EPN-V2

DATA3800 Introduction to Data Science with Scripting Course description

Course name in Norwegian
Introduction to Data Science with Scripting
Study programme
Bachelor in Applied Computer Technology
Bachelor's Degree Programme in Software Engineering
Bachelor's Degree Programme in Mathematical Modelling and Data Science
Bachelor's Degree Programme in Information Technology
Weight
10.0 ECTS
Year of study
2024/2025
Curriculum
FALL 2024
Schedule
Programme description
Course history

Introduction

On successful completion of the course, students should have the following learning outcomes defined in terms of knowledge, skills, and general competence.

Knowledge

The student:

  • has a deep understanding of complex systems modelling and analysis
  • has advanced knowledge in sub-symbolic and bio-inspired AI methods
  • has a clear understanding of key concepts in AI such as emergence, adaptation, evolution.

Skills

The student:

  • can model and analyse complex systems using cellular automata, networks and agent- based models
  • can program complex systems using bio-inspired AI methods
  • can design and implement evolutionary and swarm robotic systems

General Competence

The student:

  • has theoretical and practical understanding of complex and biologically-inspired AI methods and evolutionary robotics methods
  • can understand and discuss relevance, strength and limitations of complex and biologically inspired systems
  • is able to work in relevant research projects.

Recommended preliminary courses

Basic algebra, basic mathematical analysis and statistics are highly recommended, though a short overview on the fundamentals of these topics will be provided. The course will have a practical part using codes in python, Matlab or R. Acquaintance with these programming languages is not required, but some experience with a similar programming language is also recommended.

Learning outcomes

The course consists of lectures and seminars on techniques and methods, as well as a project to be carried out in groups (2-4 students). The project will be chosen from a portfolio of available problems. The students will work in groups and will submit the code and a project report. 

Practical training

Lab sessions.

Teaching and learning methods

None.

Course requirements

The assessment will be based on a portfolio of the following:

  • A group project delivery (2-4 students), consisting of a report (7500-3000 words) and code
  • An individual oral examination (20 minutes)

The weight of the two parts is 50% each.

The project report should be between 7500-3000 words. Both the code/program and the report will be evaluated. The comprehensiveness of the code/program is evaluated with the assumption that each student in the group has committed about 60 hours towards developing the solution. As a general guideline, the code/program carries a stronger weight than the report.

The portfolio will be assessed as a whole and the exam cannot be appealed.

 

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

Assessment

This is a portfolio exam that consists of a report based on the data analysis pipeline developed in the mandatory assignments, and its respective results.

The portfolio will consist of two parts; a report and a presentation:

  1. The report is a careful description of the work done during the semester. The report should contain a set of codes, graphs and notes, together with a sample of the dataset.
  2. 20 minutes maximum presentation of the content presented in the report within a coherent narrative and clarifying any obscure steps in the data processing, analysis, results or conclusions.

The portfolio will be assessed as a whole.

In case of a new or postponed examination, an alternative examination format may be used. Oral presentation can’t be appealed.

Permitted exam materials and equipment

All support materials are allowed for both the oral presentation and for the individual written summary.

Grading scale

Kazi Shah Nawaz Ripon

Examiners

Two examiners will be used, one of which can be external. External examiner is used regularly.

Overlapping courses

Overlapps 90% with STKD6060