Data visualization represents data graphically so analysts can explore patterns, comparisons, and trends, then communicate findings to technical and nontechnical audiences. It functions both as an exploratory step and as a deliverable such as a dashboard. section 1.1 section 10.2.4
A practical visualization workflow connects data preparation to the analytical question:
- Load and combine datasets, then sort, group, reshape, or otherwise manipulate the data before charting it. section 6.2.4 section 8.2.4
- Choose an appropriate visual form for the question and the data, including bar charts, line charts, scatter plots, or categorical-data visualizations. section 1.1 section 7.4
- Inspect categorical or time-series data visually, optionally adding statistical analysis or forecasts where the project requires them. section 7.4 section 8.2.4
- Present the result as a chart, dashboard, or interactive application whose displayed artifacts respond to user input. section 1.1 section 10.2.4
Tool-supported analysis
A Python workflow can use pandas to explore data and matplotlib or seaborn to create visualizations; a rapid prototype can display those charts in Streamlit. Equivalent workflows can use Excel, Tableau, Power BI, R, or other tools capable of reading, manipulating, and visualizing data. section 6.2.4 section 10.2.4
