Data Visualization with Python

This course is designed for aspiring data analysts, business analysts, Python learners, BI professionals, students, and working professionals who want to build practical data visualization skills.

Basic familiarity with Python is helpful, but the course introduces the visualization workflow progressively and includes the Pandas skills needed for the projects.

You will work with Pandas, Matplotlib, Seaborn, and Plotly, along with Streamlit and Dash for building interactive dashboards and applications.

Yes. The course focuses on matching visualization techniques to analytical questions such as comparison, trends, distributions, relationships, composition, geography, and hierarchy.

Yes. You will learn how to build interactive dashboards and data applications using Streamlit and Dash, including filters, widgets, callbacks, multipage applications, and deployment concepts.

Yes. You will learn how to reshape, clean, aggregate, group, and prepare data with Pandas before creating visualizations.

Yes. You will learn visual hierarchy, color and accessibility, clutter reduction, storytelling structure, chart design principles, and common visualization mistakes.

Yes. Advanced topics include animation, network and graph visualization, 3D visualization, Matplotlib's low-level API, and performance strategies for large datasets.

Yes. The course uses realistic business scenarios throughout the curriculum and includes portfolio-focused projects that combine data preparation, analysis, visualization, storytelling, and business recommendations.

You will be able to prepare data for visualization, build professional static and interactive charts, create dashboards, communicate analytical findings effectively, and develop portfolio-ready visualization work using Python.