Data Mining

Course details

"Data Mining" is a comprehensive exploration of the methods used to automatically discover patterns and knowledge from large amounts of data. The techniques learned will bridge the gap between data handling and making informed decisions in a business context, equipping students to provide value from vast datasets.

Start Date
TBD
Duration
14-15 weeks
Learning Objectives
  • Understand the significance and challenges of big data.
  • Grasp core concepts and techniques of data mining.
  • Learn to pre-process, analyze, and interpret complex datasets.
  • Extract actionable insights from large volumes of data.
  • Recognize the ethical considerations and implications of data mining.
Key Features
  • Hands-on data mining projects with real-world datasets.
  • Exploration of the latest data mining tools and software.
  • Expert guest lectures from industry professionals.
  • Case studies to understand real business implications.
  • Access to state-of-the-art resources via the LMS.
Learning path
  • Unit 1: Introduction to Data Mining (3)
  • Unit 2: Data Pre-processing (5)
  • Unit 3: Mining Frequent Patterns (4)
  • Unit 4: Classification and Prediction (5)
  • Unit 5: Cluster Analysis (4)
  • Unit 6: Advanced Data Mining Techniques (4)
  • Unit 7: Applications and Trends in Data Mining (3)

Topics covered

  • Introduction to Big Data and Data Mining
  • Challenges in Data Mining
  • Data Mining Goals and Tasks

  • Data Cleaning
  • Data Transformation
  • Data Reduction
  • Data Integration
  • Data Visualization

  • Association Rule Mining
  • Sequential Pattern Mining
  • Mining Time-series and Sequence Data
  • Statistical Measures in Pattern Mining

  • Decision Trees
  • Bayesian Classification
  • Rule-based Classification
  • Neural Networks
  • Regression Models

  • Introduction to Clustering
  • Types of Clustering: Hierarchical, Partitioning
  • Density-Based Clustering
  • Grid-Based Clustering

  • Web Mining
  • Text Mining
  • Social Network Analysis
  • Multimedia Data Mining

  • E-commerce, Bioinformatics, and Stock Market Predictions
  • Ethical Implications of Data Mining
  • Future Trends and Challenges

Grading:

  • Class Participation: 10%
  • Weekly Assignments: 30%
  • Data Mining Group Project: 20%
  • Mid-Term Examination: 20%
  • Final Examination: 20%

Reading Material: A mix of foundational textbooks and contemporary research articles on data mining. Extensive digital resources provided on the LMS.

Assignments: Diverse set of tasks including dataset handling, algorithm implementation, and results interpretation.

Faculty: An expert in data mining with substantial academic and industry experience.

Teaching Instructors: TBD

Contact Us

E-mail

arun.reddy@globuslearn.com

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