Algorithmic Complexity: Travel Time Analysis

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Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.

Covers algorithmic complexity and travel time analysis, focusing on measuring the time taken by algorithms and evaluating their performance.

Introduces functions in Python, covering predefined and user-defined functions, formal and effective parameters, and the importance of docstrings.

Explains the merge sort algorithm, its correctness, and time complexity compared to other sorting algorithms.

Covers the Euclidean algorithm for GCD calculation and algorithmic complexity analysis.

Explains the differences between mutable and immutable objects in Python and covers native container types.

Covers the basics and methods of string operations in Python, including slicing, indexing, and formatting.

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Covers the introduction to logarithms, algorithmic notations, and analysis of algorithm travel times.

Introduces selection and insertion sorting algorithms, explaining their correctness and time complexity.