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Data Processing and Analysis in R
Study Course Description
Course Description Statuss:Approved
Course Description Version:5.00
Study Course Accepted:23.04.2024 11:43:25
| Study Course Information | |||||||||
| Course Code: | SL_034 | LQF level: | All Levels | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Pharmacy; Psychology; Nursing Science; Medicine; Political Science; Rehabilitation; Communication Science; Public Health; Dentistry | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Māris Munkevics | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Statistics Unit | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | 23 Kapselu street, 2nd floor, Riga, statistika rsu[pnkts]lv, +371 67060897 | ||||||||
| Study Course Planning | |||||||||
| Full-Time - Semester No.1 | |||||||||
| Lectures (count) | 0 | Lecture Length (academic hours) | 0 | Total Contact Hours of Lectures | 0 | ||||
| Classes (count) | 8 | Class Length (academic hours) | 4 | Total Contact Hours of Classes | 32 | ||||
| Total Contact Hours | 32 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Previous knowledge in data analysis is considered beneficial. | ||||||||
| 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. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction to language R and RStudio environment. | Classes | 1.00 | computer room | |||||
| 2 | Data distributions and their evaluation, descriptive statistics and hypothesis. | Classes | 1.00 | computer room | |||||
| 3 | Tables and figures. | Classes | 1.00 | computer room | |||||
| 4 | Parametric analysis for quantitative data. | Classes | 1.00 | computer room | |||||
| 5 | Nonparametric analysis for quantitative data. | Classes | 1.00 | computer room | |||||
| 6 | Categorical data analysis. | Classes | 1.00 | computer room | |||||
| 7 | Correlations and linear regressions. | Classes | 1.00 | computer room | |||||
| 8 | Exponential regressions. | Classes | 1.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | Every class will contain independent work – student individually prepares for them. Task solutions electronically submitable for evaluation. | ||||||||
| Assessment Criteria: | Submitted tasks will be graded and cumulatively form 50% of the final grade. Remaining 50% will be formed by grade in the final test. | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | Students enhance knowledge in the most commonly used data analysis methods. | ||||||||
| Skills: | Students acquire the skills to handle the open access data analysis tool R. | ||||||||
| Competencies: | By strengthening the basic knowledge of data analysis and communication with R, it is possible to implement advanced data analysis methods. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| 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. | ||||||||
