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Home / Syllabus / B.Sc. (Data Science) / SY BSc Data Science / Semester 3 / Design and Analysis of Algorithms and Data Warehousing and Mining Practical

B.Sc. (Data Science) Design and Analysis of Algorithms and Data Warehousing and Mining Practical Syllabus - Mumbai University 2026

This is the SY BSc Data Science syllabus under NEP 2020, in force from the academic year 2025-26. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.

Design and Analysis of Algorithms and Data warehousing and Mining Practical Syllabus.pdf
Major · Semester 3 · SY BSc Data Science · 2 credits · 50 marks

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Syllabus for Design and Analysis of Algorithms and Data warehousing and Mining Practical

Major · Semester 3 · SY BSc Data Science · 2 credits · 50 marks

Module I: Algorithm Analysis - Time Complexity Calculation: Write a Python or C++ program to find the time complexity of a given algorithm using step

  • counting method. Analyze both recursive and non-recursive algorithms.
  • Implementing and Analyzing Sorting Algorithms: Implement Bubble Sort, Insertion Sort, and Merge Sort for a list of integers. Compare their time complexities for different input sizes (n=1000, 5000, 10000).
  • Singly Linked List Operations: Write a program to implement a Singly Linked List with the following operations: Insert at beginning Insert at end Insert at given position Delete from beginning Delete from end Search an element Display list
  • Circular Linked List Operations: Implement a Circular Linked List and demonstrate Insertion and Deletion operations at various positions.
  • Stack using Linked List and Array: Implement a Stack ADT using both: Arrays Singly Linked List Compare and explain the advantages/disadvantages of each.
  • Balanced Parentheses Checker using Stack: Write a program to check if a given expression has balanced parentheses using a Stack.
  • Queue using Array and Linked List: Implement a Queue ADT using both. Array (Circular Queue) Linked List Compare memory usage and performance for different sizes of input.
  • Binary Tree Traversals: Write a program to create a Binary Tree.
  • Implement Preorder, Inorder, and Postorder Traversals (Recursive and Iterative using Stacks).
  • Expression Tree Construction: Construct an Expression Tree for a given postfix expression and evaluate the expression using the tree.
  • AVL Tree Implementation: Implement an AVL Tree with insertion and rotation operations. Display the tree after each insertion.
  • Graph Representation and Traversals: Create a Graph using an Adjacency List and Adjacency Matrix. Implement Depth First Search (DFS) and Breadth First Search (BFS) for the graph.
  • Shortest Path using Dijkstra's Algorithm: Implement Dijkstra’s Algorithm to find the shortest path from a source node to all other nodes in a weighted graph.
  • Minimum Spanning Tree using Prim’s and Kruskal’s Algorithms:
  • Write a program to compute MST (Minimum Spanning Tree) for a connected graph using Prim’s Algorithm and Kruskal’s Algorithm
  • Sorting Algorithm Performance Comparison: Implement Quick Sort and Heap Sort for large arrays (n = 10000) and compare their performance using the time library.
  • Searching Techniques Comparison Implement: Linear Search Binary Search Interpolation Search

Module II: Create an Employee Table with the help of Data Mining Tool WEKA

  • Apply pre-processing to the training data-set. 3.Show implementation of classification rule using J48 algorithm 4.Show implementation of classification rule using id3 algorithm
  • Show the implementation of Naïve Bayes algorithm.
  • Show the implementation of Decision Tree.
  • Show the implementation of Time Series Algorithm.
  • Show the implementation of Clustering Algorithm.
  • Show the implementation of k-nearest neighbor. 10.Show the implementation of Apriori Algorithm 11.Show the implementation of Association Algorithm. 12.Show the implementation of Text Mining. 13.Show the implementation of Multimedia Mining. 14.To apply the web mining technique clustering algorithm for the given dataset. 15.Show the implementation of Spatial Mining.

Textbooks

  • 1 M. H. Dunham. Data Mining: Introductory and Advanced Topics. Pearson Education.
  • 2 J. Han and M. Kamber, “Data Mining: Concepts and Techniques”, Second Edition, Elsevier.
  • 3 Krzysztof J. Cios, W. Pedrycz, R. W. Swiniarski, L. A. Kurgan, “Data Mining” A Knowledge Discovery Approach”, Springer (Unit I).
  • 4 Dr. CarolynK. Hamm, “Oracle Data Mining”, RampantTechPress, SPD.
  • 1 C. Ballard, Dynamic Warehousing and Data Mining Made Easy, ReddBooks, IBM (SPD)
  • 2 H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. 2005.
  • 3 D. Hand, H. Mannila and P. Smyth. Principles of Data Mining. Prentice-Hall. 2001.
  • 4 Z. Tang and J MacLennan, “Data Mining with SQL Server 2005”, Wiley

Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020, in force from the academic year 2025-26. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 2024 25 DS SEM I & II NEP NEP 2020 syllabus, in force from 2024-25 Read full PDF Read
PDF 2023 24 BSc Data Science Sem V & VI Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read
PDF 2021 22 BSc Data Science Sem III & IV Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read
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