DSA Roadmap For Android Dev
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DSA Roadmap For Android Dev
Akshay Nandwana
October 24, 2024
5 min read
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6-Month DSA Roadmap for Android Developers

This roadmap will help you build a strong foundation in Data Structures and Algorithms (DSA) over six months, ensuring you are well-prepared for both Android development and technical interviews. Each week focuses on essential topics, with a mix of theory and practice. Let’s get started!

Month 1: Basics and Arrays

Week 1: Introduction and Basics

  • 📚 Importance of DSA: Why DSA matters for Android developers.

  • Time/Space Complexity: Understanding Big O, Theta, Omega.

  • 🔍 Linear and Binary Search: Basics of search algorithms.

Week 2: Arrays

  • 📋 Array basics: Introduction to arrays and their operations.

  • ⚙️ Operations: Insert, delete, and traverse.

  • 🗄️ 1D/2D arrays: Working with different array structures.

  • 📝 Practice: Solve beginner-level problems.

Week 3: Arrays (continued)

  • ↔️ Two-pointer technique: Learn this efficient technique for solving array problems.

  • 🔄 Sliding window: Explore this technique for subarray problems.

  • 📝 Practice: More hands-on problems.

Week 4: Strings

  • 🔤 String basics: Introduction to string operations.

  • 🔧 Functions and manipulations: Learn common string manipulation methods.

  • 🔍 Pattern searching: Naive, KMP, and Rabin-Karp algorithms.

  • 📝 Practice: Work through common string-based problems.

Month 2: Linked Lists and Stacks

Week 5: Linked Lists

  • 🔗 Basics: Understand types of linked lists (Singly, Doubly, Circular).

  • ⚙️ Operations: Insert, delete, traverse linked lists.

  • 📝 Practice: Solve beginner linked list problems.

Week 6: Advanced Linked Lists

  • 🚀 Advanced problems: Explore complex linked list problems.

  • 📝 Practice: Continue solving intermediate problems.

Week 7: Stacks

  • 🥞 Basics of Stack: LIFO concept and common operations (push, pop, peek).

  • 📊 Applications: Learn about stack use cases (e.g., function calls, parentheses validation).

  • 📝 Practice: Stack-related problems.

Week 8: Queues

  • 🚶 Queue basics: FIFO concept and operations (enqueue, dequeue).

  • 🧩 Types: Understand simple, circular, and priority queues.

  • 📝 Practice: Queue-based problem solving.

Month 3: Trees

Week 9: Trees

  • 🌳 Basics: Introduction to tree data structures.

  • 🔄 Traversals: Learn in-order, pre-order, and post-order traversals.

Week 10: Binary Search Trees (BST)

  • 🔍 BST properties: Insertion, deletion, and searching in BST.

  • 📝 Practice: Solve problems around BST operations.

Week 11: Balanced Trees

  • ⚖️ AVL Trees: Understand self-balancing trees.

  • 🔴⚫ Red-Black Trees: Explore properties and operations.

  • 📝 Practice: Work through balanced tree problems.

Week 12: Heaps

  • ⛰️ Heap basics: Learn about min-heaps and max-heaps.

  • 🧮 Priority Queue: Applications and implementation.

  • 📝 Practice: Heap-related problems.

Month 4: Graphs

Week 13: Graphs

  • 🌐 Basics: Introduction to graph theory.

  • 🛠️ Representation: Adjacency matrix, adjacency list.

Week 14: Graph Traversal

  • 🚶‍♂️ BFS and DFS: Understand graph traversal methods.

  • 📝 Practice: Solve traversal problems.

Week 15: Shortest Paths

  • 🛣️ Dijkstra's Algorithm: Shortest path in weighted graphs.

  • 🚀 Bellman-Ford: Handle negative weights.

  • 🌐 Floyd-Warshall: All-pairs shortest paths.

  • 📝 Practice: Work through pathfinding problems.

Week 16: Minimum Spanning Tree (MST)

  • 🌲 Kruskal's and Prim's: Learn how to find MST.

  • 📝 Practice: MST-related problems.

Month 5: Advanced Topics

Week 17: Dynamic Programming (DP) + Recursion

  • 🤖 DP Basics: Learn memoization vs tabulation.

  • 📜 Classic problems: Explore common DP problems.

Week 18: Advanced DP

  • 🧩 Complex problems: Solve more challenging DP problems.

  • 📝 Practice: Continue practicing DP.

Week 19: Backtracking

  • 🔙 Basics: Introduction to backtracking.

  • 🧩 Classic problems: Solve recursive and backtracking problems.

Week 20: Greedy Algorithms

  • 💡 Basics: Learn the greedy approach to problem-solving.

  • 📜 Classic problems: Solve typical greedy algorithm problems.

  • 📝 Practice: Continue working on greedy problems.

Month 6: Review and Mock Interviews

Week 21-24: Review and Practice

  • 🔄 Topics: Arrays, strings, linked lists, stacks, queues, trees, heaps, graphs, DP, backtracking, greedy.

  • 📝 Mixed problems: Focus on mixed-topic problem-solving.

Week 25: Mock Interviews

  • 🎤 Interviews: Participate in mock interviews and timed problem-solving.

  • 📝 Platforms: Solve problems on LeetCode, HackerRank, CodeChef.

Week 26: Final Revision

  • 🔍 Weak areas: Focus on areas where you need more practice.

  • 📝 Final practice: Prepare for interviews by solving final sets of problems.


Akshay Nandwana
Founder AndroidEngineers

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