Data analysis and statistical methods
Faculteit  Science and Engineering 
Jaar  2020/21 
Vakcode  WMEE00105 
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. Use the programming language R to explore large data sets with various filtering and graphical methods. 2. Solve basic data analysis problems related to probability distributions, error analysis, hypothesis testing, and linear regressions in R. 3. Assess which techniques are appropriate for a certain problem in a realistic data set related to energy and environmental science. 4. Correctly interpret the outcome and explain limitations of statistical methods. 5. Discuss and explain statistical methods and underlying theory. 

Omschrijving  This course gives a very hands on introduction to data analysis and statistics using R. It is less focused on equations and derivation and more on practical exploration of the underlying statistical concepts and their application. The first two weeks are focused on learning R and exploratory data analysis methods using tidyverse packages. The rest of the course covers introductory statistical concepts: Content: Probability distributions and their properties, the central limit theorem, confidence intervals, basic hypothesis testing (means, variances, 1way ANOVA), experimental uncertainties and error propagation, linear regression Structure: 12 computer lectures: The lectures consist of short lecture segments followed by computer exercises to illustrate and try out the concepts explained. Bringing a laptop or tablet to the lecture is necessary to tale full advantage of them. Sections from the book will be assigned to prepare for the lectures. Computer exercises will be assigned to practise the material learned in the lectures. 8 tutorials/question hours: These are reserved for help on the exercises and the project. 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 exercises and the project will prepare the students for the online exam. 

Uren per week  variabel  
Onderwijsvorm 
Hoorcollege (LC), Opdracht (ASM), Werkcollege (T)
(Guest lecturer: dr. F. Ruzzenenti; Lectures (24 h, computer lectures), Tutorials (16 h, Tutorial/Question hours), Assignment 1 (35 h, practice problems), Assignment 2 (20 h, data set project), Assignment 3 (35 h, reading assignment perusall) and Self study (10 h)) 

Toetsvorm 
Opdracht (AST), Schriftelijk tentamen (WE), Verslag (R)
(Written exam (60%; Online exam during which a data set must be analysed in R. Approximately 3 questions also containing subquestions about interpretation of statistical outcomes), Report (30%, project report), Assignment 1 (0%, exercise problems; at least 60% of the problems must be completed for a pass of the course), Assignment 2 (10%, Perusall reading assignments).) 

Vaksoort  master  
Coördinator  prof. dr. U. Dusek  
Docent(en)  prof. dr. U. Dusek , Y. Shan, PhD.  
Verplichte literatuur 


Entreevoorwaarden  Preknowledge: Bachelor in natural sciences or related field.The course unit assumes some basic prior knowledge about probability theory and data analysis. 1styear master students from the EES master programme are participating in the course unit. The course unit is compulsory for the EES master programme. Maximum capacity: 60 students. Priority for students from the Master EES (Energy and Environmental Sciences) for whom this course is obligatory. The course unit prepares students for the research internship/research projects in which the learning objectives attained are required as prior knowledge. 

Opmerkingen  Modes of assessment and calculation final grade: Written exam (60%; Online exam during which a data set must be analysed in R. Approximately 3 questions also containing subquestions about interpretation of statistical outcomes), Report (30%, project report), Assignment 1 (0%, exercise problems; at least 60% of the problems must be completed for a pass of the course), Assignment 2 (10%, Perusall reading assignments). The total grade (60% exam, 30% project, 10% Perusall) must be higher than 5.5. At least 60% of the problems need to be completed to pass the course. There is no mandatory presence for the educational activities. In case the assessment has to take place online, the written (digital) exam will be adjusted to an online written (digital) exam from home. Second examiner for the course: dr. Y. Shan This course was registered last year with course code WMEE14000 

Opgenomen in 
