Data Structures & Algorithms with Java

Data Structure and Algorithms with Java

SE1101 Object-oriented Application Development

3 hours One requisite

1 Introduction to Data Structures: / Overview of data structures and algorithms. / Python’s built-in data structures (lists, tuples, dictionaries). / Review Arrays (static vs dynamic arrays). / Numeral algorithms: basic arithmetic and modular arithmetic. / Lab: / Exercises in numeral algorithms (e.g., computing the average of a list of numbers, finding the minimum, maximum, and mode in a list, approximating the square root of a number, or finding the greatest common divisor GCD). / Implement linear search in Python

2 Runtime and Memory Efficiency: / Introduction to time complexity and Big-O notation. / Revisiting runtime and memory efficiency for all covered algorithms. / Trade-offs between time and space in practical implementations. / Introduction to amortized analysis for data structures (dynamic arrays, Fibonacci tree). / Lab: / Analyze the efficiency of various data structure implementations. / Optimize a given algorithm for both runtime and memory. / Comparison of sorting algorithms (runtime, memory efficiency)

3 Linked Lists: / Linked lists: singly, doubly, and circular linked lists. / Comparison of arrays and linked lists. / Operations: insertion, deletion, traversal. / Lab: / Implement linked lists in Python. / Solve problems like reversing a linked list and detecting cycles

4 Stacks and Queues: / Stack operations and applications: expression evaluation, function call stacks. / Queue types: linear, circular, and double-ended queues (deque). / Applications in scheduling and buffering. / Lab: / Implement stacks and queues in Python. / Solve real-world scenarios like balanced parentheses and task scheduling

5 Hash Tables: / Hash functions and their properties. / Collision resolution strategies: chaining, open addressing. / Efficiency of hash tables in searching and insertion. / Lab: / Implement a hash table from scratch in Python. / Solve problems like word frequency counting and anagrams detection

6 Trees: / Binary trees: structure and traversal techniques. / Binary Search Trees (BST): insertion, deletion, and search. / Balanced trees: AVL trees, and rotations. / Heaps: min-heap and max-heap, Fibonacci Trees. / Lab: / Implement binary trees and BSTs in Python: (discuss result with sorted and unsorted data). / Implement heaps and perform heap sort in Python. / Solve problems like finding the k largest/smallest elements

7 Recursion and String-Matching Algorithms: / Recursion: / Basics of recursion. / Divide and Conquer Algorithms / Lab: / Solve problems using recursion (e.g., Fibonacci, factorial)

8 Sorting and Searching Algorithms: / Basics of sorting algorithms: define internal/external sort and in-place sort. / Sorting Algorithms Examples: (quicksort, heapsort, merge sort, selection sort, insertion sort algorithms). / Lab: / Implement basic sorting algorithms in Python. / Solve real-world sorting problems

9 Fundamental Graph Algorithms: / Fundamental definitions and applications, bipartite graphs, strongly connected components, and topological sorting. / Graph representation: adjacency matrix and adjacency list. / Graph traversal: Breadth-First Search (BFS), and Depth-First Search (DFS). / Lab: / Implement BFS and DFS in Python. / Solve problems like detecting connected components and cycles

1.1 Mapped to: K1

Explain the fundamentals of algorithms analysis

Teaching Strategy Lectures, discussions, lab tasks and assignments

Assessment Methods Quizzes, lab exercises, assignments, and exams

1.2 Mapped to: K1

Acquire the key concepts of data structures

Teaching Strategy Lectures, lab tasks and assignments

Assessment Methods Quizzes, lab exercises, assignments, and exams

2.1 Mapped to: S2

Demonstrate fundamental knowledge and understanding of data structure and its implementation.

Teaching Strategy Lectures, labs, exercises

Assessment Methods Lab exercises and assignments, and exams

2.2 Mapped to: S2

Design and implement and choose the most appropriate data structure for a certain problem.

Teaching Strategy Lectures, labs, exercises

Assessment Methods Lab exercises and assignments, and exams

2.3 Mapped to: S1, S2

Assess and select an algorithm and justify the selection, and then apply the algorithm to a certain problem.

Teaching Strategy Lectures, labs, exercises

Assessment Methods Lab exercises and assignments, and exams

Assessment	Weight





Assignments and Quizes	30%
Midterm Exams	30%
Theoretical Final Exam	40%
Total	100%

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Data Structures & Algorithms with Java

Data Structures & Algorithms with Java

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