IIT M BS DATA SCIENCE

today is 17 december i am revising for end term exams of iitm bs data science. 11 and 12 problem solving with speed and accuracy developing skill

Edge vertices  Directed and indidrected  Indegree and put degree Questions Breadth firstsiearch

Set theory ऑपरेशन ,  union intersection elements properties empty sets questions .... Cardinality  Relations  Binary relations  for ordered pair Transtive equivalence  3.Graphing of polynomials Functions 

Prim's algorithm is another efficient greedy algorithm for finding a minimum spanning tree (MST) in a weighted undirected graph. It works by starting with an arbitrary vertex and iteratively adding the cheapest edge that connects the growing tree to an unvisited vertex, until all vertices are included. Here's how it works: Start with an empty MST and choose any vertex as the starting point. Identify all edges connected to the starting vertex. Add the cheapest edge that doesn't create a cycle to the MST. Consider the vertex at the other end of the added edge. Add all edges connected to this new vertex to the set of candidate edges. Repeat steps 2 and 3, always adding the cheapest edge that doesn't create a cycle to the MST and expanding the tree to include new vertices, until all vertices are included. Here are some key points about Prim's algorithm: It is a greedy algorithm, meaning it makes the locally optimal choice at each step (adding the cheapest edge) in the

Kruskal's algorithm is a greedy algorithm to find a minimum spanning tree for a weighted connected graph. It works by finding the cheapest edge that connects two different trees in the forest, and then adding that edge to the forest. This process is repeated until all of the vertices are connected in a single tree. Here is the implementation of Kruskal's algorithm in Python: Python class Graph:   def __init__(self, vertices):     self.vertices = vertices     self.graph = {}     for vertex in self.vertices:       self.graph[vertex] = []   def   addEdge(self, u, v, weight):     self.graph[u].append((v, weight))     self.graph[v].append((u, weight)) def find(parent, i):   if parent[i] != i:     parent[i] = find(parent, parent[i])   return parent[i] def union(parent, rank, x, y):   xRoot = find(parent, x)   yRoot = find(parent, y)   if rank[xRoot] < rank[yRoot]:     parent[xRoot] = yRoot   elif rank[xRoot] > rank[yRoot]:     parent[yRoot] = xRoot   else:     parent[yRoot] = x

Low grades but it's okk कुछ नही तो तजुर्बा होगा। Maths: Revision lecture on Set Theory Video Revision lecture on Relations Video Revision lecture on Graphing of polynomial Video Revision lecture on Functions Video Revision lecture on Logarithmic Functions Video Revision lecture on Graph theory Basics Video Revision lecture on Exponential and Composite Function Video Kruskals Algorithm:  उद्देश्य  compute minimum cost spanning trees using Kruskal’s algorithm and the concept of correctness of Kruskal’s algorithm. सीखा