The LeetCode Beginner's Guide
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Explore Card Track
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The Explore Card Track covers a very wide range; most of us will start somewhere on this track. Where we start depends on our previous experience and current skillset.
This track is designed to guide you when learning the fundamental data structures and algorithms that can be used to solve a wide variety of problems. The content is introduced topic by topic since, for most, it is faster and less challenging to learn new data structures and algorithms this way. The following graphic will help guide you when choosing the next topic to study and is intended to help you avoid running into content that is too challenging.
Explore Card Layers
As long as you follow the order shown in the flow chart, you will minimize the probability of running into material that is too challenging too early on. Although, for the smoothest results, we recommend going through the explore cards in the order listed here. The numbers below correspond to the questionnaire result.
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First and Second Layers: Arrays 101, Linked List, Array and String, Hash Table, Recursion, Queue & Stack, Heap, and Binary Search
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Second and Third Layers: Recursion, Binary Tree, Binary Search Tree, Trie, N-ary Tree, and Recursion II
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Third and Fourth Layers: Recursion II, Dynamic Programming, and Graph
Topics Available
- array_and_string
- hash_table
- queue_and_stack
- heap
- binary_search
- arrays_101
- linked_list
- recursion_1
- binary_tree
- recursion_2
- machine_learning, trie, nary_tree, binary_search_tree, dynamic_programming, graph, system-design, decision_tree
While on the Explore Card Track, we recommend, for each topic, go through the tutorial material in the Explore Card, then go to the problems page and pick out 5 to 10 problems of suitable difficulty (easy/medium) that have the topic tag for the current explore card. Spend 15 - 20 minutes trying to solve each one.
Study Tip: We all solve problems at different speeds, and this speed very gradually increases over time. Some may spend an hour on the average problem, and some may spend 5 to 10 minutes. Don't use the 15 to 20 minutes as a concrete time limit. Go at your own pace, and when you're stuck, first check just the titles in the discussion section or just the hints for the problem, then give the problem a little more thought. If you're still stuck, then check the solution article and discussion posts.
If you can’t solve the problem on your own, that’s okay! Read and try to understand the solution, then return to the problem and try to solve it with what you’ve learned. Over time, the information you gain from reading others’ solutions will help you solve new problems independently without relying on the discuss section or the official solution. That said, still, read the discuss section and the official solution when you solve the problem on your own. If you see a more efficient or more elegant solution than your own, study it and take that information with you for future problems.
Repeat this process for each topic and in time, you will gain a better understanding of how to use all of the fundamental data structures and algorithms.
That said, you don't have to restrict yourself to only Explore Card content while you're learning. It is good to expose yourself to some of the things listed in the General Advice section. Learning the fundamentals and solving problems one topic at a time makes it easier to absorb the information and learn new skills. However, it is also a crutch. In the real world or in an interview setting, you probably won't be told what's the best data structure or algorithm to use. We recommend solving at least one problem a day where you do not know the DSA ahead of time. The Daily Coding Challenge, Contests, and Assessments are all good options for this. Doing these will allow you to practice using the problem description and constraints to think of possible approaches and implement the best one. Although, depending on how much time you have to put towards your DSA studies, this exercise can wait until after you have completed the Explore Card track.