## Data analysis and statistical methods

 Faculteit Science and Engineering Jaar 2021/22 Vakcode WMEE001-05 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 digital exam.
Uren per week variabel
Onderwijsvorm Hoorcollege (LC), Opdracht (ASM), Werkcollege (T)
(Lectures (25 h, computer lectures), Tutorials (16 h, Tutorial/Question hours), Assignment 1 (35 h, practice problems), Assignment 2 (30 h, data set project), Assignment 3 (20 h, reading assignment perusall) and Self study (14 h))
Toetsvorm Opdracht (AST), Schriftelijk tentamen (WE), Verslag (R)
(Written exam (60%; digital exam during which a data set must be analysed in R. Approximately 3 questions also containing sub-questions about interpretation of statistical outcomes), Report (30%, project report), Assignment 1 (0%, exercise problems; at least 50% 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 ,Dr. Y. Shan, PhD.
Verplichte literatuur
Titel Auteur ISBN Prijs
Jupyter Notebook exercises (not mandatory, but recommended) U. Dusek/Y. Shan
Lecture slides (mandatory)
U. Dusek
Book (chapters) (mandatory): Learning statistics with R: A tutorial for psychology students and other beginners; edition 6.0; Open textbook library. (This book will be provided via Perusal) Danielle Navarro University of New South Wales d.navarro@unsw.edu.au
Software (mandatory); R and R studio; version 1.0
Entreevoorwaarden Preknowledge: Bachelor in natural sciences or related field.The course unit assumes some basic prior knowledge about probability theory and data analysis.
1st-year 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 sub-questions about interpretation of statistical outcomes), Report (30%, project report), Assignment 1 (0%, exercise problems; at least 50% 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.
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
Opgenomen in
Opleiding Jaar Periode Type
MSc Energy and Environmental Sciences  ( Obligatory Courses) 1 semester I a verplicht