DATA MINING AND ANALYTICS OF ECONOMIC DATA
| Title | ΕΞΟΡΥΞΗ ΔΕΔΟΜΕΝΩΝ ΚΑΙ ΑΝΑΛΥΤΙΚΗ ΟΙΚΟΝΟΜΙΚΩΝ ΔΕΔΟΜΕΝΩΝ / DATA MINING AND ANALYTICS OF ECONOMIC DATA |
| Code | ΕΔΥ03 |
| Faculty | Social and Economic Sciences |
| School | Economics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Athanasios Tsadiras |
| Common | No |
| Status | Active |
| Course ID | 600026517 |
Programme of Study: Data Science in Economics and Finance
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Compulsory Course | 1 | 1 | 6 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors | |
| Class ID | 600273581
|
Course Type 2021
Specialization / Direction
Course Type 2016-2020
- Scientific Area
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600273581
Learning Outcomes
With the successful completion of the course, students will have become familiar with a) the basic techniques Data Mining from economic data and b) Economic Data Analytics, using popular software (Data Visualization, Data Mining) so that they can respond to the invitations and current trends they will encounter in their later careers.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Generate new research ideas
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Τopics that will be presented in the course are the following: Financial and Business problems that are solved through Data Science and Data Mining. Analysis and Visualization of Economic Data using modern software such as Tableau, Qlik Sense, Power BI. Pre-processing data for analysis. Segmentation (decision trees), categorization (SVM, Neural Networks), clustering (hierarchical, KMean) and association rules (Apriori) using software (Knime freeware). Performance evaluation of Data Mining solutions. In the Tutoring / Laboratory Department of the course, students will study and get practical experience in the following popular software: Tableau, Knime, Qlik Sense, Power BI.
Keywords
Data Science, Business Analytics, Business Intelligence, Data Visualization, Data Mining
Educational Material Types
- Notes
- Slide presentations
- Multimedia
- 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
Apparent
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Laboratory Work | 61 | ✓ | |||
| Reading Assigment | 45 | ✓ | |||
| Written assigments | 15 | ✓ | |||
| Exams | 20 | ✓ | |||
| Total | 180 |
Student Assessment
Description
The evaluation of the students will be based:
a) 70% on the final written exams in English language.
b) 10% on an oral presentation during the semester, regarding a topic relevant to IT Trends in Digital Marketing
c) 20% on two written assigmnents based on laboratory exercises.
Student Assessment methods
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Summative)
- Performance / Staging (Formative, Summative)
- Written Exam with Problem Solving (Summative)
- Labortatory Assignment (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
1) Η Επιστήμη των Δεδομένων Για Επιχειρήσεις, Foster Provost, Tom Fawcett, εκδ. Κλειδάριθμος 2019.
2) Επιστήμη Δεδομένων: Βασικές Αρχές και Εφαρμογές με Python, 2η Έκδοση, Grus Joe, εκδ. Α. Παπασωτηρίου & Σια Ι.Κ.Ε. 2020.
Last Update
15-05-2025
