What's Next?

Congratulations

You have completed all 15 lessons. You now understand sorting algorithms, binary search, stacks, queues, linked lists, graph traversal, and dynamic programming.

That is a real accomplishment. These algorithms appear in virtually every technical interview and underlie most software systems.

What to Explore Next

You have covered the fundamentals. Here is where to go deeper:

• Heaps and Priority Queues -- Efficient O(log n) min/max operations. The foundation for Dijkstra with a priority queue and heap sort.
• Hash Tables -- O(1) average lookup, insertion, and deletion. The most used data structure in practice.
• Binary Search Trees -- O(log n) search, insert, delete for dynamic sorted data.
• Balanced BSTs (AVL, Red-Black) -- Guaranteed O(log n) even with adversarial inputs.
• Tries -- Efficient prefix searches for autocomplete and spell checking.
• Union-Find -- Efficient connected components and Kruskal's minimum spanning tree.
• Topological Sort -- Ordering dependencies in build systems and package managers.
• Bellman-Ford -- Shortest paths with negative weights.

Practice

Theory is not enough — you need to practice on real problems:

• LeetCode -- the most popular competitive programming platform. Start with "Easy" problems.
• NeetCode -- curated problem lists with video explanations, organized by pattern.
• AlgoExpert -- 160 handpicked questions with video solutions.
• Project Euler -- mathematical and computational problems that reward algorithmic thinking.

References

• Introduction to Algorithms (CLRS) by Cormen, Leiserson, Rivest, and Stein -- the definitive textbook. Dense but comprehensive.
• The Algorithm Design Manual by Steven Skiena -- practical and readable. Excellent war stories from real-world algorithm use.
• Grokking Algorithms by Aditya Bhargava -- illustrated introduction, great for visual learners.
• Visualgo -- visual animations of sorting, graph, and DP algorithms.
• Big-O Cheat Sheet -- quick reference for time and space complexity.