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Steps you need to take to learn Data Structures and Algorithms.

2 min read

1. Foundational Knowledge: #

  • Computer Science Basics: Before diving deep, ensure you have a grasp on basic computer science concepts, such as variables, loops, and functions.
  • Choose a Programming Language: Pick a language you’re comfortable with. Python, Java, and C++ are popular choices for data structures and algorithms due to their syntax and extensive libraries.

2. Learn Data Structures: #

  • Start with Basics: Understand fundamental data structures first—Arrays, Linked Lists, Stacks, and Queues.
  • Progress to Advanced Structures: Trees (Binary Trees, Binary Search Trees, AVL Trees), Graphs, Hash Tables, and Heaps.

3. Dive into Algorithms: #

  • Sorting and Searching: Begin with basic algorithms like Bubble Sort, Selection Sort, Binary Search, and then move to more advanced ones like QuickSort and MergeSort.
  • Common Paradigms: Understand different algorithmic paradigms:
    • Divide and Conquer: Break problems into smaller parts.
    • Greedy Algorithms: Make the best choice at each step.
    • Dynamic Programming: Break problems into smaller sub-problems and store results to avoid redundant computations.
    • Backtracking: Try out all possibilities to find a solution.
  • Graph Algorithms: Study algorithms like Dijkstra’s and Floyd-Warshall for pathfinding, and algorithms for traversals like BFS and DFS.

4. Practice Regularly: #

  • Coding Platforms: Use platforms like LeetCode, HackerRank, and Codewars. Start with easier problems and gradually tackle medium and hard ones.
  • Timed Challenges: Simulate real test conditions by setting a timer. We recommend spending 30 minutes MAXIMUM per question. Any longer than that, you’ll begin to experience diminishing marginal returns.

5. Deepen Your Understanding: #

  • Analyze Different Solutions: After solving a problem, review other solutions. This can expose you to different approaches and techniques.
  • Space and Time Complexity: Understand Big O notation. Analyze the efficiency of your solutions and strive for optimization.

6. Mock Interviews and Tests: #

  • Simulate Real Conditions: Practice with peers or use platforms like Pramp for mock technical interviews.
  • Feedback Loop: After each mock session, gather feedback and work on areas of improvement.

7. Review and Revise: #

  • Regularly Revisit: Periodically go back to topics you’ve covered to refresh your memory.
  • Cheat Sheets: Create summary notes or cheat sheets for quick revisions.

8. Mindset and Attitude: #

  • Stay Persistent: Mastery takes time. Don’t get discouraged by challenging problems.
  • Understand, Don’t Memorize: Strive to understand the underlying principles instead of rote memorization.