Modelling and Analysis of Data (MAD)
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.
BSc Programme in Computer Science
BSc Programme in Physics
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.
Lectures, excercises and mandatory assignments.
See Absalon when the course is set up.
Basic knowledge of programming as obtained on PoP or similar. Skills in computational thinking as obtained on PoP, DMA, LinAlgDat, and MASD or similar. Mathematical knowledge equivalent to those obtained in the courses DMA, LinAlgDat, and MASD or similar.
The courses NDAB15001U Modelling and Analysis of Data (MAD) and NDAK16003U Introduction to Data Science (IDS) have a very substantial overlap both in topics and level, and it is therefore not recommended that students pass both of these courses.
There will be written feedback for the weekly assignments (comments via Absalon).
- 7,5 ECTS
- Type of assessment
Written assignment, 7 days
- Type of assessment details
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Multiple internal examiners.
Criteria for exam assessment
See Learning Outcome.
Single subject courses (day)
- Class Instruction
- Course number
- 7,5 ECTS
- Programme level
- Block 2
- No limit.
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Computer Science
- Faculty of Science
- Bulat Ibragimov (5-6a7d74697c486c7136737d366c73)
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Courseinformation of students