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Major Courses

  • Introduction to mathematical modeling of computational problems
  • Common algorithm
  • Algorithm paradigms
  • Data structures
  • Relationship between algorithms and programming
  • Basic performance measures and analysis techniques for these problems
  • Algorithms Data Structures in Java
  • Algorithms and Data Structures in Python
  • Recursion, Backtracking, and Dynamic Programming in Java
  • JavaScript Algorithms and Data Structures
  • Union-find
  • Analysis of algorithms
  • Stacks and queues
  • Elementary sorts
  • Merge sort
  • Asymptotic (“Big-oh”) notation
  • Sorting and searching algorithms
  • Master method for analyzing algorithms
  • Quicksort algorithm and its analysis
  • Data structures-heaps, balanced search trees, hash tables, bloom filters
  • Dijkstra’s shortest-path algorithm
  • Breadth-first and depth-first search and its applications
  • Greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes)
  • Dynamic programming (knapsack, sequence alignment, optimal search trees)
  • Shortest paths algorithms (Bellman-Ford, Floyd-Warshall, Johnson)
  • NP-complete problems and exact and approximation algorithms for them
  • Local search algorithms for NP-complete problems
  • Quicksort
  • Priority queues
  • Geometric algorithms like Polynomial Multiplication
  • String algorithms
  • Numerical algorithms
  • Matrix Operations, GCD, Pattern Matching, Subsequences, Sweep, and Convex Hull
  • Graph algorithms like shortest path and spanning tree
  • Elementary symbol tables
  • Balanced search trees
  • Geometric applications of BSTs
  • Hash Tables
  • Symbol table application