Data cleaning
Data cleaning is one of the most challenging part of Data analysis and Data science, or anything that relies on data (e.g., Machine learning).
Important steps include:
- Handling Missing data.
- Check assumptions about the data, often called “sanity checks”.
- Check typos and errorneous observations.