Syllabus for Data Mining I
- 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:
- Revised: 2022-02-01
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2022
120 credits with 25 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. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Responsible department: Department of Information Technology
On completion of the course the student shall 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
- make judgments with regard to relevant scientific, social and ethical aspects in the application of data mining
- solve data mining problems in a team.
Introduction to data mining, its terminology and overview over various types of data (for example tables, text, graphs) and their properties, an overview of different methods to explore large amounts of data, data preprocessing (for example normalization, PCA), introduction to classification methods (for example k-NN, C4.5), introduction to clustering methods (for example k-means, single-link, DB-Scan, graph clustering algorithms), introduction to association analysis (for example Apriori), social and ethical aspects in the area of data mining, validation.
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 project. Guest lecture.
Written examination and a project that is presented orally and in writing.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
Applies from: Autumn 2022
Some titles may be available electronically through the University library.
Introduction to data mining
Second edition.: Harlow: Pearson Education, 2020
Ullman, Jeffrey D.
Mining of massive datasets
Third edition.: Cambridge, United Kingdom: Cambridge University Press, 2020