Experimental Data Analysis and Modeling

2021/2022

Prerequisite/Recommended prerequisite for participation in the module

The module builds on the project modules in the 1st - 4th semester.

Content, progress and pedagogy of the module

Disclaimer.
This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.

PURPOSE
The overall purpose of the project module is for the student to acquire the ability to analyze and evaluate the application of methods and techniques within database systems and / or machine intelligence to solve a specific problem. This involves an experimental analysis of the properties of the techniques as well as an experimental evaluation of the results obtained

REASON
Data representation, data analysis, and the ability to draw intelligent conclusions based on users' wishes and needs are included as central components in many modern IT systems. Within this project module, data representation and analysis covers the use of database management systems to model and store data in relation to data analysis, transform data into the desired format and be able to extract information from it using analytical queries. Intelligent systems are related to machine intelligence, where the term covers, for example, graphic models, data mining / machine learning, and autonomous agents. The following references to database systems and machine intelligence must thus be seen in this context

In this project module, the project work is primarily driven by empirical evaluations of the techniques / methods used as well as by the general software solution that may be developed through the project work. This may, for example, involve an iterative experimental approach to method development, which may require an essential element of software development, experiment design, and considerations about the statistical significance of the empirical results (such as runtimes, space consumption, and other method-specific properties).

Learning objectives

Knowledge

  • use correct concepts (in both writing and speech) notations and symbols.
  • demonstrate knowledge and overview of basic techniques in database systems or machine intelligence.
  • demonstrate knowledge of relevant methods for model evaluation

Skills

  • explain the application of relevant and central techniques within database systems or machine intelligence in relation to a selected problem area
  • interpret, communicate and visualize the results of empirical model and data analyzes

Competences

  • assess and justify the choice of relevant techniques and methods within database systems or machine intelligence for solving a current problem area
  • apply concepts and techniques within database systems or machine intelligence to solve a selected problem
  • be able to carry out an empirical evaluation of a relevant model / technique, as well as assess the validity and the statistical significance of the collected empirical results
  • apply project management

Type of instruction

Project work

Extent and expected workload

The student is expected to spend 27.5 hours per ECTS, which for this activity means 412.5 hours.

Exam

Exams

Name of examExperimental Data Analysis and Modeling
Type of exam
Oral exam based on a project
ECTS15
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Additional information

Contact: Study Board for Computer Science via cs-sn@cs.aau.dk or 9940 8854

Facts about the module

Danish titleEksperimentel dataanalyse og modellering
Module codeDSNDATB520
Module typeProject
Duration1 semester
SemesterAutumn
ECTS15
Language of instructionDanish and English
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module

Organisation

Study BoardStudy Board of Computer Science
DepartmentDepartment of Computer Science
FacultyTechnical Faculty of IT and Design