Data-driven journalism is a type of journalism that involves the use of data analysis and visualization techniques to uncover and present news stories. It involves collecting, analyzing, and interpreting large sets of data to find patterns, trends, and insights that can inform and enhance reporting.

Data-driven journalism allows journalists to go beyond anecdotal evidence and personal opinion by using hard data to support their stories. It enables them to ask more in-depth questions, identify hidden connections, and provide a more comprehensive and accurate picture of complex issues.

In data-driven journalism, journalists often work with data scientists or data analysts to collect and clean data, perform statistical analysis, and create visualizations that make the data more accessible and understandable for readers. This collaboration between journalists and data experts helps ensure that the data is accurately interpreted and presented in a way that is engaging and informative.

Data-driven journalism can cover a wide range of topics, from political and economic issues to environmental concerns and social justice. It can involve analyzing government datasets, conducting surveys and interviews, scraping data from websites, and using other data sources to uncover stories that might otherwise go unnoticed.

Overall, data-driven journalism is a