1. I was able to clean data ensuring that null values are all extracted from the dataset.
2. Established a bulletproof habit of using 5 core diagnostics checks (.head(), .shape, .info(), .dtypes, and .is null().sum()) to check for correctness before writing the full scripts.
#30daysdatasciencechallenge
Day1: Building the foundation of data wrangling
So I started a data science course on #WorldQuantLearning
And these are some things learnt:
- Before you can visualize, model or run advanced analytics, your data has to be right.
Dev productivity hack:
Start your day with the hardest task first (the 'eat the frog' method).
That one bug/feature you've been avoiding? Crush it before noon.
The rest of the day feels like cheating.
What's your frog today?