λDSA Learning Hubpart of DSA Atlas

The DSA learning roadmap

Seven stages that take you from writing your first loop to walking into an onsite loop prepared. Follow the recommended order, or use it as a map to fill specific gaps.

  1. Programming Foundations

    In progress

    Get fluent in the Python and math every solution assumes: collections, functions, classes, recursion, logs, modulo, and bit operators.

    1 topics~3 hours0% done
  2. Complexity Analysis

    Up next

    Speak the language solutions are graded in — Big O/Ω/Θ, amortized analysis, recursion trees, and the Master Theorem — and map constraints to target complexity.

    1 topics~2 hours0% done
  3. Basic Data Structures

    Up next

    Master the linear structures and their trade-offs: arrays, strings, linked lists, stacks, queues, hashing, plus recursion, sorting, and searching.

    9 topics~26 hours0% done
  4. Trees and Graphs

    Up next

    Move to hierarchical and networked data: tree traversals, BSTs, heaps, tries, and graph BFS/DFS.

    6 topics~17 hours0% done
  5. Problem-Solving Patterns

    Up next

    Learn the reusable templates that recur across hundreds of problems, and how to recognize which one a prompt is asking for.

    5 topics~11 hours0% done
  6. Advanced Algorithms

    Up next

    Tackle the hardest interview material: backtracking, greedy, dynamic programming, bit manipulation, and weighted-graph algorithms.

    5 topics~17 hours0% done
  7. Interview Preparation

    Always open

    Assemble everything into an execution plan: 30/60/90-day schedules, company focus, timed mock interviews, and a revision dashboard.