Class timing

Autumn Semester 2024
Wednesday: 12:00 - 12:55 PM | Thursday: 11:00 - 11:55 AM | Friday: 9:00 - 10:55 AM

Venue

NR411 (Nalanda Classroom Complex)

Reading materials

• Introduction to Data Mining (2nd Ed.) by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne and Vipin Kumar.
• Data Mining: The Textbook by Charu Aggarwal.
• Python for Data Analysis by Wes McKinney.
• Lecture slides and relevant online tutorials.

Tentative course content

1. Data Collection and Preprocessing
   • Data collection methods
   • Different types of data
   • Data quality and preprocessing techniques
   • Handling missing data, outliers, and noise
   • Data transformation and normalization
2. Exploratory Data Analysis
   • Descriptive statistics
   • Data visualization techniques
   • Identifying patterns and trends
   • Tools: Pandas, Matplotlib, Seaborn
3. Classification Techniques
   • Decision trees
   • k-Nearest Neighbors (k-NN)
   • Naive Bayes
   • Support Vector Machines (SVM)
4. Clustering Techniques
5. Association Rule Mining
6. Data Ethics and Privacy
   • Ethical considerations in data analytics
   • Data privacy
   • Regulations and compliance (GDPR, CCPA)

Pre-requisites for the course

• Data structures and algorithms.
• Working knowledge of AI/ML would be a plus.
• The assignments will require programming experience.

Course evaluation

• Mid-term and end-term exams: 35% + 40%
• Assignments: 20%
• Attendance and class participation: 5%