3 ECTS credits
75 h study time

Offer 2 with catalog number 4023866ENW for working students in the 1st semester at a (E) Master - advanced level.

Semester
1st semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Taught in
English
Faculty
Faculty of Sciences and Bioengineering Sciences
Department
Computer Science
Educational team
Bart De Boer (course titular)
Activities and contact hours
26 contact hours Lecture
Course Content

Refresh the basics of scientific research methods: References, experimental design, statistics, writing, reviewing.

The statistical techniques that will be treated are in principle those that are needed to analyze the experimental designs that will be treated.

Course material
Digital course material (Required) : Slides to be distributed during the course
Additional info

Slides may change during the course, as they will be updated depending on course progress. General materials will also be linked on the canvas site.
The slides will serve as the syllabus.

It is strongly recommended that students attend the lectures and take notes. Workload for master's courses is higher than for Bachelor's courses, and students are
expected to make do with less supervision than at the Bachelor's level.

Also note that although this course discusses scientific referencing, students must already know the definition of and the sanctions imposed on plagiarism as stated in the "Onderwijs en Examenreglement".

Learning Outcomes

General competences

Understanding of the scientifc method and the process of doing research.

Ability to work with precision, and according to international scientific standards.

Writing skills

Ability to refer to sources correctly.

Ability to write scientifically, especially to clearly specify a research question and a research method.

Analysis skills

Ability to apply statistic analysis and hypothesis testing independently.

Ability to use linear models and Monte-Carlo statistical methods and to implement these independently.

Reading and understanding

Ability to write a fair and insightful review of a scientific paper in a domain that is not exactlty the student's area of specialization.

Grading

The final grade is composed based on the following categories:
Written Exam determines 40% of the final mark.
LEC Practical Assignment determines 60% of the final mark.

Within the Written Exam category, the following assignments need to be completed:

  • Final Exam with a relative weight of 40 which comprises 40% of the final mark.

    Note: Written final exam covering all material of the course, including the material covered by the assignments

Within the LEC Practical Assignment category, the following assignments need to be completed:

  • Paper review with a relative weight of 30 which comprises 30% of the final mark. This is a mid-term test.

    Note: 500 word literature review, 500 word proposal/experimental design, 10 references
  • Statistics excercise with a relative weight of 30 which comprises 30% of the final mark. This is a mid-term test.

    Note: 4 statistics excercises

Additional info regarding evaluation

Student performance is evaluated through 2 practical assignments and a written exam.

A Literature review and research proposal (500 words review, 500 words proposal/experimental design, 10 references)
A statistics excercise (4 excercises)

Students can be asked to defend their assignments orally.

Students must score a passing grade on their exam and on the weighted average of the exam score (40%) and the assignment scores (2x30%). Students can resit the exam in case one of these is below the pass mark, but not the assignments.

Allowed unsatisfactory mark
The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context

This offer is part of the following study plans:
Master of Applied Sciences and Engineering: Computer Science: Artificial Intelligence
Master of Applied Sciences and Engineering: Computer Science: Multimedia
Master of Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering
Master of Applied Sciences and Engineering: Computer Science: Multimedia for Northwestern Polytechnical University (NPU)
Master of Applied Sciences and Engineering: Computer Science: Data Management and Analytics
Master of Applied Informatics: Profile Profile Big Data Technology
Master of Applied Informatics: Profile Profile Artificial Intelligence