Course content

1. Introduction to science 

- Historic philosophic concepts of science (Aristotelian heritage and its transformation over the centuries)

- Differences in science traditions

2. Research Ethics 

- Science, ethics and society

- Research ethics

- Perspectives, norms, objectivity and subjectivity

- Plagiarism and copying, referencing

3. Research methods 

- Constraint understanding for research planning/practical issues

- Frameworks of data collection within the different research methods

- Qualitative x Quantitative

- Applied statistical methods

4. Research Documentation and Communication 

- Literature surveys and annotated bibliography - (How to not re-invent the wheel)

- How to plan, organize and write a master thesis, conference paper, journal paper or a technical report.

- Structure of the thesis

- Language (scientific terminology) and References

Learning outcome

Learning outcome:

know the body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. 

knowledge of the differences between observable, empirical and measurable evidence.


basic skills in using a practical approach towards scientific investigations. T

be able to plan, conduct experiments and gather observable, empirical and measurable evidence (data) to support a hypothesis.

has practical understanding to create a common sense based experimental design.

is able to apply basic statistical methods to analyze and validate data (exploratory statistics up to the level of One-way Analysis of Variance (ANOVA)).

shall have basic skills and knowledge in validating data with regards to errors and other restrictions related to the experimental setup.

has gained the pre-requisite to take an advanced course in statistical experimental design (Statistical DOE).

be able to communicate his/her research through reports, scientific papers, and oral presentations. 


able to approach a research problem by applying practical approaches to scientific investigation, collection of data through observation and experimentation, and the formulation and testing of hypotheses. Most importantly understand the iterative nature of this process.

Learning methods and activities

Teaching methods and activities:
Lectures, In-class exercises, project work & regular homework.  
Mandatory assignment:
3 project works.

Compulsory assignments

• Mandatory assignments

Specific conditions

Recommended previous knowledge

Engineering BSc, Statistics for engineers