Master of Computer Applications Syllabus

Course Overview

• Focuses on Data Visualization and Machine Learning Models within a Master's degree in Computer Applications.
• Offered at Quantum School of Technology, located in Roorkee, Uttarakhand.

Unit 1: Data Visualization

• Introduction to foundational concepts in data visualization.
• Covers necessary data for creating effective graphics.
• Emphasizes design principles and the creation of various graphics types:
  • Categorical graphics.
  • Time series graphics.
  • Statistical data graphics.
• Total duration: 8 hours.

Unit 2: Matplotlib

• Introduction to Matplotlib, a popular Python library for data visualization.
• Basic plotting techniques using Matplotlib, including:
  • Area Plots.
  • Histograms.
  • Bar Charts.
  • Pie Charts.
  • Box Plots.
  • Scatter Plots.
• Total duration: 7 hours.

Unit 3: Machine Learning Fundamentals

• Introduction to machine learning concepts, addressing:
  • Different types of machine learning problems.
  • Data and tools essential for implementing machine learning.
• Introduction to visualization techniques relevant to machine learning.
• Overview of tools such as Matlab and Python for practical applications.
• Key concepts include:
  • Linear regression.
  • Sum of Squared Errors (SSE).
  • Gradient descent.
  • Understanding overfitting and model complexity.
  • Importance of training, validation, and test datasets.
• Total duration: 7 hours.

Unit 4: Classification Problems

• Exploration of classification challenges in machine learning.
• Key topics include:
  • Understanding decision boundaries.
  • Nearest neighbor methods.
  • Linear classifiers.
  • Ensemble methods such as:
    • Random forests.
    • Support Vector Machines (SVM).
    • Neural Networks.
• Total duration: 7 hours.

Unit 5: Unsupervised Learning

• Introduction to unsupervised classification methods, focusing on:
  • K-means clustering.
  • Fuzzy C-means clustering.
  • Gaussian Mixture models.
• Emphasis on the application and significance of these techniques in data analysis.
• Total duration: 7 hours.