The Complete Advanced Python Programming Course 2025

Implement a Python singly linked list by creating node objects, linking them with next pointers, and traversing the list to access data.

Learn search operations in a doubly linked list by traversing nodes, comparing data, and returning the matching node or false; the approach mirrors the singly linked list, with O(1) append.

Explore push and pop operations in a stack data structure, adding and removing elements from the top, understanding size changes, underflow errors, and the last-in, first-out principle.

Describe a queue as a linear structure that follows the first in, first out principle, with enqueue at the rear, dequeue at the front, and restricted front access.

Define a queue with an empty items list and a size counter, then enqueue by inserting at the front and dequeue by popping, demonstrating a 3-item run.

Learn how object oriented programming in Python organizes code into objects and classes, with attributes, methods, and concepts like inheritance, encapsulation, and polymorphism to create modular, reusable, and scalable solutions.

Explore how a subclass inherits from a superclass to enable code reuse. See single and multiple inheritance, the diamond problem, and how dog and cat illustrate overriding.

Explore how Python treats functions as first-class objects, including lambdas and callable objects, and contrast pure versus impure functions with attention to side effects.

Learn about lambda functions, anonymous inline functions in Python used for concise, on-the-fly operations. See examples of using lambda with two args, map, filter, and sorting by key.

Explore immutable data in Python, including tuples, strings, and frozen set; learn how their values cannot be modified, and safety helps when passing data to functions or using dictionary keys.

Identify and handle errors and exceptions in Python, distinguishing syntax errors from runtime errors, and use try-except blocks to catch division by zero and other issues.

Explore metaprogramming in Python by writing code that manipulates or generates other code at runtime, including decorators, class decorators, metaclasses, and dynamic code generation.

Explore switching metaclasses by implementing an income statement metaclass and a balance sheet metaclass, customizing attributes and methods to prefix domain terms and compute revenue, assets, and liabilities.

Explore concurrency in computer science, including parallelism, threads, and processes, and learn how models like cooperative and preemptive multitasking affect scalable software while addressing race conditions, deadlocks, and synchronization overhead.

Explore what a thread is in Python and how threads run concurrently, using the threading module to create, start, and join multiple threads that simulate work with delays.

Explore how a multi-threaded priority queue enables concurrent access by multiple threads, storing and retrieving elements by priority, with thread safety ensured through locks or atomic operations.