Databases and Data Mining

Course description Databases and Data Mining
Year: 2017-2018
Catalog number: 4343DTBDM
Teacher(s):
  • dr. E.M. Bakker; erwin(at liacs.nl)
Language: English
Blackboard: No
EC: 6.0
Level: 500
Period: Semester 1
Hours of study: 26:00 hrs
  • Yes Elective choice
  • Yes Contractonderwijs
  • Yes Exchange
  • Yes Study Abroad
  • No Evening course
  • No A la Carte
  • No Honours Class

Admission requirements

C++, Databases

Description

The course Databases & Data Mining consists of a series of lectures in which advanced database and data mining techniques will be discussed, with applications to bioinformatics.

Course objectives

At the end of the course, students:

  • Should have a clear understanding of the current challenges and state of the art of databases and data mining.
  • Will have an understanding of the basic algorithms for preparing data and databases for data warehousing and data mining.
  • Will understand the basic data structures and organization that enable data analysis and data mining huge data sets.
  • Have an understanding of the important algorithms and challenges in several important emerging applications of data mining: mining biosequence databases, social networks, and graph mining.

Timetable

The most recent timetable can be found at the students' website

Mode of instruction

  • Lectures
  • Panel presentations and discussions.
  • Assignments
  • Reports

Assessment method

“There will be a total of 4 database- and data mining assignments and a final exam (open book). The assignments
P1 and P2 should both be passed with a ‘good’. Subsequently, the final grade will be based on a weighted average
of the grades obtained for assignments P3, P4 and the Exam (E >5): Final Grade = (P3 +2*P4 + 3*E)/6.”

Reading list

J. Han, M. Kamber, J. Pei. Data Mining Concepts and Techniques (3rd Edition), Morgan Kaufman Publishers, July 2011 (ISBN 978-0123814791)

Registration

You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and activity codes.

Contact information

Lecturer: dr. Erwin Bakker
Website: Databases and Datamining

Languages