Algorithms are stepbystep procedures or methods used to solve computational problems. They describe how to perform specific tasks or operations. Data structures, on the other hand, are containers used to store and organize data effectively. While algorithms focus on the logic and steps to solve a problem, data structures focus on how the data is stored and accessed efficiently.

Algorithms are pervasive in modern technology. Some common examples include:

• Sorting algorithms: Like quicksort or mergesort used in databases and file systems.
• Search algorithms: Such as binary search used in data retrieval.
• Pathfinding algorithms: Like Dijkstra’s algorithm used in navigation systems.
• Compression algorithms: Such as Huffman coding used in data compression.

Efficiency of an algorithm is typically analyzed in terms of time complexity (how long it takes to run) and space complexity (how much memory it uses). This analysis involves evaluating how the algorithm’s performance scales with input size using techniques like Big O notation. An efficient algorithm minimizes time and space complexity to ensure optimal performance, especially for large datasets.

There are several algorithm design techniques including:

• Divide and Conquer: Breaks down a problem into smaller, more manageable subproblems (e.g., merge sort).
• Greedy Algorithms: Makes locally optimal choices at each step to achieve a global optimum (e.g., Dijkstra’s algorithm for shortest path).
• Dynamic Programming: Solves problems by breaking them down into overlapping subproblems (e.g., Fibonacci sequence calculation).