Course title in Estonian
Andmeanalüüsi lahendused R-keeles
Course title in English
Statistical Analysis Solutions in R
approximate amount of contact lessons
autumn - spring
lecturer of 2019/2020 Autumn semester
Jaagup Kippar (eesti keel) e-toega kursus
lecturer of 2019/2020 Spring semester
lecturer not assigned
Create premise for forming knowledge and skills necessary for finding suitable data analysis methods for presenting data and making conclusions.
Brief description of the course
Review of data analysis methods using case studies. R language areas of use, potentials and restrictions. Using R with other development tools and technologies. Examples of mathematical modelling. Encoding characteristics of real-world phenomenons into modellable form. Finding and checking relationships and dependencies. Interpreting calculation results in real environment while taking into account possible restrictions. Models in ecosystems, meteorology, traffic. Multidimentional scaling, usable equations. Syntax of R. Expressions, vectors, matrixes, standard statistical functions. Graphical presentation of results. Using add-on modules. File input and output. Iterations. Shaping data from web into suitable form for analysis. Using databases and XML-sources.
Reading and understanding cited sources, finishing assingments started in class. Individual assignments:
* Presenting common statistical relationships and drawing graphs using example data.
* Making statistical summaries from data gathered from SQL databases and XML sources,
* Creating an example of multidimentional scaling and presenting results.
* Introduction and analysis of model used in natural sciences.
* Creating and solving a modeling problem based on natural sciences.
* Creating and solving multi-level modelling problem as group assignment. Description of simplifications, creating algorithms with different level of accuracy to solve the same problem. Passing iterations and interpreting and presenting results.
* Active participation of revision seminar,
* Solving exam problem and explaining theoretical methods used.
Learning outcomes in the course
Studend knows potentials and restrictions of automated statistical analysis.
Student can plan and implement model describing real-world phenomenon and interpret its results.
Exam includes practical and theoretical parts.
The grade will be affected by the level of individual assignments and active participation in seminars.
The R Book ftp://ftp.tuebingen.mpg.de/pub/kyb/bresciani/Crawley%20-%20The%20R%20Book.pdf
Aines osalemiseks on soovitatavad eelteadmised aine IFI7041 "Andmeanalüüs: statistiline andmestik ja kirjeldav statistika" või IFI6201.DT " Teaduslik mõtteviis" mahus.
Kursuse asenduskirjanduse alusel läbimine lubatud vaid eraldi kokkuleppel õppejõuga.