NDAB16012U Modelling and Analysis of Data (MAD)

Volume 2017/2018

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; to introduce the student to simple statistical analysis of experimental data and data visualization. This includes an introduction to multivariate calculus along with its applications in signal processing and data analysis. Examples will be taken from real-world problems, such as analysis of internet traffic, language technology, digital sound and image processing, etc. In addition, the course will provide an introduction to programming tools suitable for data analysis (MATLAB, Python or R).

The course will include the following:

  • Introduction to multivariate calculus, in particular partial derivatives and multivariate integration
  • Introduction to data processing, in particular convolution
  • Least squares methods, linear regression
  • Simple models for classification
  • Presentation and validation of machine learning results
  • Multivariate statistics, Principal Component Analysis
  • Presentation of analysis results, including visualization by simple plotting
  • Introduction to programming tools for data analysis
Learning Outcome

After the course the student should have the following knowledge, skills, and competences.


The student will have knowledge about multivariate calculus as well as data-analysis techniques including data-representation, filtering, modelling and estimation, and visualisation.

The student will be able to

  • Compute partial derivatives and gradients and use these for filtering, optimization and derivation of data analysis algorithms
  • Apply the least squares method for linear modeling and estimation.
  • Analyse sampled data by appropriate mathematical modelling methods
  • Describe certain useful multivariate methods and their use, especially principal component analysis (PCA) and its use in dimensionality reduction.
  • Visualize low- and high-dimensional data by simple plots and images.
  • Implement simple data analysis and modeling methods.
  • Perform the analysis of experimental data using the methods learnt during the course and evaluate the results.



  • The student will be capable of performing basic data analysis tasks which include modelling, visualisation, and interpretation of the results.
  • The student will be able to describe the limitations of the used methods.
  • The student will be able to apply calculus tools, such as partial derivatives, gradients, and integrals, in image processing, optimization, and analytical derivation of algorithms for data analysis.

See Absalon when the course is set up.

Mathematical knowledge equivalent to those obtained in the courses LinAlgDat, DMA, and MASD. or similar.
Lectures, excercises and mandatory assignments
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.
  • Category
  • Hours
  • Lectures
  • 32
  • Practical exercises
  • 72
  • Preparation
  • 30
  • Theory exercises
  • 72
  • Total
  • 206
7,5 ECTS
Type of assessment
Written assignment
Written assignment, due on the last day of the block. The students have 7 days to work on the exam.
Exam registration requirements

There are five to seven mandatory written take-home assignments (which may include programming tasks), all but one of which must be passed in order to be eligible for the exam.

All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Multiple internal examiners.

If the student is not qualified for exam participation, qualification can be achieved by hand-in and approval of equivalent written assignments.  Hand-in deadline is two weeks prior to the exam.

The exam form is 20 minutes oral exam without preparation and in course curriculum. No aids allowed.

Criteria for exam assesment

See Learning Outcome.