Data Analysis and Programming In R
Public syllabus for 2025-2026
Academic overview
Teaching team
Learning time distribution
| Total | ||||||
|---|---|---|---|---|---|---|
| Curriculum | Lecture | Practice | Total Weekly | Lecture | Practice | |
| 42 | 28 | 14 | 3 | 2 | 1 | |
| Exam hours | ||||||
| 3 | ||||||
| Individual Study | Bibliography study | Field study | Homework | Tutoring | Others | |
| 108 | 36 | 23 | 32 | 14 | 0 | |
| Overall | ||||||
| 150 |
Learning outcomes
Knowledge
- Knowledge of basic statistics and regression models
Skills
- Modeling skills for specific phenomena and processes in the domains: economic, technic, medical, social etc., by using fundamental mathematical, statistical, and computer science knowledge;
Responsibility
- Developing and testing a regression model
Online platform
Course content
| Content | Methods | Obs |
|---|---|---|
| C1. General facts about R. Objects and attributes | Lecturing, conversation, demonstration | Resources: [1], Ch. 1 |
| C2. Lists, matrices and dataframes | Lecturing, conversation, demonstration | Resources: [1], Ch. 1 |
| C3. Elements of programming in R | Lecturing, conversation, demonstration | Resources: [4], Ch. 4 |
| C4. Probability distributions. Generating random numbers following a particular probability distribution | Lecturing, conversation, demonstration | Resources: [4], Ch. 5 |
| C5. Elements of descriptive statistics in R | Lecturing, conversation, demonstration | Resources: [1], Ch. 2, [2], Ch. 2,3 |
| C6. Tools for visualization. The ggplot2 package. Multivariate exploratory analysis | Lecturing, conversation, demonstration | Resources: [1], Ch. 2, [2], Ch. 2,3 |
| C7-8. Tools for estimation and statistical hypothesis testing | Lecturing, conversation, demonstration | Resources: [1], Ch. 4, [2], Ch. 6 |
| C9-10. Predicting continuous responses: correlation and linear regression | Lecturing, conversation, demonstration | Resources: [1], Ch. 5 |
| C11-12. Predicting quantitative responses | Lecturing, conversation, demonstration | Resources: [2], Ch. 9, [3], Ch. 7-11 |
| C13-14. Case studies | Lecturing, conversation, demonstration | Resources: [3], Ch. 20 |
Course bibliography
J. Maindonald, W. J. Braun, Data Analysis and Graphics using R – An Example – based Approach, 3rd ed., Cambridge University Press, 2003 2. T. Fischetti, Data Analysis with R, Packt Publishing, 2015 3. J. Ledolter, Data Mining and Business Analytics with R, Wiley, 2013 4. W. J. Braun, D. J. Murdoch, A First Course in Statistical Programming with R, Cambridge University Press, 2007 5. J. M. Chambers, Software for Data Analysis. Programming with R, Springer, 2008
Seminar content
| Content | Methods | Obs |
|---|---|---|
| 1. Introduction to R. Objects and attributes. Dataframes. Packages | Dialogue with students, cooperative learning, modeling, case studies | |
| 2. Programming in R | Dialogue with students, cooperative learning, modeling, case studies | |
| 3. Estimating probability by simulation | Dialogue with students, cooperative learning, modeling, case studies | |
| 4. Tools for descriptive statistics | Dialogue with students, cooperative learning, modeling, case studies | |
| 5. Testing statistical hypotheses with R | Dialogue with students, cooperative learning, modeling, case studies | |
| 6. Univariate and multivariate linear regression | Dialogue with students, cooperative learning, modeling, case studies | |
| 7. Logistic regression models | Dialogue with students, cooperative learning, modeling, case studies | |
| Bibliography: Same as for the lecture |
Seminar bibliography
The course is consistent with similar ones from representative universities and covers the most important aspects regarding data analysis. The concepts are presented using the open source software R, currently one of the most widely used tools for data analysis, in teaching, research, as well as practical applications. The skills acquired are necessary for a person working in IT in order to analyze a set of data and make predictions.
Corroboration
(none)
AI tools guidance
Evaluation and delivery
| Activity | Criteria | Methods | Percentage |
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Performance standards
Minimal standards (knowledge and skills for the grade 5) Exploratory data analysis: producing simple graphical representations to investigate the relation between two or more variables and interpreting them. Prediction: describing a regression technique. The final grade is the weighted average of grades obtained for components 9.4 and 9.5. The exam is passed if the final grade is at least 5 (it is not necessary for each grade to be greater than 5). For every exam session, the grade is computed by the same rule. During the semester, students may attend tutoring hours, during which the teacher answers their questions and provides supplementary explanations regarding the lecture, lab applications and homework.
Additional info
(none)