# 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. 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.

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 of

• 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

• Multivariate statistics

• 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

Skills to

• 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.

Competences in

• 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.

Mathematical knowledge equivalent to those obtained in the courses LinAlgDat, DMA, and MASD or similar. Basic knowledge of programming.

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.

Written
Oral
Continuous feedback during the course of the semester

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).

ECTS
7,5 ECTS
Type of assessment
Written assignment, 7 days
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Aid
All aids allowed
Marking scale
Censorship form
No external censorship
Multiple internal examiners.
##### Criteria for exam assessment

See Learning Outcome.

Single subject courses (day)

• Category
• Hours
• Lectures
• 32
• Preparation
• 30
• Theory exercises
• 62
• Practical exercises
• 62
• Exam
• 20
• English
• 206