1. Arrays and Strings
Importance: Arrays and strings are fundamental data structures, forming the basis for many problems.
Common Problems:
Two Sum
Rotate Array
Longest Substring Without Repeating Characters
Palindrome Substring
Tips: Focus on understanding array manipulations, sliding window techniques, and common string operations.
2. Linked Lists
Importance: Linked lists are used in various scenarios, particularly when dealing with dynamic data structures.
Common Problems:
Reverse a Linked List
Merge Two Sorted Lists
Detect a Cycle in a Linked List
Find the Middle of a Linked List
Tips: Practice pointer manipulations, especially handling edge cases like null pointers and list boundaries.
3. Stacks and Queues
Importance: These data structures are essential for implementing various algorithms, particularly for parsing and tree traversals.
Common Problems:
Valid Parentheses
Implement Queue using Stacks
Next Greater Element
Sliding Window Maximum
Tips: Understand the LIFO (Last In First Out) and FIFO (First In First Out) principles and their applications.
4. Trees and Graphs
Importance: Trees and graphs are critical for solving hierarchical and network-related problems.
Common Problems:
Binary Tree Inorder Traversal
Lowest Common Ancestor of a Binary Tree
Number of Islands
Course Schedule (Topological Sort)
Tips: Focus on traversal techniques (BFS, DFS), and be comfortable with recursive and iterative solutions.
5. Hash Tables
Importance: Hash tables provide efficient key-value pair storage, crucial for problems requiring fast lookups and inserts.
Common Problems:
Two Sum
Group Anagrams
Subarray Sum Equals K
Copy List with Random Pointer
Tips: Practice collision handling methods and understand the trade-offs of different hashing techniques.
6. Heaps
Importance: Heaps are used for implementing priority queues and are essential for various greedy algorithms and graph problems.
Common Problems:
Kth Largest Element in an Array
Merge k Sorted Lists
Top K Frequent Elements
Median Finder
Tips: Understand heap operations (insert, delete, heapify) and practice implementing both min-heaps and max-heaps.
7. Sorting and Searching Algorithms
Importance: Efficient sorting and searching are foundational for optimizing other algorithms.
Common Problems:
Binary Search
Merge Sort
Quick Sort
Search in Rotated Sorted Array
Tips: Master binary search variations and understand the time complexity and stability of different sorting algorithms.
8. Dynamic Programming
Importance: Dynamic programming (DP) is crucial for solving optimization problems by breaking them down into simpler subproblems.
Common Problems:
Climbing Stairs
Longest Increasing Subsequence
0/1 Knapsack Problem
Edit Distance
Tips: Focus on identifying overlapping subproblems and optimal substructure properties, and practice memoization and tabulation techniques.
9. Backtracking
Importance: Backtracking is essential for solving combinatorial problems by exploring all possible solutions.
Common Problems:
Subsets
Permutations
Combination Sum
N-Queens Problem
Tips: Understand the concept of backtracking with pruning to avoid unnecessary computations and ensure efficiency.
10. Bit Manipulation
Importance: Bit manipulation is used for optimizing space and time complexity in various algorithms.
Common Problems:
Single Number
Reverse Bits
Counting Bits
Missing Number
Tips: Get comfortable with bitwise operations (AND, OR, XOR, NOT) and practice problems that require bit-level manipulation.