Fundamentals of Data Structures & Algorithms (DSA) — Beginner Friendly Guide
DSA Fundamentals
This post covers the basic concepts of Data Structures and Algorithms (DSA) in simple language with clear examples.
What is DSA?
Data Structures are ways of organizing data, and Algorithms are methods to operate on that data efficiently. Together they form the backbone of problem-solving in programming.
Big-O Complexity (Performance)
- O(1): Constant time (e.g., accessing array element)
- O(log n): Logarithmic time (e.g., binary search)
- O(n): Linear time (e.g., traversing an array)
- O(n log n): Efficient sorting (e.g., merge sort)
- O(n²): Nested loops (e.g., bubble sort)
Common Data Structures
- Array: Fast random access, fixed size
- Linked List: Flexible size, slower access
- Stack: Last In First Out (Undo operations)
- Queue: First In First Out (Task scheduling)
- Hash Table: Fast lookups using keys
- Tree: Hierarchical structure, good for searching
- Graph: Represents connections between entities
Algorithm Examples
- Binary Search: Quickly finds an element in sorted data
- Sorting: Quick Sort, Merge Sort, Bubble Sort
- DFS & BFS: Explore graphs
- Dynamic Programming: Optimize overlapping problems (e.g., Fibonacci)
Tips for Problem Solving
- Restate the problem in your own words
- Start with brute force, then optimize
- Think about edge cases (empty input, duplicates)
- Always analyze time and space complexity
More Learning & Java Resources
We will continue this as a series covering each of these topics in depth, and we will also do implementations of each Data Structure step by step. Follow us for more upcoming posts. Meanwhile, check out related articles:
This post is designed as a simple reference for quick DSA revision.

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