NDAB16012U  Modelling and Analysis of Data (MAD)

Volume 2018/2019
Education

BSc Programme in Computer Science
BSc Programme in Physics

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 visualization 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. In addition, the course will provide an introduction to a programming tool suitable for data analysis (most likely one of the following: MATLAB, Python or R).

 

Learning Outcome

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

 

Knowledge

The student will have knowledge about statistical and data-analysis techniques including data-representation, filtering, modelling and estimation, and visualisation. This includes:

  • The central limit theorem

  • Descriptive statistical methods

  • Parameter estimation and confidence intervals

  • Hypothesis testing.

  • Likelihood functions and maximum likelihood estimation.

  • 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

 

Skills


The student will be able to:

  • 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.

  • Visualise 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 able to build and use simple statistical models, assess their relevance for solving concrete scientific problems, and quantify uncertainty about the drawn conclusions.

  • 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, optimisation, 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. Basic knowledge of programming.
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.

The course has a slightly changed curriculum compared to previous years.
Continuous feedback during the course of the semester
Credit
7,5 ECTS
Type of assessment
Written assignment, 7 days
7-day take-home-assignment, due on the last day of the block.
Exam registration requirements

Qualification for exam:

There are five to seven mandatory written assignments during the course, which may include programming tasks. All but one of these must be passed in order to be eligible for the exam.

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


If the student is not qualified for exam participation, qualification can be achieved by hand-in and approval of equivalent written assignments or course assignments that has not previously been approved.  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.

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