Algorithms & Data Structures

This topic covers the mathematical analysis of computer algorithms, focusing on time and space complexity. Learners will understand how to use Big O notation to evaluate algorithm efficiency, compare performance, and optimize code for large-scale data.

This topic covers systematic methods for solving computational problems, such as recursion, backtracking, and heuristics. Learners will understand how to analyze problem requirements and select or design efficient algorithmic strategies to solve them.

Algorithms are step-by-step procedures used to solve computational problems and process data. Learners will understand sorting, searching, graph traversal, and how to analyze time and space complexity using Big O notation to write efficient code.

Data structures are specialized formats for organizing, processing, and storing data in a computer. Learners will understand arrays, linked lists, stacks, queues, trees, and graphs, and how to select the optimal structure for efficient data retrieval.

This algorithmic paradigm involves breaking a complex problem into smaller subproblems, solving them recursively, and combining their results. Learners will understand how to apply this technique to optimize sorting, searching, and mathematical computations.

This algorithmic technique solves complex problems by breaking them down into overlapping subproblems and storing intermediate results to avoid redundant calculations. Learners will understand how to identify optimization problems and implement efficient, memoized solutions.

This topic focuses on methods for traversing, searching, and analyzing network structures represented as vertices and edges. Learners will understand how to solve practical problems like finding the shortest path, detecting cycles, and modeling connectivity.

This algorithmic approach builds up a solution piece by piece, always choosing the next option that offers the most immediate benefit. Learners will understand when this heuristic yields optimal solutions and how to apply it to scheduling and optimization problems.

This topic covers techniques for retrieving specific information stored within various data structures, ranging from simple linear searches to advanced tree and graph traversals. Learners will understand how to implement and compare the efficiency of different search methodologies.

This topic examines the methods used to arrange elements in a specific order, such as numerical or alphabetical. Learners will understand the mechanics, trade-offs, and efficiency profiles of algorithms like quicksort, mergesort, and heapsort.