Scope of data journalism for developers
summary
Argues that developers can pivot into data journalism by scraping and cleaning messy sources (Vahan, MCA, RBI, IMDb, 007), turning them into publishable findings such as unexpected COVID outliers or a shift of flight traffic to tier‑two cities, and entertainment metrics that drive readership. Uses R, Python, Excel with libraries like pandas, BeautifulSoup, ggplot to automate ETL, join datasets, visualize trends, and produce stories that prompt policy or consumer decisions.
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chapters (15)
- 0:00Introduction to data journalism
- 1:06Basics of journalism for developers
- 2:11What is data journalism
- 3:34How developers contribute
- 4:38When data journalism is needed
- 6:05Transforming data into stories
- 7:50Understanding situation gravity
- 8:33Data journalism in all fields
- 10:00Data sources across beats
- 12:28Why developers should try data journalism
- 13:51Career growth and creativity
- 15:40How to start in data journalism
- 17:25Finding data stories
- 19:13Entertainment data examples
- 20:16Q&A on data journalism beats
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