Data Analytics and Communication

This course emphasizes on clarity, impact, and responsibility in data communication—skills essential for researchers, analysts, and industry professionals.

. Python (Jupyter Notebook)

· Matplotlib, Seaborn

· Markdown, LaTeX (Overleaf)

· PowerBI or (Tableau basics, upon request)

· GitHub - commit

By the end of the course, students will be able to:

1. Understand what is data and why it is important.

2. Translate data analysis problems and results into clear narratives for technical and non-technical audiences.

3. Design effective static and interactive data visualizations using best practices.

4. Build interactive dashboards for exploratory and explanatory analysis.

5. Communicate findings through professional technical reports and presentations.