| Reference Code | EUG2_T2_1_0016 |
|---|---|
| Host Institution | UNS - University of Novi Sad |
| Description | This hybrid course introduces participants to problem solving using the open-source programming languages R and Python. Through a combination of online sessions and an onsite component at the University of Novi Sad, participants will gain practical skills in data manipulation, visualization, statistical analysis, and introductory machine learning. The course is designed for students from different backgrounds who are interested in learning how to analyze data and approach real-world problems using modern open-source tools. By the end of the course, participants will be able to work with data in R and Python, apply basic analytical methods, and collaborate on a practical project that integrates the knowledge and skills acquired throughout the program. Number of ECTS: 3 Course syllabus (link) Course program (link) Dates: The course will have an online component on the 14, 15, 21, 22, 28, 29 of September and 5, 6. 12, 13, 19 and 20 of October, and an in-person component at the University of Novi Sad from 23 to 24 October. |
| Mode | Blended |
| Period |
Physical: 23 Oct 2026 11:00:00 — 24 Oct 2026 15:30:00 Online: 14 Sep 2026 09:00:00 — 20 Oct 2026 13:00:00 |
| Duration | Up to 1 month in length |
| Type of activity | Course |
| Target groups | Undergraduate students, Master students, PhD students |
| Location | Novi Sad, Serbia |
| WP | WP 2 |
| ISCED Fields of Study | 05 - Natural sciences, mathematics and statistics |
| Contact Person | Nataša Hrabovski natasa.hrabovski@uns.ac.rs |
| Content and Methodology | In the initial week, participants will acquire introduction to the R and Python programming languages, encompassing the setup of the environment, basic syntax, data types, as well as fundamental data manipulation and analysis. Moving on to the second week, the learning objectives will revolve around data input/output, employing dplyr in R and pandas in Python for data manipulation, and utilizing ggplot2 in R as well as matplotlib/seaborn in Python for data visualization. The focus of the third week will shift towards statistical analysis using R and Python, accompanied by an introduction to machine learning. Supervised learning algorithms, including linear regression, logistic regression, decision trees, and random forests, will be introduced. In the concluding phase, the final project will require students to engage in a practical application of their acquired skills and concepts. This project will involve working with R and/or Python to demonstrate proficiency in the tools and methodologies learned throughout the course. |
| Recognition | Transcript of records - ECTS |
| Language | English |
| Funding by EUGLOH budget | Funded in full |
| Recruitment of Participants | Qualitative Assessment |
| Number of open spots | 20 |
| Call for Applications |
OpenCurrent call11 Mar 2026 — 1 Jul 2026 Apply now |