BSc:DataMining.S22.previous version

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

• CSE201 — Mathematical Analysis I
• CSE201 — Mathematical Analysis II
• CSE202 — Analytical Geometry and Linear Algebra I
• CSE204 — Analytic Geometry And Linear Algebra II
• CSE206 — Probability And Statistics
• CSE117 — Data Structures and Algorithms
• CSE113 — Philosophy I - (Discrete Math and Logic)

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%)