Data Structures Compendium

Data structures can be seen as instrumental tools used to address a wide array of problems. They frequently encompass complex and mathematical concepts and can often be rather intricate. However, it’s not essential to have an all-encompassing knowledge of every data structure. Instead, it’s more crucial to gain a profound understanding of certain fundamental concepts and be skilled at applying them effectively.

Here are the topics that merit your focused attention and comprehension:

• Complexity Analysis – understanding how to measure and compare algorithm efficiency using time and space complexity.
• Memory – how data is stored and accessed in RAM, including the distinction between stack and heap memory.
• Big O Notation – the standard notation for describing the upper bound of an algorithm’s growth rate.
• Logarithm – the mathematical concept behind the efficiency of divide-and-conquer algorithms and balanced trees.
• Arrays – contiguous blocks of memory offering O(1) access by index, the most fundamental data structure.
• Linked Lists – sequences of nodes where each node points to the next, enabling efficient insertions and deletions.
• Hash Tables – key-value stores providing average O(1) lookup through hashing functions.
• Stacks and Queues – LIFO and FIFO structures used extensively in algorithms, parsing, and scheduling.
• Strings – sequences of characters with their own set of manipulation techniques and pattern-matching algorithms.
• Graphs – collections of vertices and edges that model relationships and networks.
• Trees – hierarchical structures with a root node and children, fundamental to searching and sorting.
• Depth First Search in Java – a tree/graph traversal algorithm that explores as deep as possible along each branch before backtracking.

Additional data structures worth knowing about:

• Heaps – specialized tree-based structures that satisfy the heap property (min-heap or max-heap), commonly used to implement priority queues. Heaps provide O(1) access to the minimum or maximum element and O(log n) insertions and deletions.
• Tries (Prefix Trees) – tree-like structures optimized for storing and searching strings by their prefixes. Tries excel at problems involving autocomplete, spell-checking, and IP routing, offering O(m) lookup time where m is the length of the key.