DATA ANALYSIS
| Title | ΑΝΑΛΥΣΗ ΔΕΔΟΜΕΝΩΝ / DATA ANALYSIS |
| Code | 228 |
| Faculty | Engineering |
| School | Mechanical Engineering |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Sofia Panagiotidou |
| Common | Yes |
| Status | Active |
| Course ID | 600021718 |
Programme of Study: UPS of School of Mechanical Engineering
Registered students: 72
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Energy | Elective Courses belonging to the other | 8 | 4 | 5 |
| Design and Structures | Elective Courses belonging to the other | 8 | 4 | 5 |
| Industrial Management | Compulsory Course belonging to the selected specialization (Compulsory Specialization Course) | 8 | 4 | 5 |
| Academic Year | 2022 – 2023 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 4 |
| Class ID | 600216125
|
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600216125
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
A very good knoledge of probability theory and statistics is necessary.
Learning Outcomes
Upon successful completion of the course, students will be able to:
• graph and analyze data sets
• perform simple and multiple linear regression and apply variable selection methods in multiple regression models
• calculate confidence intervals and prediction intervals in regression problems
• apply sorting, classification and clustering methods
• validate the applied models
• apply special sampling methods
• perform all the aforementioned techniques using Python programming and choose the most appropriate one
• interpret the results and evaluate the performance of the applied methods using statistical tools
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 an interdisciplinary team
- Advance free, creative and causative thinking
Course Content (Syllabus)
The content of the course covers modern statistical methods of data processing and management. Specifically, the course focuses on statistical methods suitable for Big Data in both supervised and unsupervised environment, as well as on methods for evaluating the effectiveness of various statistical techniques and selecting the most appropriate one.
Briefly, the course analyzes the following:
• Linear and Non-linear Regression
• K-nearest neighbors
• Logistic regression
• Linear Discriminant Analysis
• Special regression methods
• Cross-Validation and Bootstrap
• Regression and Classification Trees
• Random Forests
• Bagging – Boosting
• K-means Clustering, Hierarchical Clustering
• Meyhods for the statistical analysis of the results
Keywords
Data Analysis, Supervised learning, Unsupervised learning
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
Description
Teaching and student evaluation using PC and specialized software (Python programming).
Online office hours via Zoom.
Announcements and general information about the course are available at eLearning
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 52 | 1.7 | ✓ | ||
| Reading Assigment | 30 | 1 | ✓ | ✓ | |
| Written assigments | 65 | 2.2 | ✓ | ||
| Exams | 3 | 0.1 | ✓ | ||
| Total | 150 | 5 |
Student Assessment
Description
The final grade M is a combination of the grades in the final written examination (T) and the average projects grade (E) as follows: Μ = (0,6)Τ + (0,4)Ε if Τ>4. In every other case Μ = Τ.
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Exam with Short Answer Questions (Summative)
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Formative)
- Oral Exams (Formative)
- Written Exam with Problem Solving (Summative)
Bibliography
Course Bibliography (Eudoxus)
[1] Εφαρμοσμένη Στατιστική και Στατιστική Μηχανική Μάθηση με χρήση των IBM SPSS Statistics, R Python, Μπερσίμης Σωτήριος, Μπάρτζης Γεώργιος, Παπαδάκης Γεώργιος, Σαχλάς Αθανάσιος, Εκδ. Τζιόλα, 2021.
[2] Επιστήμη Δεδομένων: Βασικές Αρχές και Εφαρμογές με Python, Grus Joel, Εκδ. Α. Παπασωτηρίου & ΣΙΑ Ι.Κ.Ε., 2020.
[3] Ανάλυση Δεδομένων με την R, Νικολάου Χριστόφορος, Εκδ. Δίσιγμα ΙΚΕ, 2019.
[4] Μηχανική Μάθηση, Κωνσταντίνος Διαμαντάρας, Δημήτρης Μπότσης, Εκδ. Κλειδάριθμος ΕΠΕ, 2019.
[5] Αναγνώριση Προτύπων και Μηχανική Μάθηση, C.M. Bishop, Εκδ. Γρηγόριος Χρυσοστόμου Φούντας, 2019.
[6] Στατιστική και Μηχανική Μάθηση με την R, Ιωαννίδης Δημήτριος- Αθανασιάδης Ιωάννης, Εκδ. Τζιόλα, 2017.
Additional bibliography for study
[1] An Introduction to Statistical Learning with applications in R, Second Edition, Gareth James, Daniela Witter, Trevor Hastie, Robert Tibshirani, Springer, 2021.
Last Update
18-07-2025
