Data Structures and Algorithms Roadmap

A solid foundation for problem-solving and writing efficient code for every programmer.

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Phase Main Topic Content & Learning Activities Objectives & Deliverables
1. Fundamentals Programming & Math
  • Master a programming language (C++, Java, or Python).
  • Understand pointers and memory management (especially with C++).
  • Master the concept of recursion.
  • Learn about Time & Space Complexity (Big O Notation).
  • Analyze the complexity of a simple algorithm.
  • Solve basic recursive problems.
2. Linear DS Sequential Storage
  • Arrays and Strings.
  • Linked Lists: singly, doubly, circular.
  • Stacks.
  • Queues & Priority Queues.
  • Implement these data structures from scratch.
  • Solve application problems (e.g., validate parentheses using a Stack).
3. Basic Algorithms Searching & Sorting
  • Searching Algorithms: Linear, Binary.
  • Basic Sorting Algorithms: Bubble Sort, Selection Sort, Insertion Sort.
  • Efficient Sorting Algorithms: Merge Sort, Quick Sort, Heap Sort.
  • Understand the pros and cons of each algorithm.
  • Implement sorting algorithms and compare their performance.
4. Non-Linear DS Hierarchical & Network Storage
  • Hash Tables: collision handling, hash functions.
  • Trees: Binary Trees, Binary Search Trees (BST).
  • Balanced Trees: AVL Trees, Red-Black Trees.
  • Graphs: Representation (adjacency matrix, adjacency list), types.
  • Implement and operate on binary search trees.
  • Model real-world problems using graphs.
5. Advanced Algorithms Problem-Solving Techniques
  • Graph Traversal: Breadth-First Search (BFS), Depth-First Search (DFS).
  • Greedy Algorithms.
  • Dynamic Programming.
  • Shortest Path: Dijkstra, Bellman-Ford.
  • Minimum Spanning Tree: Prim, Kruskal.
  • Identify problem types and apply appropriate techniques.
  • Solve classic dynamic programming problems.
6. Practice & Application Hands-on Practice
  • Practice on platforms like LeetCode, HackerRank, Codeforces.
  • Focus on common interview question patterns.
  • Participate in programming contests (if possible).
  • Build a small project applying learned DS & Algos.
  • Increase speed and confidence in problem-solving.
  • Build a portfolio with solved problems.
  • Be ready for technical interviews.

Core Mindsets

1. Abstract Thinking

View problems at a high level, identify patterns, and choose the right data structure to model the data effectively.

2. Problem Decomposition

Break a large, complex problem into smaller, more manageable sub-problems. This is the foundation of recursion and dynamic programming.

3. Consider Trade-offs

No solution is perfect. Always consider the trade-offs between speed (time) and resources (memory) to choose the optimal solution for each context.

4. Pattern Recognition

Through practice, learn to recognize familiar problem patterns to quickly apply the correct known data structures and algorithms.