Syllabus for Data Mining I
A revised version of the syllabus is available.
- 5 credits
- Course code: 1DL360
- Education cycle: Second cycle
Main field(s) of study and in-depth level:
Computer Science A1N,
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2010-03-18
- Established by: The Faculty Board of Science and Technology
- Revised: 2015-04-29
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2015
120 credits with 30 credits in mathematics, including mathematical statistics, 45 credits in computer science and/or engineering, including Database Design I and a second course in computer programming. Algorithms and Data Structures I is recommended.
- Responsible department: Department of Information Technology
To pass, the student should be able to:
- explain different methods to extract processed information from large amounts of data, both in theory and in practical application
- use these methods with appropriate tools
- evaluate and compare the suitability of different methods.
Introduction to data mining, its terminology and overview over various types of data and its properties, an overview of different methods to explore and visualise large amounts of data, introduction to classification methods, introduction to clustering methods, introduction to association analysis, handling of personal integrity in the area of data mining.
The subjects are treated both theoretically and practically through laboratory sessions where selected methods are implemented and tested on typical amounts of data.
Lectures, seminars, laboratory sessions and written assignments. Guest lecture.
Written examination (3 HE credits) and assignments that are presented orally and written (2 HE credits).
Applies from: Autumn 2015
Some titles may be available electronically through the University library.
Introduction to Data Mining
1st or international edition: Addison-Wesley, 2006