Data Processing for Researchers

Short description of the course:

This course offers a practical introduction to data processing and analysis, tailored for PhD students across disciplines. Participants will learn how to collect, organize, and analyze data using widely accessible tools such as Excel, SQL, R, and Python. The course focuses on the full data workflow: from cleaning and structuring information, to applying descriptive and inferential statistics, to communicating results effectively through tables and visualizations. No prior programming experience is required.

Learning outcomes:

By completing this course, students will be able to:

• Organize and summarize raw data using Excel and SQL queries.
• Apply descriptive statistics to explore distributions and variability.
• Conduct basic inferential analyses, including correlations and simple regressions.
• Use R and Python (pandas) to automate data cleaning, manipulation, and visualization.
• Interpret statistical results in the context of academic research.
•  Present findings clearly through tables, charts, and concise conclusions.