Big data Analysis
| Title | Ανάλυση μεγάλων δεδομένων / Big data Analysis |
| Code | UAV07 |
| Faculty | Engineering |
| School | Electrical and Computer Engineering |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Andreas Symeonidis |
| Common | No |
| Status | Active |
| Course ID | 600024620 |
Programme of Study: DPMS Enaéria Aytónoma Systīmata
Registered students: 3
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 2 | 1 | 5 |
| Academic Year | 2025 – 2026 |
| Class Period | Spring |
| Faculty Instructors | |
| Instructors from Other Categories | |
| Class ID | 600293512
|
Course Type 2021
Specific Foundation
Course Type 2016-2020
- Scientific Area
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/1/600293512
Language of Instruction
- English (Instruction, Examination)
Learning Outcomes
Knowledge
Upon successfully completing this course, students will be familiar with:
• Big data challenges and advantages
• Popular big data domains
• Big data analysis tasks
• Machine learning techniques used for big data analytics problems
Capacities
The course participants upon completion will be able to:
• Query big data infrastructures
• Visualize and (pre)process big data collections
• Understand the fundamental machine learning techniques and algorithms
• Apply machine learning techniques (classification, clustering, regression analysis, outlier/deviation detection) to pilot problems
• Select the most efficient algorithm, based on problem requirements
• Design the methodology for big data analysis problems of medium complexity
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
Course Content (Syllabus)
1. Introduction to Big data analytics: Definitions Examples Application areas
2. Modeling big data – Big data management architectures
3. Data exploration/visualization
4. Data Preparation and Preprocessing
5. Machine learning techniques (Part 0): Model evaluation
6. Machine learning techniques (Part I): Classification, Overview Definitions Algorithms
7. Machine learning techniques (Part II): Clustering, Overview Definitions Algorithms
Keywords
Algorithms, machine learning techniques, Big Data Analytics
Educational Material Types
- Notes
- Slide presentations
- Video lectures
- 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
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 43 | 1.7 | ✓ | ||
| Reading Assigment | 19 | 0.8 | ✓ | ||
| Interactive Teaching in Information Center | ✓ | ||||
| Project | |||||
| Exams | 63 | 2.5 | ✓ | ||
| Total | 125 | 5 |
Student Assessment
Description
- Two online quizes (expected week-6, week-12) (40% + 40%)
- Assessment on one big data analysis problem to be submitted by each student at the end of the course (20%)
-- Report (1000 – 1500 words) discussing the hypothesis and the solution of the problem
-- R/Python scripts of the solution, including the experiments and the training models created.
- Assessment based on comments submitted by each student in online discussion fora (10%)
Student Assessment methods
- Written Exam with Multiple Choice Questions (Formative)
- Written Exam with Problem Solving (Formative, Summative)
- Report (Formative, Summative)
- Labortatory Assignment (Formative, Summative)
Bibliography
Additional bibliography for study
-Recommended Bibliography:
1. Introduction to Data mining, P. Tan, M. Steinbach & V. Kumar, Addison Wesley, 2005.
2. Data Mining; Concepts and Techniques, 2nd edition, J. Han and M. Kamber, Morgan Kaufmann, 2006.
3. Practical Machine Learning in R, 2nd edition, K. Chatzidimitriou et al., 2015.
Last Update
08-11-2024
