Data Analytics and Statistical Decision-making
| Structure Type: | Study unit |
|---|---|
| Code: | MA00BW52 |
| Curriculum: | TUTA 2026 |
| Level: | Bachelor of Engineering |
| Year of Study: | 2 (2027-2028) |
| Semester: | Spring |
| Credits: | 5 cr |
| Responsible Teacher: | Pyhälahti, Onni |
| Language of Instruction: | Finnish |
Learning Outcomes
After completing the course, the student has a good command of the basic concepts of statistics and the fundamental principles of probability theory. The student is able to apply regression analysis and understands the basic idea of cluster analysis. The student understands the role of percentage calculations as well as discounting and compound interest calculations in production economics applications. In addition, the student is able to utilize analysis tools related to the course topics in computer-based exercises.
Student's Workload
The total student workload of the course is 135 hours. This workload is divided between scheduled teaching and independent study. The timetable includes lectures, exercise sessions and computer-based exercises.
Prerequisites / Recommended Optional Courses
The course has no formal prerequisites. The contents are based on basic engineering mathematics studies, but students may participate in the course even if previous courses have not been completed.
Contents
The course covers basic statistical concepts such as means, standard deviation and variance. The probability distributions addressed in the course include the normal distribution, the binomial distribution and the Poisson distribution, as well as the basics of the chi-square distribution and the t-distribution. In addition, confidence intervals are covered.
In probability theory, the course covers the fundamental rules of probability, including Kolmogorov’s axioms and Bayes’ theorem. Discrete and continuous random variables are discussed, together with expected value and variance. In combinatorics, permutations, combinations and the binomial coefficient are covered.
The course includes regression analysis and a brief introduction to cluster analysis. In addition, percentage calculations as well as discounting and compound interest are covered. The course includes computer-based exercises related to the course topics.
Recommended or Required Reading and Other Learning Resources/Tools
Materials announced by the instructor.
Mode of Delivery / Planned Learning Activities and Teaching Methods
The main forms of instruction in the course are lectures and exercise sessions. In addition, the course includes computer-based exercises.
Assessment Criteria
Grade 1:
The student demonstrates knowledge of the course topics that are essential for further studies and working life.
Grade 3:
The student is able to make good use of the course topics.
Grade 5:
The student is able to apply the course topics creatively.
Assessment Methods
The instructor will announce the assessment methods during the first teaching session.