Data cleaning and normalization¶

Python notebook: https://github.com/daviskregers/data-science-recap/blob/main/24-cleaning-data.ipynb

• The reality is, much of your time as a data scientist will be spent preparing and cleaning your data.
  • Outliers
  • Missing data
  • Malicious data
  • Erroneous data
  • Irrelevant data
  • Inconsistent data
  • Formatting

Garbage in, garbage out¶

• Look at your data, examine it.
• Question your results.
  • And always do this - not just when you don't get a result that you like.