Course syllabus - Multivariate Data Analysis in Engineering

Objectives

The purpose of the course is to provide essential knowledge and understanding of principles of multivariate data analysis methods in engineering. The knowledge is applied to multivariate analysis, modeling, and interpretation of results related to different fields of engineering using data exploration, artificial intelligence (AI), and machine learning algorithms.

Learning outcomes

After completing the course, the student shall be able to:
1. Describe essential principles of multivariate data analysis methods including data exploration as well as artificial intelligence (AI) and machine learning algorithms
2. Construct an exploratory principal component analysis based on relevant data sets and interpret the results
3. Correctly apply different multivariate qualitative and quantitative methods to given datasets and analyze, evaluate, and interpret the results.
4. Evaluate the applicability of various multivariate data analysis methods and models in engineering fields.
5. Critically assess how different multivariate data analysis methods can contribute to sustainable development.

Course content

The course mainly addresses the following:
- Explanatory data analysis and interpretation
- Multivariate qualitative and quantitative data analysis and interpretation using artificial intelligence (AI) and machine learning algorithms
- Multivariate time-series data analysis and interpretation

Tuition

Web-based teaching and assignments

Specific requirements

120 credits of which 90 credits engineering and/or natural science, and 7.5 credits mathematics.
In addition Swedish course B/Swedish course 3 and English course B/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.

Examination

Home examination (HEM1), 1.5 credits, grades Excellent (A), Very good (B), Good (C), Satisfactory (D), Sufficient (E), Insufficient, complementary work possible (Fx), Insufficient (F), (Learning outcomes: 1, 4, 5)
Project (PRO1) 6 credits, grades Excellent (A), Very good (B), Good (C), Satisfactory (D), Sufficient (E), Insufficient, complementary work possible (Fx), Insufficient (F), (Learning outcomes: 2, 3, 4, 5)

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Grade

AF-skala