SummerTraining presentation from lovely professional university

SummerTraining presentation

1. DATA STRUCTURES & ALGORITHM Self Paced GeeksforGeeks under the guidance of Mr. Sandeep Jain

2. Link | Your Link here Track Score : 701 Contest Rating : 1160.25

3. Course Insight Sharpens DSA from basic to advanced level Practice for being able to solve problems which are asked in PBC Solve problems in contests like coding round for SDE role Inculcates traits for becoming a strong and efficient coder Practice DSA concepts with a real time Project

4. Core for Programming Data Structures Forms the basis of data organization in our systems Algorithm Provides a series rather sequence of finite steps for solving simple to complex problems

5. Asymptotic Complexity Space and Time Complexity and their trade off checks for the effectiveness of an algorithm Best, Average and Worst-Case Complexities Big O Omega Theta Notations

6. Arrays Contiguous Linear Data Structure of items of same data type. Insertion Deletion Searching Linear Search | Binary Search (Sorted Arrays) Sorting Selection | Bubble | Insertion | Merge | Quick Sort

7. Linked Lists Non-Contiguous Linear Data Structure of items of same data type. Insertion Deletion Doubly Linked List (2 Way Linking) Circular Linked List (Tail-Head Linking)

8. Stacks Linear Data Structure following LIFO/FILO pattern. Insertion | Push Operation Deletion | Pop Operation Top/Peek Operation

9. Queue Linear Data Structure following FIFO/LILO pattern with insertion and deletion at either but one end Insertion | Enque Operation Deletion |Deque Operation Front and Rear Doubly Ended Queue and Priority Queues

10. Trees Non-Linear Data Structure where each node is connected to a number of nodes with the help of pointers or references. Preorder In-order and Post order Traversal of a Tree Level Order Traversal of a Tree Binary Tree : with nodes having at most 2 children

11. Heaps It is a Tree-based data structure, which satisfies the below properties: Heap is a complete tree (All levels are completely filled except possibly the last level and the last level has all keys as left as possible). Heap is either Min Heap or Max Heap. In a Min-Heap, the key at root must be minimum among all keys present.

12. Graphs A data structure that consists of the following two components: finite set of vertices also called nodes. finite set of ordered pair of the form (u, v) called as edge Breadth First Traversal or BFS traversal of a graph is like that of the Level Order Traversal of Trees. Depth-First Traversal or the DFS traversal of a Graph is used to traverse a graph depth wise.

13. Greedy Paradigm An algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most obvious and immediate benefit. Backtracking Algorithm An algorithmic technique for solving problems recursively by trying to build a solution incrementally, one piece at a time, removing those solutions that fail to satisfy the constraints of the problem at any point in time

14. Dynamic Programming An algorithmic approach to solve some complex problems easily and save time and number of comparisons by storing the results of past computations. The basic idea of dynamic programming is to store the results of previous calculation and reuse it in future instead of recalculating them. Overlapping Subproblems Optimal Substructure

15. Project | N-Queen Visualizer Problem N-Queen is the problem of placing N-chess queens on an N×N chessboard so that no two queens attack each other (No Queen on same Diagonal and four directions) Backtracking Algorithm The idea is to place queens one by one in different columns, starting from the leftmost column. When we place a queen in a column, we check for clashes with already placed queens. In the current column, if we find a row for which there is no clash, we mark this row and column as part of the solution. If we do not find such a row due to clashes, then we backtrack and return false.

16. Project | N-Queen Visualizer Algorithm Start in the leftmost column If all queens are placed return true Try all rows in the current column. Do following for every tried row. 1. If the queen can be placed safely in this row, then mark this [row, column] as part of the solution and recursively check if placing queen here leads to a solution. 2. If placing the queen in [row, column] leads to a solution then return true. 3. If placing queen doesn't lead to a solution then unmark this [row, column] (Backtrack) and go to step (1) to try other rows. If all rows have been tried and nothing worked, return false to trigger backtracking.

17. Project | N-Queen Visualizer GitHub Repo | https://github.com/Sayan-Batabyal/N-Queen-Visualiser.git

18. Thank You for giving such a great opportunity