DATAWAREHOUSES AND DATA MINING
| Title | ΑΠΟΘΗΚΕΣ ΔΕΔΟΜΕΝΩΝ ΚΑΙ ΕΞΟΡΥΞΗ ΔΕΔΟΜΕΝΩΝ / DATAWAREHOUSES AND DATA MINING |
| Code | NIS-07-03 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | John Paparrizos |
| Common | No |
| Status | Active |
| Course ID | 40002983 |
Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)
Registered students: 1
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| GENIKĪ KATEUTHYNSĪ | YPOCΗREŌTIKO KATA EPILOGĪ | 6 | 3 | 5 |
| Academic Year | 2018 – 2019 |
| Class Period | Spring |
| Faculty Instructors | |
| Class ID | 600121332
|
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/1/600121332
- At the Website of the School: http://datalab.csd.auth.gr/~gounaris/courses/dwdm
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Examination)
Prerequisites
General Prerequisites
N/A
Learning Outcomes
Knowledge to be aquired on completion:
Methodologies for knowledge discovery in databases. Understanding of the main methodologies for classification, clustering, and association rules. Deeper study of database technologies and familiarization with data warehouses.
Skills:
Acquisition of skills in applying data warehouse and data mining techniques, and in using existing tools. Acquisition of skills in extractging knowledge from data and evaluating 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)
Introduction, data issues and data preprocessing, data warehouses, classification, clustering, association rules, outlier detection, applications and case studies, practical exercises in R/Spark/RapidMiner.
Keywords
Data Warehouses, Data Processing, Classification, Clustering, Association Rules
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)
"Εισαγωγή στην Εξόρυξη και τις Αποθήκες Δεδομένων" (Α.Νανόπουλος, Ι. Μανωλόπουλος).
Additional bibliography for study
- Margaret Dunham, Data Mining Introductory and Advanced Topics, ISBN: 0130888923, Prentice Hall, 2003
- Jiawei Han, Micheline Kamber, Data Mining : Concepts and Techniques, 3rd edition, Morgan Kaufmann, ISBN 978-0123814791, 2011
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Pearson Addison Wesley, 2006
- Mehmed Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, ISBN: 0471228524, Wiley-IEEE Press, 2002.
Last Update
02-12-2020
