Data Mining
| Title | Εξόρυξη δεδομένων / Data Mining |
| Code | E4 |
| Faculty | Health Sciences |
| School | Medicine |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Andreas Symeonidis |
| Common | No |
| Status | Active |
| Course ID | 600018997 |
Programme of Study: PPS Medical Research Methodology
Registered students: 6
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Elective Courses | 1 | 1 | 4 |
| Academic Year | 2025 – 2026 |
| Class Period | Spring |
| Faculty Instructors |
|
| Instructors from Other Categories | |
| Weekly Hours | 4 |
| Total Hours | 96 |
| Class ID | 600290279
|
Course Type 2021
Specialization / Direction
Course Type 2016-2020
- Scientific Area
- Skills Development
Course Type 2011-2015
Knowledge Deepening / Consolidation
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600290279
- eLearning: https://elearning.auth.gr/course/view.php?id=12504
Language of Instruction
- English (Instruction, Examination)
Prerequisites
Required Courses
- C1 Introduction to Statistics
General Prerequisites
Students must be at least aware of basic statistics concepts and must be able to use related software tools. Also, they should be keen on learning the basic of Data Analytics.
Learning Outcomes
The course aims to introduce students to the knowledge discovery process with a focus on data mining/machine learning techniques. Within the context of the course the following concepts will be discussed and elaborated: data mining principles (data mining), supervised machine learning techniques (prediction and characterization - classification), unsupervised machine learning techniques (grouping and aggregation - clustering), anomaly detection (outlier analysis).
Knowledge
Upon successfully completing this course, students will be familiar with:
- Know the basic principles of data mining theory and the main application domains
- Understand the fundamental data mining techniques
- Apply well-known machine learning algorithms to health-related pilot problems
- Assess the performance of algorithms on a given problem and given requirements
- Formulate and answer data mining hypotheses
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Work in teams
- Work in an international context
- Work in an interdisciplinary team
- Generate new research ideas
Course Content (Syllabus)
- Introduction to Data Mining
- Classification basics and classification model evaluation
- Classification techniques: Decision Trees, Probabilistic classifiers, SVMs, Neural Networks, Model Ensembles
- Data preprocessing and dimensionality reduction.
- Clustering basics and clustering model evaluation
- Clustering techniques: Partitioning Clustering, Hierarchical Clustering, Density-based clustering.
Keywords
Data Mining, Classification, Clustering, Data Analytics, Supervised learning, Non-supervised learning
Educational Material Types
- Slide presentations
- Video lectures
- Interactive excersises
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Description
E-learning, a moodle-based system has been customized by the university IT team. It allows instructors to post announcements, communicate with students, upload lectures, exercises and their solutions, set up and run course projects, while it also offers self-assessment capabilities. E-learning also supports a Forum for coursework discussion. All quizzes and exams are implemented and executed through e-learning
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 30 | 1.2 | ✓ | ||
| Reading Assigment | 10 | 0.4 | ✓ | ||
| Tutorial | 15 | 0.6 | ✓ | ||
| Interactive Teaching in Information Center | 15 | 0.6 | ✓ | ||
| Exams | 60 | 2.4 | |||
| Other / Others | |||||
| Total | 130 | 5.2 |
Student Assessment
Description
The overall mark for this module is a weighted average which is based on:
- Three online quizes (expected online week-2, online week-3, online-week-4) (30% + 30% + 30%)
- Assessment based on comments submitted by each student in online discussion fora (10%)
Requirements for a pass: Score a mark of 5 or higher.
Student Assessment methods
- Written Exam with Multiple Choice Questions (Formative)
- Written Exam with Short Answer Questions (Summative)
- Written Exam with Problem Solving (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
1. Practical Machine Learning in R, K. Chatzidimitriou et al., Leanpub publising, 2017.
2. Introduction to Data mining, P. Tan, M. Steinbach & V. Kumar, Addison Wesley, 2005.
3. Data Mining; Concepts and Techniques, 2nd edition, J. Han and M. Kamber, Morgan Kaufmann, 2006.
Last Update
19-11-2023
