This introductory training provides a practical overview of the R programming language for data analysis. Participants will learn the fundamentals of R, including working with data, basic programming concepts, data manipulation, visualization, and simple statistical analysis. The course combines short theoretical explanations with hands-on exercises, enabling participants to build a solid foundation for further work with R in research, analytics, or data science.
The workshop will be interactive, featuring progress checks and opportunities for participants to ask questions.
Upon completion of the workshop, participants will receive an electronic certificate of completion.

Date: to be arranged with the teacher once the full group has been assembled (minimum of 3 participants), in accordance with participants’ suggestions (2-days online workshop).

Time: 10 hours (5 hours each day with two coffee breaks).

Location: online.

Group: up to 10 persons.

Teacher: Piotr Płuciennik, Ph.D., D.Sc., Associate Professor

Price: 300 EUR (plus VAT if applicable) per person.

Methods of payment: wire transfer.

Topics:

• Introduction to R and the RStudio environment
• R syntax and basic programming concepts
• Working with data types and data structures
• Importing and exporting data
• Data cleaning and preprocessing
• Data manipulation and transformation
• Exploratory data analysis
• Data visualization using ggplot2
• Basic statistical analysis in R
• Writing functions and scripts
• Best practices for reproducible data analysis

Learning Outcomes

After completing the course, the participant will:

1. Understand the basic principles and syntax of the R programming language.
2. Import, explore, and manage datasets in R.
3. Perform basic data manipulation and cleaning.
4. Create clear and informative data visualizations.
5. Apply fundamental statistical functions and analyses in R.
6. Write simple scripts and use R packages to extend functionality.

Piotr Płuciennik, Ph.D., D.Sc., Associate Professor

Mathematician and economist. Certified academic tutor. His research focuses on computational finance and financial data analysis, while also encompassing the analysis of other types of data, including medical datasets. He is the author of more than 50 scientific publications and has participated in five research projects. He has gained research experience through work at higher education institutions and public sector organizations.
He teaches courses in data analysis and computational finance at the bachelor’s, master’s, and doctoral levels. He has supervised more than 150 bachelor’s and master’s theses and provides individual tutoring programs for highly talented students. He delivers training for academic staff and corporate professionals in data analysis using R, Python, MATLAB, and Wolfram Mathematica, as well as specialized software such as Gretl and MedCalc. He has performed computational analyses for both research and corporate projects.