PRINCIPLES OF DATA AND WEB SCIENCE
| Title | ΑΡΧΕΣ ΕΠΙΣΤΗΜΗΣ ΔΕΔΟΜΕΝΩΝ ΚΑΙ ΙΣΤΟΥ / PRINCIPLES OF DATA AND WEB SCIENCE |
| Code | NIS-08-06 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | Anastasios Gounaris |
| Common | No |
| Status | Active |
| Course ID | 600020392 |
Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)
Registered students: 93
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| GENIKĪ KATEUTHYNSĪ | YPOCΗREŌTIKO KATA EPILOGĪ | 8 | 4 | 5 |
| Academic Year | 2024 – 2025 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600259883
|
Course Type 2021
Specialization / Direction
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600259883
- eLearning:
Language of Instruction
- Greek (Instruction, Examination)
- English (Examination)
Prerequisites
General Prerequisites
N/A
Learning Outcomes
Cognitive:
Fundamental concepts and Μethodologies for Data and Web Science. Deep understanding of issues regarding data access, data protection and ethics during data usage/processing. Familiarisation with applications of Data Science and network analysis.
Skills:
Acquisition of skills in applying data science techniques, and in using existing tools. Acquisition of skills in accessing data, extracting knowledge and visualizing the results.
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
- Advance free, creative and causative thinking
Course Content (Syllabus)
Knowledge of the fundamentals of Data and Web Sciences is particularly important for every graduate of the Department of Informatics, given that at any subsequent stage of his/her career he/she will be required to manage diverse and complex data especially on the World Wide Web. Nowadays, the new types, volume, speed, authenticity and protection of data require skills for high-quality knowledge extraction, summarization of essential information, finding patterns, discovering communities, identifying trends and phenomena, etc., so as to give impetus to quality understanding, interpretation, and analysis of data. Understanding data and spotting trends is essential for decision making in many different areas. In this light, the proposed course will emphasize the utilization of existing information but also the utilization of emerging data (eg, from social networks). The course is aimed at undergraduate students who already have familiarity with database structures and databases, algorithms, data mining, and programming skills. For example, the course will focus on the following topics:
- Data and metadata as valuable information entities
- Data Types (Structured, Semi-Structured, Unstructured, Web-Evolving)
- Data access, collection, preparation, and modeling (APIs, Open Data sources)
- Exploration and detection of data relevance (application of basic data mining algorithms, community finding techniques)
- Data visualization, recognition and interpretation of phenomena
– Studying examples in multiple domains (business process data, flows, evolving web data, graph data) by creating open datasets (creating APIs)
- Data protection issues and ethics
- Data and Web Science Challenges and Open Issues
Keywords
Data, Metada, Data Source API, Data Science API, Data Science Applications, Data protection
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
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 48 | ✓ | |||
| Laboratory Work | 4 | ✓ | |||
| Project | 50 | ✓ | |||
| Written assigments | 48 | ✓ | |||
| Total | 150 |
Student Assessment
Description
Written exams, and projects. The exact procedure and weightning is announced on the course's website.
Student Assessment methods
- Written Exam with Short Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Performance / Staging (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
Βασίλειος Σ. Βερύκιος, Σωτήριος Β. Κωτσιαντής, Ηλίας Κ. Σταυρόπουλος, Μανώλης Μ. Τζαγκαράκης : Η Επιστήμη των Δεδομένων – Βασικές Αρχές, Θεωρία & Εφαρμογές με τη Γλώσσα R, (Εύδοξος 77120638), Εκδόσεις Νέων Τεχνολογιών, 2017.
Βασίλειος Σ. Βερύκιος (μετάφραση): Η Επιστήμη των Δεδομένων για Επιχειρήσεις, Κλειδάριθμος, 2019.
Grus, Joel. Data science from scratch: first principles with python. O'Reilly Media, 2019.
Jin, Brenda, Saurabh Sahni, and Amir Shevat. Designing Web APIs: Building APIs That Developers Love. " O'Reilly Media, Inc.", 2018.
Leek, Jeff. "The elements of data analytic style." J. Leek.—Amazon Digital Services, Inc (2015).
Munzner, Tamara. Visualization analysis and design. CRC press, 2014.
Peng, Roger D., and Elizabeth Matsui. The Art of Data Science: A guide for anyone who works with Data. Skybrude consulting LLC, 2016.
Wilke, Claus O. Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media, 2019
Weske Mathias, Μάρω Βλαχοπούλου, Κωνσταντίνος Βεργίδης, Διαχείριση Επιχειρησιακών Διαδικασιών, (ISBN: 9789604187942), 2η Έκδοση, Κωδικός Βιβλίου στον Εύδοξο: 77106790
Additional bibliography for study
Wil M. P. van der Aalst: Process Mining - Data Science in Action, Second Edition. Springer 2016, ISBN 978-3-662-49850-7, pp. 3-452
Last Update
07-01-2023
