Problem Solving Using Open-Source Languages

This is a hybrid course, with online sessions and onsite session at University of Novi Sad. The course aims at improving students’ knowledge of programming with both R and Python, together with solving problems in both languages. Participants who finish the course will gain 3 ECTS.

The programme schedule will include both in-person and online sessions: Online: 8-10 April, 15-16 April and 22-23 April 2024 (from 8:30-15:30) — Onsite: 27 April 2024. The course is structured as follows:

• Introduction to R and Python: 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.
• Data Manipulation and Visualization: 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.
• Statistical Analysis and Machine Learning: the focus 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.
• Final Project at University of Novi Sad: 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.

Learning objectives

Upon completion of a course on Problem Solving Using Open-Source Languages R and Python, learners can expect to achieve the following learning outcomes:

• Proficiency in programming with both R and Python and the ability to solve problems in both languages.
• Ability to perform data manipulation and visualization tasks using the respective tools/packages of R and Python.
• Understanding of basic statistical concepts and ability to analyze data using statistical techniques in both R and Python.
• Familiarity with the principles of machine learning, its algorithms, and its application in both R and Python.
• Ability to think critically and creatively to solve real-world problems using R and/or Python.
• Ability to work collaboratively and communicate effectively in a team to complete a final project that applies concepts learned in the course.

Competences

• Digital skills
• Critical thinking
• Creativity & problem solving

Requirements

Previous knowledge is not required. The learners interested in critically and creatively solving real-world problems using R and/or Python should apply.

• Selection strategy: it is based on goals and objectives for taking the course as well as specific needs and areas of interest related to R and Python software, which will be described in a motivation letter (required).

Another selection criteria is diversity in order to create a diverse group of participants, including those from different backgrounds, industries, and levels of experience, to promote learning and collaboration among participants.

• Number of participants: 20.
• Certificate/ECTS: 3 ECTS.
• Language: English.

Please note that a scholarship for travel and subsistence is included for participants.

This activity is part of Work Package 2.