MAT785 Applied Statistics for Professional Practice (Autumn 2026)
- ECTS Credits:
- 7.5
- Responsible faculty:
- Avdeling for logistikk
- Campus:
- Molde
- Course Leader:
- Salman Saleem Virani
- Teaching language:
- English
- Study level:
- {code=HN, name=Second degree level}
- Duration:
- ½ year
Table of contents
Contents
Data is everywhere in professional life, and the ability to reason from it is increasingly what separates good decisions from guesswork. This course equips you with the statistical thinking and practical skills needed to engage confidently with data in your field, whether you encounter it in reports, research, or your own analytical work. The focus is on understanding and application, not mathematical abstraction. You will learn to ask the right questions of data, interpret results critically, and communicate findings in ways that are meaningful to colleagues and decision-makers. These are skills that transfer directly to the workplace, regardless of your professional background. A core part of the course is working hands-on with R, one of the most widely used tools for statistical analysis across business, health, engineering, and the social sciences. By the end of the course, you will understand the methods and be able to use them.
The course is connected to the following study programs
- Experience-based Master in Logistics
Recommended knowledge
Students should have basic digital skills and be willing to work with statistical software. No advanced programming experience is required, but students are expected to learn basic data handling and statistical analysis using software introduced in the course.
Reduction of Credits
This course’s contents overlap with the following courses. A reduction of credits will occur if one of these courses is taken in addition:
| Course | Reduction of Credits |
|---|---|
| LOG708 – Applied Statistics. | 7.5 |
The student's learning outcomes after passing the exam
Knowledge:
After completing the course, the student shall have:
- broad knowledge of key statistical concepts, including variation, probability, sampling, estimation, uncertainty, and statistical inference.
- knowledge of common statistical methods used in professional practice, including hypothesis testing, correlation, regression, analysis of variance, and categorical data analysis.
- an understanding of how statistical methods can support evidence-based decision-making in organizations.
- knowledge of data quality issues, measurement problems, bias, uncertainty, and limitations in statistical analysis.
- an understanding of ethical and responsible use of data and statistical results.
Skills:
After completing the course, the student shall be able to:
- prepare, describe, and visualize data for analysis.
- select appropriate statistical methods for practical problems.
- conduct statistical analyses using relevant software.
- interpret statistical results in relation to a practical problem or decision situation.
- evaluate the quality and limitations of statistical evidence.
- communicate statistical findings clearly in written and oral form to both specialist and non-specialist audiences.
General competence:
After completing the course, the student shall be able to:
- apply statistical reasoning to professional and organizational challenges.
- critically assess claims based on data and statistical analysis.
- use data analysis as a basis for informed decision-making.
- reflect on uncertainty, assumptions, and ethical issues in the use of statistics.
- contribute to analytical work in interdisciplinary professional settings.
Forms of teaching and learning
The course combines lecture recordings, synchronous online teaching sessions every second week, practical exercises, and case-based learning. Students are expected to study lecture recordings and other course materials before attending the synchronous sessions. These sessions will be used to discuss key concepts, clarify questions, work through examples, and connect statistical methods to practical and professional problems. Practical exercises and case-based assignments will be an important part of the course. Students will work with datasets and use the statistical software package R to conduct analyses, interpret results, and communicate findings. Active preparation and participation are expected throughout the course.
Coursework requirements - conditions for taking the exam
Completion of mandatory statistical exercises
Exam
- Home exam 1: 20%
- Home exam 2: 20%
- Applied project report and oral presentation: 60%
The applied project report and oral presentation should demonstrate the student’s ability to apply statistical methods to a practical problem, interpret the results, and communicate the findings professionally. The project should also demonstrate the student’s ability to use a statistical software package to conduct analyses and present results clearly.
- Grading scale: Letter grades A–F
Pensum
The current reading list for 2026 Autumn can be found in Leganto
Last updated from FS (Felles studentsystem)
Oct. 14, 2026 11:18:20 PM