Data professionals spend 80% of their time cleaning data.
Working in data is not always fun and glamourous like AI & ML.
But data cleaning is the most valuable work because it supports all of it.
And everything else downstream and in the ecosystem.
Save these SQL tips to handle messy datasets w/ ease!!
↳ Clean strings: remove extra spaces, weird quotes, and fix casing
↳ Fix dates: standardize formats and remove stray characters
↳ Recode variables: turn "CA", "California", and "calif" into one consistent value
↳ Handle NULLs: don't let missing data mess with your analysis
Data cleaning doesn't have to be painful if you have the right SQL functions!
♻️ Repost for your network and save for later!
Working in data is not always fun and glamourous like AI & ML.
But data cleaning is the most valuable work because it supports all of it.
And everything else downstream and in the ecosystem.
Save these SQL tips to handle messy datasets w/ ease!!
↳ Clean strings: remove extra spaces, weird quotes, and fix casing
↳ Fix dates: standardize formats and remove stray characters
↳ Recode variables: turn "CA", "California", and "calif" into one consistent value
↳ Handle NULLs: don't let missing data mess with your analysis
Data cleaning doesn't have to be painful if you have the right SQL functions!
♻️ Repost for your network and save for later!