Day 1: What is Data Science, Applications of Data Science, Python Libraries for Data Science, NumPy- Arrays, Standard Data Types, UFuncs, Aggregates, Broadcasting. Pandas- Series Object, DataFrame Object, Handling Missing Data, Trade-Offs in Missing Data Conventions, Missing Data in Pandas, Operating on Null Values, Combining Datasets: Concat and Append, Aggregation and Grouping, Matplotlib
Day 2: Linear Regression with Boston Housing Price Dataset, Logistic Regression with Titanic DatasetDay 1 Day 2
Day 1:Introduction to Data Structures, Stacks, Queues, Linked Lists, Linked Stacks and Linked Queues.
Day 2:Trees, Binary Tree, Binary Tree Traversal, Binary Search Tree and Operations on BST.
Day 3:Introduction to Graphs, Terminologies, Representation of Graphs, BFS, DFS, Dijkstra's Algorithm, Weighted Graphs, Spanning Trees, Prim's and Krushkal's AlgorithmDay 1 Day 2 Day3