Veidlapa Nr. M-3 (8)
Study Course Description
Data Processing and Analysis in R
Course Code
SL_034
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
3.00
Target Audience
Communication Science; Dentistry; Medicine; Nursing Science; Pharmacy; Political Science; Psychology; Public Health; Rehabilitation
LQF
All Levels
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Māris Munkevics
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
23 Kapselu street, 2nd floor, Riga, statistika@rsu.lv, +371 67060897
About Study Course
Objective
To introduce participants with open access programm R, its approaches in data processing and visualisation as well as with the most commonly used statistical analysis. Students will receive experience that will mitigate individual learning of more advanced data analysis methods.
Preliminary Knowledge
Previous knowledge in data analysis is considered beneficial.
Learning Outcomes
Knowledge
1.Students enhance knowledge in the most commonly used data analysis methods.
Skills
1.Students acquire the skills to handle the open access data analysis tool R.
Competences
1.By strengthening the basic knowledge of data analysis and communication with R, it is possible to implement advanced data analysis methods.
Assessment
Individual work
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Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
Every class will contain independent work – student individually prepares for them. Task solutions electronically submitable for evaluation.
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||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
Submitted tasks will be graded and cumulatively form 50% of the final grade. Remaining 50% will be formed by grade in the final test.
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||
Study Course Theme Plan
FULL-TIME
Part 1
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Introduction to language R and RStudio environment.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Data distributions and their evaluation, descriptive statistics and hypothesis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Tables and figures.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Parametric analysis for quantitative data.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Nonparametric analysis for quantitative data.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Categorical data analysis.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Correlations and linear regressions.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
4
|
Topics
|
Exponential regressions.
Description
Annotation: Practical data analysis task and theorethical test in moodle environment
|
Total ECTS (Creditpoints):
3.00
Contact hours:
32 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Sokal, R.R. & Rohlf, F.J. 2009. Introduction to Biostatistics. Second edition.
2.
Dalgaard, P. 2008. Introductory Statistics with R. Second edition.
3.
Field, A., Miles, J., Field, Z. 2012. Discovering statistics using R.
Other Information Sources
1.
Elferts D., Praktiskā biometrija, 2016, elektroniskā grāmata.