Section outline

• Select section Day 1: Python Object-Oriented Programming & Memory Management (7 Hours)

  Day 1: Python Object-Oriented Programming & Memory Management (7 Hours)

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  • Session 1: Python Object-Oriented Design Style (1.5 Hours)

    • Classes & Objects – Understanding object attributes & instance methods
    • Using dir() to explore object attributes
    • Method Resolution Order (MRO) & Inheritance
    • Hands-on Lab: Developing a class hierarchy & applying MRO

  • Session 2: Decorators (1.5 Hours)

    • Understanding @classmethod, @staticmethod, and @property
    • Creating custom decorators for enhanced functionality
    • Hands-on Lab: Practical case studies using decorators

  • Session 3: Iterators and Generators (1.5 Hours)

    • Understanding iterators (iter(), next()) & their use cases
    • Recursive file access with generators & memory-efficient processing
    • Hands-on Lab: Case studies on efficient data processing with generators

  • Session 4: Python Memory Layout & Internals (1.5 Hours)

    • Overview of Python memory management & internal structures
    • Exploring CPython source code layout
    • Mapping from Python parser to objects
    • Hands-on Lab: Inspecting Python’s memory allocation & debugging performance
• Select section Day 2: Advanced Data Structures, API Integration & Regex (7 Hours)

  Day 2: Advanced Data Structures, API Integration & Regex (7 Hours)

  • Session 1: Python Multidimensional Structures & Ordered Dictionaries (1.5 Hours)

    • Working with lists, tuples & dictionaries in multidimensional structures
    • Using OrderedDict for structured data handling
    • Hands-on Lab: Organizing complex datasets using Python dictionaries

  • Session 2: Requests & JSON Modules (1.5 Hours)

    • Introduction to the requests module for web integration
    • Downloading & processing URLs
    • Converting Python objects to JSON & vice versa
    • Hands-on Lab: Retrieving & parsing API data using JSON

  • Session 3: Python Arrays & NumPy Basics (1.5 Hours)

    • Introduction to NumPy & its role in scientific computing
    • Array indexing, slicing, and data types
    • Basic NumPy operations: Mathematical & statistical functions
    • Hands-on Lab: Performing data analysis with NumPy arrays

  • Session 4: Python Regular Expressions (Regex) (1.5 Hours)

    • Basics of Regular Expressions (BRE) & Extended Regex (ERE)
    • Searching, substituting, and validating text input
    • Hands-on Lab: Implementing regex for pattern matching & data validation