EXORYXĪ PLĪROFORIAS KAI ANALYTIKĪ OIKONOMIKŌN DEDOMENŌN
| Title | ΕΞΟΡΥΞΗ ΠΛΗΡΟΦΟΡΙΑΣ ΚΑΙ ΑΝΑΛΥΤΙΚΗ ΟΙΚΟΝΟΜΙΚΩΝ ΔΕΔΟΜΕΝΩΝ / EXORYXĪ PLĪROFORIAS KAI ANALYTIKĪ OIKONOMIKŌN DEDOMENŌN |
| Code | 12ΕΖ07 |
| Faculty | Social and Economic Sciences |
| School | Economics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Winter |
| Coordinator | Athanasios Tsadiras |
| Common | Yes |
| Status | Active |
| Course ID | 600000352 |
Programme of Study: UPS School of Economics (2013-today)
Registered students: 88
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| OIKONOMIAS | Elective Course belonging to the selected specialization (Elective Specialization Course) | 7 | 4 | 3 |
| DIOIKĪSĪ EPICΗEIRĪSEŌN | Elective Courses belonging to the other | 7 | 4 | 3 |
| Academic Year | 2023 – 2024 |
| Class Period | Winter |
| Faculty Instructors | |
| Class ID | 600241831
|
Class Schedule
| Building | Αμφιθέατρα Νομικής & Κοινωνικών και Οικονομικών Επιστημών |
| Floor | Όροφος 1 |
| Hall | ΑΙΘΟΥΣΑ 2 (160) |
| Calendar | Δευτέρα 16:00 έως 19:00 |
Course Type 2021
Skills Development
Course Type 2011-2015
Knowledge Deepening / Consolidation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600241831
- eLearning: https://elearning.auth.gr/course/view.php?id=5335
Learning Outcomes
With the successful completion of the course, students will have become familiar with a) the basic techniques of Machine Learning and Data Mining from economic data and b) Economic Data Analytics, using popular software (Data Visualization, Machine Learning) 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
- Work autonomously
- Work in teams
- Work in an interdisciplinary team
- Generate new research ideas
- 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 (free Weka software). 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, Weka, Qlik Sense, Power BI.
Keywords
Data Science, Business Analytics, Business Intelligence, Data Visualization, Data Mining, Machine Learning
Educational Material Types
- Slide presentations
- 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 | 69 | 2.5 | ✓ | ||
| Laboratory Work | 13 | 0.5 | ✓ | ||
| Exams | 2 | 0.1 | ✓ | ||
| Total | 84 | 3 |
Student Assessment
Description
written exams
Written Assignments
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
1)Εισαγωγή στη Μηχανική Μάθηση, Alpaydin Ethem, 2022 (προτεινόμενο)
2) Η Επιστήμη των Δεδομένων Για Επιχειρήσεις, Foster Provost, Tom Fawcett, εκδ. Κλειδάριθμος 2019.
3) Επιστήμη Δεδομένων: Βασικές Αρχές και Εφαρμογές με Python, 2η Έκδοση, Grus Joe, εκδ. Α. Παπασωτηρίου & Σια Ι.Κ.Ε. 2020.
4)Μηχανική Μάθηση, Κων/νος Διαμαντάρας, Δημήτρης Μπότσης, εκδ. Κλειδάριθμος 2019.
Last Update
12-12-2022
