Data structures are fundamental building blocks in computer science, providing efficient ways to store, organize, and access data. Here are some key points to remember:
Fundamental Data Structures
- Arrays: Ordered collections of elements with fixed size.
- Advantages: Efficient access to WhatsApp Number elements using indices.
- Disadvantages: Fixed size, inefficient insertions and deletions.
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Linked Lists
- : Collections of elements connected through pointers.
- Advantages: Dynamic size, efficient insertions and deletions at the beginning or end.
- Disadvantages: Slower random access compared to arrays.
- Stacks: Last-In-First-Out (LIFO) data structure.
- Advantages: Simple implementation, efficient push and pop operations.
- Disadvantages: Limited access to elements.
- Queues: First-In-First-Out (FIFO) data structure.
- Advantages: Efficient enqueue and dequeue operations.
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Disadvantages
- Limited access to elements.
- Trees: Hierarchical data structures.
- Binary Trees: Each node has at most two children.
- Binary Search Trees: A binary tree where left child nodes are less than the parent, and right child nodes are greater.
- Heaps: A complete binary tree that satisfies the heap property (e.g., min-heap or max-heap).
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Graphs
- : Collections of nodes (vertices) connected by edges.
- Directed Graphs: Edges have a Phone Number Library direction.
- Undirected Graphs: Edges do not have a direction.
Algorithms and Operations
- Searching: Finding a specific element in a data structure.
- Linear Search: Iterates through the elements sequentially.
- Binary Search: Efficient for sorted arrays.
- Sorting: Arranging elements in a specific order.
- Bubble Sort: Compares adjacent elements and swaps them if necessary.
- Insertion Sort: Inserts elements into their correct positions in a sorted subarray.
- Selection Sort: Finds the minimum element and swaps it with the first element.
- Merge Sort: Divides the array into halves, sorts them recursively, and merges the sorted halves.
- Quick Sort: Picks a pivot element and partitions the array around it.
- Traversals: Visiting all elements in a data structure.
- Tree Traversals: Pre-order, in-order, post-order.
- Graph Traversals: Depth-first search (DFS), breadth-first search (BFS).
Key Concepts
- Time Complexity: Measures how the running time KYB Directory of an algorithm grows with the input size.
- Space Complexity: Measures the amount of memory an algorithm uses.
- Data Structures and Algorithms: Understanding the relationship between data structures and algorithms is crucial for efficient problem-solving.
- Problem-Solving Techniques: Develop problem-solving skills to identify the appropriate data structures and algorithms for given tasks.
