- 20E-B2-2;Hold 01;;Modelling and Analysis of Data
- 20E-B2-2;Hold 02;;Modelling and Analysis of Data
- 20E-B2-2;Hold 03;;Modelling and Analysis of Data
- 20E-B2-2;Hold 04;;Modelling and Analysis of Data
- 20E-B2-2;Hold 05;;Modelling and Analysis of Data
- 20E-B2-2;Hold 06;;Modelling and Analysis of Data
- 20E-B2-2;Hold 07;;Modelling and Analysis of Data
NDAB16012U Modelling and Analysis of Data (MAD)
BSc Programme in Computer Science
BSc Programme in Physics
The purpose of the course is to provide a basic and broad introduction to the representation, analysis, and processing of sampled data. The course will introduce the student to statistical analysis, mathematical modelling, machine learning and visualisation for experimental data. Examples will be taken from real-world problems, such as analysis of internet traffic, language technology, digital sound and image processing, etc.
After the course, the student should have the following knowledge, skills, and competences.
Descriptive statistical methods
Likelihood functions and maximum likelihood estimation
Least-squares methods, linear regression
Simple models for classification
Presentation and validation of machine learning results
Presentation of analysis results, including visualisation by simple plotting
Introduction to programming tools for data analysis
The student will also become familiar with the analytical derivation of algorithms for data analysis
Apply the least-squares method for linear modelling and estimation.
Analyse sampled data by appropriate mathematical modelling methods.
Describe certain useful multivariate methods and their use
Visualise low- and high-dimensional data with simple plots and images.
Implement simple data analysis and modelling methods.
Perform the analysis of experimental data using the methods learnt during the course and evaluate the results.
Building and using simple statistical models, assessing their relevance for solving concrete scientific problems, and quantifying uncertainty about the drawn conclusions.
Performing basic data analysis tasks which include modelling, visualisation, and interpretation of the results.
Assessing the limitations of the used methods.
Applying calculus tools, such as partial derivatives, gradients, and integrals.
See Absalon when the course is set up.
- Theory exercises
- Practical exercises
There will be written feedback for the weekly assignments (comments via Absalon). For the final exam, the students can have individual oral feedback (there will be one feedback session that the students can attend).
- 7,5 ECTS
- Type of assessment
- Written assignment, 7 days---
- Exam registration requirements
4-6 mandatory individual assignments written during the course, which may include programming tasks. All but one of these must be passed in order to be qualified for the exam.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Multiple internal examiners.
The re-exam is a 20 minutes oral examination without preparation in the course curriculum. No aids allowed.
If the student is not qualified for the exam, qualification can be achieved by submitting and approval of equivalent written assignments or course assignments that has not previously been approved. The assignments must be submitted two weeks prior to the re-exam.
Criteria for exam assesment
See Learning Outcome.