Learn how to evaluate algorithms by quantifying their time and space requirements using Big O, Theta, and Omega notations.

Understand arrays with their contiguous memory storage for fixed-size sequences, supporting constant-time indexing and linear-time insertion or deletion at arbitrary positions. Dynamic Arrays are also discussed at length.

Describe associative arrays that map keys to values via hash functions, enabling average-case constant-time insertions, deletions, and lookups. Learn about how hashing and compression functions work along with utilizing different techniques on how to handle key collisons.

Review techniques such as linear search and binary search for efficiently locating elements within data structures.

Examine algorithms like bubble sort, selection sort, insertion sort, merge sort, quicksort, and heapsort for ordering data based on various performance trade-offs.

Details storage and manipulation of character sequences, including operations for concatenation and substring search.

Learn to operate with singly, doubly, and circular linked lists for dynamic data sequences with constant-time insertions and deletions at known positions.

Use different variants of last-in, first-out (LIFO) structures for managing nested operations, function calls, and undo/redo mechanisms.

Understand first-in, first-out (FIFO) structures for scheduling tasks, buffering data streams, and breadth-first traversal.

Learn a different way to solve problems by utilizing functions that call themselves with smaller inputs, and gain a deeper insight into the analysis of recursion depth and base cases.

Learn different techniques to optimize recursive problems by storing and reusing solutions to overlapping subproblems to achieve polynomial-time solutions.

Explore different hierarchical node-based structures, including definitions, traversals (preorder, inorder, postorder), and common use cases.

Learn about binary trees that maintain the heap order, their variants, operations and how they’re used for efficient priority queue implementations.

Cover ordered binary trees supporting logarithmic-time search, insertion, and deletion in balanced scenarios.

Learn to use prefix trees for storing strings, enabling efficient retrieval and prefix-based operations.

Define vertex and edge representations for modeling networks, directed and undirected graphs, and common storage formats.

Have a deeper understanding of common graph traversal (DFS, BFS), shortest paths (Dijkstra’s, Bellman-Ford), topological sorting(Kahn’s Algorithm), and cycle finding algorithms.

Learn how to implement and use disjoint-set data structures for maintaining element partitions with near-constant-time union and find operations.

Explain and implement algorithms such as Kruskal’s and Prim’s for connecting all vertices with minimal total edge weight.

Describe depth-first search of solution spaces with pruning to solve constraint satisfaction problems.

Learn to utilize low-level operations (AND, OR, XOR, shifts) for efficient arithmetic and algorithmic optimizations.