lecture 152 Setting Up a User Account in AWS with IAM

Lecture 153 Create Access Keys for Jenkins Credentials

Lecture 154 Set Up Access Keys in Jenkins Pipeline

Lecture 155 Export AWS Credentials in Jenkins Pipeline

Lecture 156 Build the Job and Verify EC2 Instance Creation in AWS

Lecture 157 Access the Web Server Using URL

Section 21: Integrate GitHub Webhooks with Jenkins

Lecture 158 Setup GitHub Webhooks in Jenkins

Lecture 159 GitHub hook trigger for GITScm polling

Lecture 160 Add Jenkins Webhook to GitHub Repository

Lecture 161 Test the Webhook by Editing the Python Script

Lecture 162 Last Lecture

Individuals with little or no prior programming experience who want to learn Python from scratch.,DevOps Professionals: Anyone interested in automation, cloud computing, and using Python for DevOps practices.,Students and Professionals: Anyone seeking to enhance their resume or career prospects by adding Python programming to their skill set.,Overall, this course is suitable for anyone eager to learn Python and apply it in real-world scenarios.

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