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+# Fenwick Tree (Binary Indexed Tree)
+
+A Fenwick Tree, or Binary Indexed Tree (BIT), is a data structure that provides efficient methods for calculation and manipulation of the prefix sums of an array. Compared to a standard Segment Tree, a Fenwick Tree requires less memory, is much easier to implement, and performs both element updates and prefix sum queries in $O(\log n)$ time. It teaches you how to leverage bits and lowest significant bit (LSB) operations to navigate a tree-like structure stored implicitly within an array.
+
+Visit the following resources to learn more:
+
+- [@article@Range Sum Query - Mutable - LeetCode](https://leetcode.com/problems/range-sum-query-mutable/)
+- [@video@Fenwick Tree / Binary Indexed Tree Complete Tutorial](https://www.youtube.com/watch?v=CWDQHfOnK08)
+- [@article@Binary Indexed Tree / Fenwick Tree Insights](https://takeuforward.org/data-structure/binary-indexed-tree-fenwick-tree)
+- [@article@Inversion Count using Fenwick Tree](https://takeuforward.org/data-structure/inversion-count-using-fenwick-tree)
+- [@video@Fenwick Tree / Binary Indexed Tree Structure](https://www.youtube.com/watch?v=uSFzHC_dgAg)
+- [@video@Inversion Count Problem using BIT](https://www.youtube.com/watch?v=kPaJfAUwViY)
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diff --git a/src/data/roadmaps/leetcode/content/segment-tree@an0thErRAnd0mId.md b/src/data/roadmaps/leetcode/content/segment-tree@an0thErRAnd0mId.md
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+# Segment Tree
+
+A Segment Tree is an advanced data structure used for storing information about intervals or segments. It allows querying a range (e.g., range sum, minimum, or maximum queries) and updating an element both in efficient $O(\log n)$ time. It teaches you how to perform bottom-up array-based divide and conquer structures and lays the groundwork for handling complex range-query competitive programming problems.
+
+Visit the following resources to learn more:
+
+- [@article@Range Sum Query - Mutable - LeetCode](https://leetcode.com/problems/range-sum-query-mutable/)
+- [@video@Segment Trees Complete Guide](https://www.youtube.com/watch?v=2bSS8rtFym4)
+- [@article@Introduction to Segment Trees | Range Queries](https://takeuforward.org/data-structure/segment-tree-intro-with-range-queries)
+- [@article@Lazy Propagation in Segment Trees (Range Updates)](https://takeuforward.org/data-structure/lazy-propagation-segment-tree)
+- [@video@Segment Trees Playlist: Basics to Lazy Propagation](https://www.youtube.com/watch?v=negJCfpBfiQ)
+- [@video@Lazy Propagation in Segment Trees Explained](https://www.youtube.com/watch?v=rwXVCDeE96c)
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diff --git a/src/data/roadmaps/leetcode/content/tries@th1sIsArAnd0mId.md b/src/data/roadmaps/leetcode/content/tries@th1sIsArAnd0mId.md
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+# Tries (Prefix Tree)
+
+A Trie is a tree-like data structure used to efficiently store and retrieve keys in a dataset of strings. It shines in prefix-matching problems, autocomplete engines, and dictionary implementations. Inserting and searching for a string of length $L$ takes $O(L)$ time, making it significantly faster than standard brute-force string comparisons. This pattern teaches you how to structure tree nodes containing character array pointers or hash maps.
+
+Visit the following resources to learn more:
+
+- [@article@Implement Trie (Prefix Tree) - LeetCode](https://leetcode.com/problems/implement-trie-prefix-tree/)
+- [@video@Trie Data Structure | Complete Implementation](https://www.youtube.com/watch?v=giiaIofn31A)
+- [@article@Implement Trie - I](https://takeuforward.org/data-structure/implement-trie-1)
+- [@article@Implement Trie - II (Advanced Operations)](https://takeuforward.org/data-structure/implement-trie-ii)
+- [@video@Tries Master Class by Striver](https://www.youtube.com/watch?v=thsleiL6zxg)
+- [@video@Implement Trie - II (Count & Erase)](https://www.youtube.com/watch?v=K5pcpkEMCN0)
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