Problem Solving Using Open-Source Languages; R and Python

Novi Sad, Serbia

Date

Physical: 23 Oct 2026 11:00 - 24 Oct 2026 15:30
Online: 14 Sep 2026 09:00 - 20 Oct 2026 13:00

Location Novi Sad, Serbia

Host university University of Novi Sad (UNS)

Mode Blended

WP WP 2

Target groups Master students, PhD students, Undergraduate students

Contact person Nataša Hrabovski - natasa.hrabovski@uns.ac.rs

Duration Up to 1 month in length

Reference code EUG2_T2_1_0016

Type of event Course

Recognition Transcript of records - ECTS

Language English

Recruitment of participants Qualitative Assessment

Number of open spots 20

Is eligible to international certificate Yes

Evaluation criteria 100% - Motivation

Isced fields of study 05 - Natural sciences, mathematics and statistics

Find out more and register now
Deadline: 1 Jul 2026 23:59

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.

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.

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Last update 13 Mar 2026 12:05

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