PRINCIPLES OF DATA MINING
Academic year2016/17
CourseCOMPUTER SCIENCE
Code335AA
Credits6
PeriodSemester 1
LanguageEnglish
| Modules | Area | Type | Hours | Teacher(s) |
| DATA MINING: FONDAMENTI | INF/01 | LEZIONI | 48 | |
Programma non disponibile nella lingua selezionata
Knowledge
… a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data. Hal Varian, Google’s chief economist, predicts that the job of statistician will become the “sexiest” around. Data, he explains, are widely available; what is scarce is the ability to extract wisdom from them.
Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.
The student develops basic capacities of data analytics and mining, and mastering the knowledge discovery process from various kinds of data.
Assessment criteria of knowledge
Methods:
- Final oral exam
- Final laboratory practical demonstration
- Laboratory report
Teaching methods
Delivery: face to face
Learning activities:
- attending lectures
- preparation of oral/written report
- individual study
- group work
Attendance: Advised
Teaching methods:
- Lectures
- Task-based learning/problem-based learning/inquiry-based learning
- project work
Syllabus
Fundamentals of the knowledge discovery process
Explorative Data Analysis
Visual analytics
Fundamental data mining techniques: patterns and rules, clustering, classification
thical issues of data mining, responsibility of the data scientist, and privacy
Bibliography
Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Addison Wesley, ISBN 0-321-32136-7, 2006
http://www-users.cs.umn.edu/~kumar/dmbook/index.php
See course wiki at http://didawiki.cli.di.unipi.it/doku.php/dm/start
Updated: 14/11/2016 17:27
