| Course Name | Data Structures and Algorithms |
| Course Code | 24ASD501 |
| Program | M.Sc. in Applied Statistics and Data Analytics |
| Semester | 1 |
| Credits | 4 |
| Campus | Coimbatore , Kochi |
Abstraction – Abstract data types; Data Representation; Elementary data types; Basic concepts of data Structures; Mathematical preliminaries – big-Oh notation; efficiency of algorithms; notion of time and space complexity; performance measures for data structures. ADT array – Computations on arrays.
ADT Stack, Queue, list – array, linked list, cursor based implementations of linear structures. ADT Tree – tree representation, properties traversal of trees; ADT- Binary Trees – properties and algorithms.
ADT Priority Queue – Heaps; heap-based implementations; applications of heaps – sorting; Search Tree – Binary search tree; balanced binary search trees – AVL tree; Applications of Search Trees – TRIE; 2-3-4 tree; concept of B-Tree. ADT Dictionary – array based and tree based implementations; hashing – definition and application.
Sorting and Searching Algorithms: Merge Sort, Quick-Sort, Insertion Sort, Bin Sort, Bucket-Sort and Radix-Sort Selection Sort, Comparison of Sorting Algorithms. Introduction to time complexity. Bio-O, worst case complexity, polynomial classifications. Satisfiability, NP Complete and NP Hard (Definitions only).
Graphs algorithms: ADT- Data structure for graphs – Graph traversal- Transitive Closure- Directed Acyclic graphs – Weighted graphs – Shortest Paths – Minimum spanning tree – Greedy Methods for MST. Travelling salesman problem.
Course Outcomes:
CO1: Understand the various basic data types and trees.
CO2: Gain knowledge about standard data structures like Stack, Queue, list – array, linked
list.
CO3: Know the importance of priority queue – Heaps; heap-based implementations;
applications of heaps – sorting; Search Tree – Binary search tree.
CO4: To understand the basic concepts of time complexity and classes of time complexity.
CO5: To gain knowledge about same graph algorithms like shortest path algorithm and
minimal spanning tree algorithms.
CO-PO Mapping:
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PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
|
|
CO1 |
3 |
3 |
3 |
2 |
2 |
2 |
2 |
1 |
1 |
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|
CO2 |
3 |
3 |
3 |
2 |
2 |
2 |
2 |
1 |
1 |
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|
CO3 |
3 |
3 |
3 |
2 |
2 |
3 |
2 |
1 |
1 |
||||
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CO4 |
3 |
2 |
3 |
2 |
2 |
2 |
2 |
1 |
1 |
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CO5 |
2 |
2 |
2 |
1 |
1 |
2 |
1 |
1 |
1 |
Text Books /Reference Books:
in Python++”, Wiley publication, 2013.
Wiley publication, 2010.
Second Edition, Tata McGraw-Hill, 2002.
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