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BJ02A2000 Knowledge Discovery and Process Data Analysis, 5 cr 
Code BJ02A2000  Validity 01.08.2018 -
Name Knowledge Discovery and Process Data Analysis  Abbreviation Knowledge Disco 
Credits5 cr   
TypeBasic studies  
ClassCourse   
  Grading scaleStudy modules 0-5,P/F 
  Eligibility for post-graduate studiesYes
    Allowed to study several timesno
Organisation LUT School of Engineering Science 

Teachers
Name
Satu-Pia Reinikainen 
Tuomas Sihvonen 

Description by Study Guide
Note  Location: kokonaan verkossa / full digi

The course is suitable for distance learning.

 
Year  M.Sc. (Tech.) 1 
Period 
Teaching Language  English 
Teacher(s) in Charge 

Professor, D.Sc. (Tech.) Satu-Pia Reinikainen, M. Sc. Tuomas Sihvonen

 
Aims 

By the end of the course, the student is expected to
• Be aware of the effect of digitalization and automation on amount, nature, and quality of data from chemical engineering point of view
• have acquired a basic information of the main concept of knowledge discovery process concerning industrial data
• be able to apply specified methods and methodology on data
• be able to apply management and cooperation skills in implementation of project work.

 
Contents 

The knowledge discovery is referring to the overall process of discovering useful knowledge from data. The knowledge discovery process is interactive and iterative and involves several steps starting from studying the application domain and ending to use of the information discovered. Process data analysis can be part of this process. Fundamental concepts - such as reliability of data, preprocessing (e.g., de-noising, handling missing data, and scaling strategy), data reduction, choosing methodology, validation, modelling, etc - will be addressed in tutorials, Moodle assignments, and discussions. A project work will be carried out in small groups that will define their working methodology. The course is suitable for distance learning.

 
Teaching Methods 

Online tutorials 7 h, online discussions and peer feedback 7 h, Moodle exams 7 h, and assignments 40 h. Project work 20 h, online independent study 49 h. Total workload 130 h.

 
Suitability for doctoral studies (Yes/Leave empty)  Yes 
Examination in Examination schedule (Yes/No)  No 
Examination in Moodle (Yes/No)  No 
Examination in Exam (Yes/No)  No 
Assessment scale and assessment methods 

Numerical assessment (0-5), Project work 40 %, assignments 30 %, Moodle exams and peer feedback 30 %.

 
Course Materials 

Tutorial videos, online material distributed or announced in Moodle.

 
Prerequisites  Basic skills in Matlab programming and mathematics. 
Limitation for students? (Yes, number, priorities/Leave empty)  Yes, 50, Students in Chemical Engineering M.Sc. programme 
Places for exchange-students? (Yes, number/No)  max 10 
Places for Open University Students?(Yes, number/No)  max 10 


Current and upcoming courses
Functions Name Type cr Teacher Timing
  Knowledge Discovery and Process Data Analysis  COURSE  Satu-Pia Reinikainen,
Tuomas Sihvonen 
02.03.20 -17.04.20 -

Upcoming exams
No exams