Weeks 1-2: Focus on basic data structures (arrays, linked lists, stacks, and queues). Understand their implementations, operations, and applications. Start with simple problems.
Weeks 3-4: Move on to trees and graphs. Learn about binary trees, binary search trees, graph representations, and basic graph algorithms (DFS, BFS).
Weeks 5-6: Dive into sorting and searching algorithms. Understand different sorting techniques and their complexities. Practice binary search problems.
Weeks 7-8: Explore more complex data structures like heaps, tries, and hash tables. Solve problems that require these data structures.
Weeks 9-10: Begin studying dynamic programming. Start with basic problems and gradually move to more complex ones.
Weeks 11-12: Cover greedy algorithms, backtracking, and advanced topics in dynamic programming.
Weeks 13-14: Revisit all data structures and algorithms, focusing on areas of weakness. Solve complex and mixed problems.
Final Days: Take mock interviews, solve problems under timed conditions, and refine your problem-solving strategies.
Practice Daily: Aim for at least 1-2 new problems each day and revisit previous ones to reinforce concepts.
Understand, Don't Memorize: Focus on understanding the logic behind algorithms and data structures instead of memorization.
Peer Learning: Engage with a community or find a study partner to discuss problems and solutions.
Review and Reflect: Regularly review your progress, identify weak areas, and adjust your learning plan accordingly.