Welcome to the E-Course: python programming

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Skills you'll gain

Python Programming
Online Course
Professional Development
Best Practices
Coding Techniques
Career Advancement
Skill Enhancement

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python programming FREE

Unlock your potential with "The Python Professional: Tools, Techniques, and Best Practices" eCourse! This comprehensive program is designed for aspiring developers and seasoned professionals alike, covering essential Python tools, advanced techniques, and industry best practices. Gain hands-on experience through real-world projects, interactive quizzes, and expert insights. Whether you're looking to enhance your coding skills, improve your workflow, or stay updated with the latest Python trends, this course equips you with everything you need to excel in the tech industry. Enroll today and take your Python expertise to the next level!

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Introduction to Python for Professionals +
  • Introduction to Python for Professionals
  • Why Python?
  • Course Objectives
  • Course Structure
  • Getting Started with Python
  • Setting Up Your Development Environment
  • Writing Your First Python Program
  • Understanding Python Syntax
  • Conclusion
Advanced Data Structures and Algorithms +
  • Chapter 2: Advanced Data Structures and Algorithms
  • 2.1 Overview of Data Structures
  • 2.2 Graphs
  • 2.3 Trees
  • 2.4 Heaps
  • 2.5 Hash Tables
  • 2.6 Tries
  • 2.7 Conclusion
  • 2.2.1 Types of Graphs
  • 2.2.2 Graph Representation
  • 2.3.1 Types of Trees
  • 2.3.2 Tree Traversal Techniques
  • 2.4.1 Applications of Heaps
  • 2.5.1 Collision Resolution Techniques
  • 2.6.1 Structure of a Trie
Object-Oriented Programming in Python +
  • Object-Oriented Programming in Python
  • 1. Introduction to Object-Oriented Programming
  • 2. Classes and Objects
  • 3. Attributes and Methods
  • 4. Encapsulation
  • 5. Inheritance
  • 6. Polymorphism
  • 7. Conclusion
Functional Programming Paradigms +
  • Chapter 4: Functional Programming Paradigms
  • 4.1 Introduction to Functional Programming
  • 4.2 Core Concepts of Functional Programming
  • 4.3 Common Functional Programming Languages
  • 4.4 Advantages of Functional Programming
  • 4.5 Challenges in Functional Programming
  • 4.6 Practical Applications of Functional Programming
  • 4.7 Summary of Functional Programming Paradigms
  • 4.2.1 Pure Functions
  • 4.2.2 Immutability
  • 4.2.3 Function Composition
  • 4.2.4 Recursion
Error Handling and Debugging Techniques +
  • Error Handling and Debugging Techniques
  • Understanding Errors
  • Importance of Error Handling
  • Common Error Handling Techniques
  • Implementing Try-Catch Blocks
  • Debugging Techniques
  • Using Print Statements for Debugging
  • Interactive Debuggers
  • Best Practices for Error Handling
  • Conclusion
Working with Libraries and Frameworks +
  • Chapter 6: Working with Libraries and Frameworks
  • 1. Understanding Libraries and Frameworks
  • 2. Benefits of Using Libraries and Frameworks
  • 3. Choosing the Right Library or Framework
  • 4. Best Practices for Working with Libraries and Frameworks
  • 5. Example: Using a Library in Python
  • 6. Example: Using a Framework in PHP
  • 7. Conclusion
Testing and Test-Driven Development +
  • Chapter 7: Testing and Test-Driven Development
  • What is Testing?
  • Importance of Testing
  • What is Test-Driven Development (TDD)?
  • The TDD Cycle
  • Benefits of TDD
  • Writing Tests in TDD
  • Common Pitfalls in TDD
  • Conclusion
Data Manipulation and Analysis +
  • Chapter 8: Data Manipulation and Analysis
  • 8.1 Introduction to Data Manipulation
  • 8.2 Importance of Data Manipulation
  • 8.3 Data Manipulation Techniques
  • 8.4 Data Analysis Techniques
  • 8.5 Conclusion
  • 8.6 Further Reading and Resources
  • 8.3.1 Data Cleaning
  • 8.3.2 Data Transformation
  • 8.3.3 Data Filtering and Sorting
  • 8.4.1 Descriptive Statistics
  • 8.4.2 Inferential Statistics
  • 8.4.3 Data Visualization
Building RESTful APIs with Python +
  • Chapter 9: Building RESTful APIs with Python
  • Understanding REST Principles
  • Setting Up Your Environment
  • Installing Required Packages
  • Creating Your First API
  • Testing Your API
  • Best Practices for Building RESTful APIs
  • Securing Your API
  • Conclusion
  • Directory Structure
  • Writing the Application Code
  • Adding a Book
  • Getting a Book
  • Deleting a Book
Introduction to Machine Learning with Python +
  • Chapter 10: Introduction to Machine Learning with Python
  • What is Machine Learning?
  • Types of Machine Learning
  • Getting Started with Python for Machine Learning
  • Key Libraries for Machine Learning
  • Setting Up Your Environment
  • Basic Machine Learning Workflow
  • Example: Building a Simple Machine Learning Model
  • Conclusion
  • Further Reading and Resources
  • Step 1: Import Required Libraries
  • Step 2: Load the Dataset
  • Step 3: Preprocess the Data
  • Step 4: Split the Dataset
  • Step 5: Train the Model
  • Step 6: Make Predictions
  • Step 7: Evaluate the Model
Deployment and Continuous Integration +
  • Chapter 11: Deployment and Continuous Integration
  • Understanding Deployment
  • Continuous Integration (CI)
  • Setting Up a CI/CD Pipeline
  • Tools for Deployment and CI
  • Best Practices for Deployment and CI
  • Conclusion
Best Practices for Python Development +
  • Best Practices for Python Development
  • 1. Code Readability and Style
  • 2. Using Version Control
  • 3. Writing Tests
  • 4. Documentation
  • 5. Dependency Management
  • 6. Code Performance
  • 7. Security Practices
  • 8. Code Reviews
  • 9. Continuous Integration and Deployment (CI/CD)
  • 10. Conclusion

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