Data analysis and statistical methods
Faculteit  Science and Engineering 
Jaar  2019/20 
Vakcode  WMEE14000 
Vaknaam  Data analysis and statistical methods 
Niveau(s)  master 
Voertaal  Engels 
Periode  semester I a 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Data analysis and statistical methods  
Leerdoelen  At the end of the course, the student is able to: 1. Solve basic data analysis problems relating to probability, distributions, error analysis, hypothesis testing, and linear regressions. 2. Explain simpler and more complex statistical methods, such as time series analysis. 3. Apply these techniques to analyze realistic data sets and to assess which techniques are appropriate for a certain problem. 4. Examine the theory behind the statistical methods, which helps to evaluate usefulness and limits of statistical methods depending on context. 5. Use the knowledge gained in this course to investigate and correctly apply new statistical methods that might be encountered during the master research project (not specifically tested at the end) 

Omschrijving  Content: Probability, distributions commonly encountered in environmental science and their properties, estimation of means and other parameters, hypothesis testing, experimental uncertainties and error propagation, linear regression and fitting, introduction to time series analysis Structure: 2 x 2 hour lectures / week: The lectures are focused on understanding the theoretical background of a statistical technique and the illustration of the technique with the help of simple examples. The tutorials will be used to discuss the assigned problem sets (Approximately 6 problem sets will be assigned). 1 x 2 hour computer practicum/week: Application of statistical techniques using the statistical programming language R. During the course the students will work on individual projects, where every student analyzes a real data set using methods learned in this course and write a small report. The projects are designed to prepare the students for the digital exam. 

Uren per week  variabel  
Onderwijsvorm 
Hoorcollege (LC), Opdracht (ASM), Practisch werk (PRC)
(Lectures (22 h), assignment 1 (problem sets) (42 h), assignment 2 (data set projects) (15 h), practical work: computer practicum (16 h)) 

Toetsvorm 
Opdracht (AST), Schriftelijk tentamen (WE), Verslag (R)
(Written exam (digital exam) 40%, Report (project report) 30%, Assignment (problem sets) 30% of final grade..) 

Vaksoort  master  
Coördinator  U. Dusek, PhD.  
Docent(en)  U. Dusek, PhD. ,dr. K.N. Hoefnagel  
Verplichte literatuur 


Entreevoorwaarden  Prior knowledge: The course unit assumes some basic prior knowledge about probabilities and probability distributions. If necessary this knowledge can be gained by reading the mandatory book. Followed by: 1styear master students from the EES master programme are participating in the course unit. The course unit is compulsory for the EES master programme. Preparation for: The course unit is often followed by, or prepares students for the training thesis and master thesis in which the learning objectives attained are required as prior knowledge. 

Opmerkingen  Modes of assessment and calculation final grade: Written Exam (digital exam) (40%): must be passed to pass the course, if the exam grade is lower than 5.5 the maximum course grade is a 5: approx 100 point total, divided over approx. 810 individual questions. Report: Project report (30%) Assignment (30%): Problem sets: Each problem set 10 points. Calculation final grade: The exam must be passed to pass the course. Final grade: if exam points < =55 > Gr = fail. if exam points > 55, Gr = sum(problem set points)/number of problem sets*0.3 + exam grade *0.4 + project grade*0.3 Presence during (parts of) the course is mandatory: No. 

Opgenomen in 
