Week-wise Modules

• The course is divided into 19 modules which will be covered in 3 months
• Getting to know DSA (2 hours), Algorithm analysis
• Learning the Importance of algorithm analysis (2 hours),Time and space complexity,Big O notation,Complexity theory
• Working on knowing various Types of complexity (worst-case, average-case, best-case) (2hours),Time complexity classes (O (1), O(log n), O(n), O(n log n), O(n^2), etc.),Space complexity,Asymptotic notation.
• Learning Big O notation (2 hours),Omega notation,Theta notation,Data Structures, Arrays
• Working on the art of Basic operations (access, insert, delete) (2 hours), Multi-dimensional arrays, Dynamic arrays, Linked lists, Singly linked lists, Doubly linked lists,Circular linked lists,Operations (insertion, deletion, traversal),Time complexity analysis
• Making Stacks and queues hand in hand (2 hours),Stack implementation and operations (push, pop),Queue implementation and operations (enqueue, dequeue),Applications of stacks and queues, Trees
• Building Binary trees (2 hours), Binary search trees, AVL trees, Heap trees, Traversals (in-order, pre-order, post-order), Operations (insertion, deletion, searching),Heaps.
• Learning Max heap and min heap (2 hours),Heapify operation,Heap sort algorithm,Priority queues,Hash tables.
• Basic to advance Hashing functions (2 hours),Collision resolution techniques (chaining, open addressing),Hash table operations (insertion, deletion, searching), Load factor and resizing
• Build your Graphs (2 hours),Graph representation (adjacency matrix, adjacency list),Graph traversal algorithms (depth-first search, breadth-first search),Shortest path algorithms (Dijkstra's algorithm, Bellman-Ford algorithm),Minimum spanning tree algorithms (Prim's algorithm, Kruskal's algorithm),Algorithm Design Techniques,Brute force
• Learning Exhaustive search (2 hours),Generating all subsets or permutations,Time complexity analysis
• Dividing and conquering is not just in kingdoms (2 hours), Concept of dividing a problem into smaller sub problems, Examples: merge sort, quicksort, binary search,Time complexity analysis
• Learn the basics of Dynamic programming (2 hours),Memorization and tabulation,Examples: Fibonacci series, knapsack problem,Time complexity analysis
• The only Greedy algorithms you ever wanna learn (2 hours),Greedy choice property,Examples: fractional knapsack, minimum spanning tree,Time complexity analysis
• Backtracking is always fun (2 hours),Concept of backtracking and recursion,Examples: N-Queens problem, Sudoku solver,Time complexity analysis, Specific Algorithms
• Sorting algorithms not life (2 hours), Bubble sort, Selection sort, Insertion sort,Merge sort,Quick sort,Radix sort,Time complexity analysis and comparison
• Let’s Search algorithms (2 hours),Linear search,Binary search,Interpolation search,Hash-based search,Time complexity analysis and comparison
• Keep in track your Graph traversal algorithms (2 hours),Depth-first search (DFS),Breadth-first search (BFS), Topological sorting
• Live projects included with every module (6 hours)