Final Remarks
Here’s a quick summary of what we learned from the “Heaps, Hashing and Tracking” module.
We'll cover the following
Congratulations! You have successfully completed the “Heaps, Hashing and Tracking” module.
Summary#
In this module, we explored the coding patterns that empower us to navigate data-centric challenges with finesse. We began our exploration with Hash Maps, a versatile tool for efficient data storage, retrieval, and tracking. Moving forward, we dived into the Knowing What to Track pattern, emphasizing the art of identifying and storing crucial information during problem-solving. The pattern Top K Elements equips us with efficient techniques to address diverse challenges such as extracting the most significant elements from datasets, finding frequent elements, extreme values, or elements as per any other criteria. Finally, we delved into Two Heaps, our solution for managing minimum and maximum elements efficiently. By employing two distinct heaps, we acquired the ability to balance extreme values and maintain order in dynamic datasets. These patterns are pivotal for optimizing algorithms and crafting elegant solutions, a foundation for success in coding interviews.
Additional resources#
If you’re looking for more challenges to solve using the patterns in this module, you can hone your skills on these problems:
Pattern Name | Problem Name |
Hash Maps | |
Hash Maps | |
Hash Maps | |
Hash Maps | |
Hash Maps | |
Knowing What to Track | |
Knowing What to Track | |
Knowing What to Track | |
Knowing What to Track | |
Knowing What to Track | |
Knowing What to Track | |
Top K Elements | |
Top K Elements | |
Top K Elements | |
Top K Elements | |
Two Heaps | |
Two Heaps | |
Two Heaps |
What’s next?#
Having acquired valuable hands-on experience with solving problems using the Knowing What to Track, Top K Elements, and Two Heaps patterns, we're now well-prepared to delve deeper into the use of heaps to solve problems using the K-way Merge Pattern, as well as learn other techniques to merge data from multiple sources, in the next module, “Fusion.”
Solution: Find Median from a Data Stream
Fusion