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LLEF10174U  Exploratory Data Analysis / Chemometrics Volume 2013/2014

Course information

LanguageEnglish
Credit7,5 ECTS
LevelFull Degree Master
Bachelor
Duration1 block
Placement
Block 1
Schedule
C
Course capacity60.
Continuing and further education
Study boardStudy Board of Food, Human Nutrition and Sports
Contracting department
  • Department of Food Science
Course responsible
  • Åsmund Rinnan (3-69697a486e77776c36737d366c73)
Saved on the 30-04-2013
Education
MSc Programme in Food Science and Technology
MSc Programme in Agriculture
MSc Programme in BIology- Biotechnology
Content
In industry and research huge amounts of physical, chemical, sensory and other quality measurements are produced on all sorts of materials, processes and products. Exploratory data analysis / chemometrics offers a tool for extracting the optimal information from these data sets through the use of modern software and computer technology.
The course will give a step-by-step theoretical introduction to exploratory data analysis / chemometrics supported by practical examples from food science, agro technology, medicine, pharmaceutical science etc.
Methods for exploratory analysis (Principal Component Analysis), multivariate calibration (Partial Least Squares) and basic data preprocessing are considered. Understanding and interpretation of the computed models is central. As is methods for outlier detection and model validation. Computer exercises and the project will be performed applying user-friendly software. A thorough introduction to the software will be given.
Learning Outcome

The course introduces basic chemometric methods (PCA and PLS) and their use on different kinds of multivariate data of relevance for research and development. Furthermore, the exploratory element in research and development is illustrated.

After completing the course the student should be able to:
Knowledge:
Describe chemometric methods for multivariate data analysis (exploration and regression)
Describe techniques for data pre-preprocessing
Describe techniques for outlier detection
Describe method validation principles
Describe methods for variable selection

Skills:
Apply theory on real life data analytical cases
Apply commercial software for data analysis
Report in writing a full data analysis of a given problem including all aspects presented under Knowledge.
Interpret multivariate models (both exploratory and regression)

Competences:
Discuss and respond to univariate versus multivariate data analytical methodology in problem solving in society

Literature

Textbook: Compendium

Notes, papers and other course material.

Teaching and learning methods
Lectures, guest lectures, cases, workshops and computer exercises will introduce the chemometric theory and the practical aspects of multivariate data analysis. In the project real data analytical problems are solved from a methodological perspective and the results are reported in written form. The project will mainly be based on data sets from the Spectroscopic and Chemometrics group, Quality & Technology, Department of Food Science.
Sign up
Through STADS self service
Exam (Written report and subsequent oral exam)
Credit7,5 ECTS
Type of assessment
Oral examination, 15 minutes under invigilation
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Exam registration requirementsHanding in written report
AidOnly certain aids allowed
You're allowed to bring your written report to the exam
Marking scale7-point grading scale
Censorship formNo external censorship
There will be one internal censor for the exam.
Criteria for exam assesment
The students knowledge in the curriculum of the course.
Workload
CategoryHours
Guidance7
Lectures24
Theory exercises45
Project work70
Preparation59
Exam1
Total206
Saved on the 30-04-2013