# Modelling and Analysis of Data (MAD)

### Course content

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 measurements of internet traffic, language technology, digital sound and pictures, 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 (partial derivatives, integration)
* Introduction to data processing, and filtering
* Sampled data, sampling, frequency representation
* Least squares methods, linear regression
* Mathematical modelling
* Multivariate statistics, Principal Component Analysis
* Presentation of analysis results, including visualization by simple plotting
* Introduction to programming tools for data analysis

Education

BSc Programme in Computer Science
BSc Programme in Physics

Learning outcome

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

Knowledge:

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

Skills:

The student will be able to

* Compute partial derivatives and gradients and use these for both filtering, optimization and derivation of data analysis algorithms
* Choose an appropriate data representation, and transform between space/time and frequency domains, filter in both space/time- and frequency domains.
* Apply the least squares method for linear modeling and estimation.
* Analyze 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.

Competences:

The student will be capable of performing basic data-analysis tasks which include modelling, visualisation, and interpretation of the results and the describing limitations of the used methods. The student will be able to use calculus tools such as partial derivatives, gradients and integrals, both directly in image processing or optimization, but also analytically for deriving analytical algorithms for data analysis.

Lectures, excercises and mandatory assignments

See Absalon when the course is set up.

LinAlgDat, Datastrukturer og diskret matematik, MASD.

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 these courses.

ECTS
7,5 ECTS
Type of assessment
Continuous assessment
Continuous assessment of 4-8 assignments.
Aid
All aids allowed
Marking scale
Censorship form
No external censorship
Multiple internal examiners.
##### Criteria for exam assessment

See Learning Outcomes.

Single subject courses (day)

• Category
• Hours
• Lectures
• 32
• Theory exercises
• 72
• Practical exercises
• 72
• Preparation
• 30
• English
• 206