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Data Mining

• Course name: Data Mining
• Course number: N/A

Course Characteristics

Key concepts of the class

• Data Preparation
• Association Pattern Mining
• Cluster Analysis
• Outlier Analysis
• Data Classification
• Mining Data Streams, Text Data and Discrete Sequences

What is the purpose of this course?

Data mining is the study of collecting, cleaning, processing, analyzing, and gaining useful insights from data. A wide variation exists in terms of the problem domains, applications, formulations, and data representations that are encountered in real applications. Therefore, “data mining” is a broad umbrella term that is used to describe these different aspects of data processing. This course aims to help students to correctly address large data volumes using advanced tools and techniques. This leads to unique challenges from the perspective of processing and analysis.

Course objectives based on Bloom’s taxonomy

- What should a student remember at the end of the course?

By the end of the course, the students should be able to

• The most common structures of distributed storage.
• Batch processing techniques
• Stream processing techniques
• Basic distributed data processing algorithms
• Basic tools to address specific processing needs

- What should a student be able to understand at the end of the course?

By the end of the course, the students should be able to

• 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

- What should a student be able to apply at the end of the course?

By the end of the course, the students should be able to

• Design an appropriate model to cope with new requirements
• 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

Course evaluation

Course grade breakdown

type	points
Labs/seminar classes	30
Interim performance assessment	30
Exams	40

Grades range

Course grading range

grade	low	high
A	90	100
B	75	89
C	60	74
D	0	59

Resources and reference material

• Jiawei Han, Micheline Kamber and Jian Pei. {\it Data Mining: Concepts and Techniques (3nd Edition)}
• Jure Leskovec, Anand Rajaraman and Jeffrey D. Ullman. {\it Mining of Massive Datasets}

Course Sections

The main sections of the course and approximate hour distribution between them is as follows: