Q&A 3 What are common sources of datasets for Python and R?

3.1 Explanation

Before working with data, it’s important to know where data comes from. In both Python and R, you can use:

1. Public datasets from libraries or platforms
   
2. Downloaded datasets from repositories
   
3. Real-world data from research, surveys, APIs, or government sources

These sources help you practice data skills using real, structured information.

Common sources include:

• Built-in datasets:
  • Python: seaborn, sklearn.datasets, statsmodels, pydataset
    
  • R: datasets package, MASS, ggplot2, palmerpenguins
• Online repositories:
  • UCI Machine Learning Repository
    
  • Kaggle Datasets
    
  • data.gov
    
  • WHO, UN, World Bank
• Research & Surveys:
  • CSV/Excel/JSON files published with academic papers or institutions
    
  • Survey data from organizations (e.g., Pew Research, Eurostat)
• APIs and live feeds:
  • Weather, financial markets, genomics, social media (e.g., Twitter API)
• Local files:
  • Saved from tools like Excel, Google Sheets, SPSS, or exported from databases

Once you acquire a dataset, you can load, clean, explore, and transform it in Python or R.