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Data Mining

Course name: Data Mining
Course number: XYZ
Knowledge area: Data Science

Administrative details

Faculty: Computer Science and Engineering
Year of instruction: 3rd year of BS
Semester of instruction: 2nd semester
No. of Credits: 4 ECTS
Total workload on average: 144 hours overall.
Class lecture hours: 2 per week.
Class tutorial hours: 0
Lab hours: 2 per week.
Individual lab hours: 2 per week
Frequency: weekly throughout the semester.
Grading mode: letters: A, B, C, D.

Prerequisites

Course outline

This course is designed for undergraduate students to provide core techniques of data processing and applications. Data Mining is an analytic process, which explores large data sets (also known as big data) to discover consistent patterns. This computational process involves a use of methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. This course will discuss advanced algorithms for classification, clustering, association analysis, and mining social network analysis. The subjects are treated both theoretically and practically through lab sessions.

Expected learning outcomes

Understand the entire chain of data processing
Understand principle theories, models, tools and techniques
Analyze and apply adequate models for new problems
Understand new data mining tasks and provide solutions in different domains
Design an appropriate model to cope with new requirements

Expected acquired core competences

Latest trends, algorithms, technologies in big data
Ability to determine appropriate approaches towards new challenges
Proficiency in data analysis and performance evaluations
Application of models, combination of multiple approaches, adaptation to interdisciplinary fields

Detailed topics covered in the course

Foundations of interaction design
Data Preprocessing
Data Warehouse
Association Rules
Frequent Pattern mining
Classification
Clustering
Recommendation Systems
Mining graphs
Mining data streams
Neural Networks
Outlier Detection
Dimensionality Deduction

Textbook

Jiawei Han, Micheline Kamber and Jian Pei. Data Mining: Concepts and Techniques (3nd Edition)

Reference material

Jure Leskovec, Anand Rajaraman and Jeffrey D. Ullman. Mining of Massive Datasets

Required computer resources

NA

Evaluation

Individual Assignments (30%)
Course Project (20%)
Mid-term Exam (20%)
Final Exam (30%)

BSc: Data Mining.previous version

Contents