Read a dataset card, load the data with pandas, deal with missing values, and check you are allowed to use it.
Most of the work in any AI project happens before a model is trained. This course takes real datasets from the glitchdata hub and walks through the everyday steps: understanding what a dataset contains, loading it, cleaning it, and making sure its licence lets you do what you plan to do.
You will need Python with pandas installed (pip install pandas pyarrow).
4 lessons · 58 min
The questions to answer before you download anything.
CSV quirks, Parquet files, and a first look at what you loaded.
Find the gaps, decide what to do about them, and tidy odd encodings.
What the common data licences allow, and what to do when there isn't one.
No ratings yet.
Sign in to leave a rating. Sign in
Use @handle to bring somebody in, and #tag to file it.
Nothing here yet. Ask a question, or say how you used it.
Sign in to join the discussion. Sign in
1 min read
2 min read
2 min read
2 min read
Writing the card that lets somebody else use data correctly — including you, later.
Dividing data so a score means something, and finding the leak when it does not.
Guidelines, agreement and the quality controls that make labels worth training on.