NFOK24001U Data Handling and Analyses

Volume 2024/2025

MSc Programme in Biosolutions


Students will learn to design, evaluate and optimize sustainable processes using data science tools. The participants will be introduced to general principles of data handling, processing, visualization and analyses using statistical and mathematical modelling approaches. The theoretical principles will be studies using real world examples from experimental and industrial sensor data.

The course is structured in two parts: 4 weeks, covering generic topics to provide a basic understanding of data handling and analyses, and familiarize the participant with high-level data analyses programming tools such as R (Python, Matlab, TBD). The following 4 weeks will go in-depth with two topics: time-series (continuous data, process dynamics and on-line collected spectral data) and clustering plus classification of larger datasets (incl. biostatistics, machine learning and big data). The students will work both individually and in groups on real-life industry case(s) throughout the course.

Learning Outcome

The course will train students to properly handle, analyze and visualize diverse types of data encountered in biotechnology / novel biosolutions.



  • Data handling, management and quality assurance; data and signal transfer and organization (importing, format / structuring, traceability, documentation and file structures)
  • General principles and limitations of statistical data analyses and modeling
  • Basic concepts underlying regression, data clustering, visualization and process analyses



  • Formulate problems and testable hypotheses
  • Assess data quality
  • Data visualization
  • Select and carry out statistical analyses suitable for a given data structure
  • Use iterative process optimization for time series
  • Document, interpret and communicate results from data analyses
  • Implement basic programming tasks in the high-level statistical programming language R (Python, Matlab TBD)
  • Interpret, understand and modify data analysis R scripts written by third parties for tasks relevant to biosolutions.  



  • Use computational-statistical thinking to develop solutions to challenges in bio-based production
  • Use a data-driven strategy in the diverse aspects of biosolutions in both research and production
  • Assess data quality, and interpret and reflect upon data processing strategies, the results / findings and the underlying data quality

See Absalon for a list of course literatures. Source code and toolboxes for the statistical software is available via Absalon.

It is assumed that the student have competences corresponding to a course in basic statistics. Academic qualifications equivalent to a BSc degree are recommended.
The students will be introduced to the theory through lectures, and class-wide computer exercises. The students will work individually and in groups on a data analytical assignments using the taught concepts / theory and software to analyze a problem. The results are formulated in written assignment reports, 4 times during the course, evaluated without grading, by the teachers.
  • Category
  • Hours
  • Lectures
  • 40
  • Preparation
  • 75
  • Theory exercises
  • 40
  • Exercises
  • 50
  • Exam
  • 1
  • Total
  • 206
Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Oral exam on basis of previous submission, 20 minutes (no preparation)
Type of assessment details
At the individual, oral examination the students discuss questions asked by the censors from the curriculum / theory of the course plus the assignment / exercise work handed in during the course. During the examination the students are evaluated on the skills and understanding in data analysis, presentation of analytical results and data collection and preparation skill in statistical software.
It is not possible to participate in the exam if the assignment/exercise work has not been handed in during the course.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Same as the ordinary exam.

If the assignments / exercise work was not handed in during the course, these must be handed in at least 2 weeks before the re-exam.

Criteria for exam assesment

See Learning Outcome