Data mining and numerical analysis
| Title | Εξόρυξη δεδομένων και αριθμητική ανάλυση / Data mining and numerical analysis |
| Code | ΕΔ1 |
| Faculty | Engineering |
| School | Chemical Engineering |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Nikolaos Passalis |
| Common | No |
| Status | Active |
| Course ID | 600023187 |
Programme of Study: MSc Chemical and Biochemical Engineering: Health & Food
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Compulsory Course | 1 | 1 | 7 |
| Academic Year | 2024 – 2025 |
| Class Period | Winter |
| Faculty Instructors | |
| Class ID | 600245278
|
Course Type 2021
General Foundation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600245278
- eLearning:
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Prerequisites
General Prerequisites
-
Learning Outcomes
Upon successful completion of the module, students will be able to:
- Process, analyse and visualize large complex datasets
- Apply advanced statistical techniques to infer correlations from large datasets
- Identify appropriate data mining and/or machine learning alogirthms depending on the characteristics of the dataset and posed query
- Implement and/or modify computational alogorithms for the analysis of complex datasets with advanced stastistical and/or data mining techniques
- Author, compile and execture computational algorithms for numerical and statistical analysis
- Simulate and perform numerical analysis for typical Chemical and Biochemical Engineering processes
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
- Work in an interdisciplinary team
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
1. Introduction to programming: syntax, basic commands, variables and functions, expressions and flow control, libraries
expressions, variables and values
2. Data Processing: qualitative assessment, visualisation, normalisation
3. Advanced Statistics: Hypothesis testing, types of regression, statistics on large data sets, Principal Component Analysis, Design of Experiments, ANOVA
4. Data Mining Techniques: Supervised and Unsupervised Learning, Dimensionality Reduction, Classification, Descision Trees, Clustering
5. Numerical Analysis: root finding, numerical solution of systems of ODEs, linear and
integer programming
6. Applications in Chemical and Biochemical Engineering processes
Keywords
Data mining, Numerical Analysis, Computational techniques, Data Processing
Educational Material Types
- Notes
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 21 | 0.7 | ✓ | ||
| Laboratory Work | 18 | 0.6 | ✓ | ✓ | |
| Reading Assigment | 128 | 4.3 | ✓ | ||
| Project | 40 | 1.3 | |||
| Exams | 3 | 0.1 | ✓ | ||
| Total | 210 | 7 |
Student Assessment
Description
Written examination 80%, project: 20%
Student Assessment methods
- Written Assignment (Summative)
- Written Exam with Problem Solving (Summative)
Last Update
03-06-2025
