Heads up! To view this whole video, sign in with your Courses account or enroll in your free 7-day trial. Sign In Enroll
Well done!
You have completed Preparing Data for Analysis!
You have completed Preparing Data for Analysis!
Preview
Learn the different types of bad data and what they mean.
Terms
- Duplicates - repeated data
- Missing data - data labeled as unknown, Nan, or empty
- Formatting - misspellings, extra whitespace, differences after combining multiple datasets
- Type - data that is a different type than expected
- Nonsensical - data that does not make sense
- Saturated - data that is at the extremes of the measurement
- Confidential - personally identifiable information
- Individual Error - errors that affect a single value
- Systematic Error - errors that affect all or large portions of the data ### Further PII Resources:
- DOL PII
- EU GDPR
Related Discussions
Have questions about this video? Start a discussion with the community and Treehouse staff.
Sign upRelated Discussions
Have questions about this video? Start a discussion with the community and Treehouse staff.
Sign up
You might be able to pick out
a few of the issues already, but
0:00
let's give them a name.
0:00
These are in no particular order.
0:01
First, duplicates.
0:04
There are two Blastoise
entries in the data set.
0:07
Duplicates can bias your results and
0:11
can also make the dataset take
up more space than it needs to.
0:14
Next is missing data on line 44.
0:19
Golbat has some values that are missing.
0:23
Instead of values there are empty cells or
even NAN,
0:28
You need to sign up for Treehouse in order to download course files.
Sign upYou need to sign up for Treehouse in order to set up Workspace
Sign up