Data Mining
Course, Master's level, 1DL370
Autumn 2023 Autumn 2023, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 28 August 2023–30 October 2023
- Language of instruction
- English
- Entry requirements
-
120 credits of which 30 credits in mathematics and 45 credits in computer science and/or engineering, including Database Design I, Statistical Machine Learning and a second course in computer programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Selection
-
Higher education credits in science and engineering (maximum 240 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- Application fee: SEK 900
- First tuition fee instalment: SEK 18,125
- Total tuition fee: SEK 18,125
- Application deadline
- 17 April 2023
- Application code
- UU-11036
Admitted or on the waiting list?
- Registration period
- 28 July 2023–4 September 2023
- Information on registration
Autumn 2023 Autumn 2023, Uppsala, 50%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 28 August 2023–30 October 2023
- Language of instruction
- English
- Entry requirements
-
120 credits of which 30 credits in mathematics and 45 credits in computer science and/or engineering, including Database Design I, Statistical Machine Learning and a second course in computer programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 28 July 2023–4 September 2023
- Information on registration
About the course
Types of data (e.g. tables, texts, graphs) and their properties, association analysis (introduction and advanced methods), cluster analysis and validation, text and graph analysis, anomaly detection, and social and ethical aspects in the area of data mining. Selected advanced topics. The topics are treated both theoretically and practically through laboratory work where selected methods are implemented and tested on typical data sets.