Recommended Starting Point
Jumping into advanced problems without a solid base is a common mistake. Whether you're just beginning your DSA interview preparation or picking it up after a break, starting with the DKodeTree Roots 25 set is highly recommended. This focused collection is designed to help you build a rock-solid foundation in core problem-solving techniques—a critical step in cracking coding interviews.
Too often, candidates dive into large problem sets without first mastering the basics, only to feel overwhelmed and discouraged. The Roots 25 ensures you gain clarity, confidence, and consistency early on.
So, when you move on to more advanced lists, you're not just solving problems—you’re truly understanding them. Strong fundamentals aren’t just helpful—they’re essential for success in top tech interviews.
This roadmap outlines the core problem-solving categories. While the order provides a logical progression, consistent practice across all areas is key to success.
Master using hash maps/sets for efficient lookups, counting, and grouping.
Understand techniques involving precomputed prefix sums/products for quick range queries.
Learn techniques involving iterating with two pointers, often from different ends or at different speeds.
Apply binary search on sorted data for logarithmic time complexity searches and optimizations.
Learn various sorting algorithms and their applications in problem-solving.
Understand how to use a dynamic window to process contiguous subarrays or substrings efficiently.
Utilize stack data structure for problems involving LIFO (Last-In, First-Out) order, like parentheses matching.
Work with nodes and pointers, understanding traversal, manipulation, and cycle detection.
Explore binary tree structures, traversals (DFS, BFS), and common algorithms like finding depth or checking for equality.
Understand BST properties, validation, searching, insertion, deletion, and finding LCA or Kth smallest element.
Work with general tree structures (more than two children per node), including traversals like preorder, postorder, and level order.
Use heaps to efficiently manage ordered elements, finding min/max, or scheduling tasks.
Solve problems by exploring all possible solutions incrementally and abandoning paths that don't work.
Understand graph representations, traversals (BFS, DFS), and fundamental graph algorithms like finding connected components or detecting cycles.
Tackle more complex graph problems involving algorithms like Dijkstra's, MST, and advanced applications of traversals.
Solve problems using a 1D array or few variables to store results of subproblems, optimizing for overlapping subproblems.
Utilize 2D tables to solve problems involving two sequences or states, often related to grids or matrices.
Learn to make locally optimal choices at each stage with the hope of finding a global optimum.
Solve problems involving merging, inserting, or checking overlaps of intervals.
Understand Trie data structures for efficient string prefix searching and operations.
Solve problems involving properties of integers, divisibility, prime numbers, and modular arithmetic.
Work with 2D arrays, performing operations like rotation, traversal, and searching.
Tackle problems that require designing data structures or systems.
Understand and apply bitwise operations to solve problems efficiently.
Keep Practicing!
Consistent practice across all these categories will significantly improve your problem-solving abilities. Good luck!
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