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Advanced Python Syllabus

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Softlogic Systems’ Advanced Python Course Syllabus is specifically designed for College Students, Freshers, and Job Seekers. Our Advanced Python syllabus covers advanced programming techniques, working with libraries, database integration, data analysis, web frameworks, and real-world project development. Our Advanced Python Course Content helps you learn Advanced Python step by Step with real-time projects and Interview Preparations.

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Syllabus for The Advanced Python Syllabus Course

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  • Advanced Data Types: Lists, Tuples, Sets, and Dictionaries
  • Comprehensions: List, Dictionary, and Set Comprehensions
  • Lambda Functions and Higher-Order Functions
  • Iterators and Generators
  • Decorators and Closures
  • Classes and Objects
  • Inheritance and Polymorphism
  • Encapsulation and Abstraction
  • Magic Methods and Operator Overloading
  • Abstract Classes and Interfaces

  • Advanced Exception Handling Techniques
  • Custom Exception Handling
  • File Handling and I/O Operations
  • Working with JSON and CSV files
  • Advanced Lists and Arrays
  • Stacks, Queues, Linked Lists
  • Trees, Graphs, and Hash Tables
  • Sorting and Searching Algorithms
  • Recursion and Dynamic Programming
  • NumPy for Numerical Computing
  • Pandas for Data Manipulation
  • Matplotlib and Seaborn for Data Visualization
  • SciPy for Scientific Computing
  • Introduction to Data Cleaning and Preprocessing
  • Introduction to Web Frameworks (Flask and Django)
  • Building REST APIs with Flask
  • Working with Databases (SQLAlchemy, SQLite)
  • Templating Engines (Jinja2)
  • User Authentication and Authorization
  • Introduction to Machine Learning Concepts
  • Supervised vs Unsupervised Learning
  • Linear Regression, Logistic Regression, and Decision Trees
  • Working with Scikit-learn
  • Model Evaluation and Cross-Validation
  • Advanced Pandas Techniques
  • Data Wrangling and Transformation
  • Visualizing Data with Matplotlib and Seaborn
  • Time Series Analysis
  • Introduction to Big Data and Hadoop
  • RESTful APIs and HTTP Requests
  • Working with APIs using Requests Library
  • Web Scraping with BeautifulSoup and Scrapy
  • Handling JSON and XML Data
  • Automating Tasks with Python
  • Web Scraping and Data Extraction
  • Writing Python Scripts for System Administration
  • Automating Web Testing with Selenium
  • Basics of Neural Networks and Deep Learning
  • Introduction to TensorFlow
  • Building and Training Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Real-world Python Application Development
  • Group Project: Web Application or Data Science Project
  • Code Optimization and Refactoring
  • Best Practices for Writing Clean and Maintainable Code

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