Computer-Intensive Statistics and Data Mining DS
Course, Master's level, 1MS043
Autumn 2023 Autumn 2023, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 31 October 2023–14 January 2024
- Language of instruction
- English
- Entry requirements
-
120 credits. Regression analysis or participation in Introduction to data science. 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.
- First tuition fee instalment: SEK 18,125
- Total tuition fee: SEK 18,125
- Application deadline
- 17 April 2023
- Application code
- UU-10517
Admitted or on the waiting list?
- Registration period
- 17 October 2023–13 November 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
- 31 October 2023–14 January 2024
- Language of instruction
- English
- Entry requirements
-
120 credits. Regression analysis or participation in Introduction to data science. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 17 October 2023–13 November 2023
- Information on registration
About the course
In this course, you will learn several statistical techniques recently developed to meet the latest years increasing computer capacity.
The course includes resampling techniques, bootstrap, EM algorithms, SIMEX methodology, Markov Chain Monte Carlo (MCMC) methods, random number generators, smoothing techniques as kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, and splines. The choice of smoothing parameters is presented. All methods are explained by applications and the use of statistical software.
Contact
- Study counselling
- studievagledare@math.uu.se
- +46 18 471 32 03, +46 18 471 32 00