raw data

Examples (20)

• We have amassed a large amount of raw data for analysis.
• Always back up and store the raw data before any cleaning.
• The algorithm works by processing large volumes of raw data.
• The algorithm performs best when trained on raw data collected in the field.
• Before it can be used, the raw data must be cleaned and organized.
• We’ve aggregated everything, but the insights must come from analyzing the raw data.
• Our servers collect raw data from user interactions on the website.
• Regulators requested access to all raw data underlying the published results.
• What insights can we derive from this pile of raw data?
• Could you export the raw data as a CSV for me?
• Working with raw data can be challenging due to its unstructured nature.
• At this stage, we simply label the raw data and postpone modeling.
• The scientists spent months collecting the raw data from their experiments.
• The dashboard only displays metrics; the raw data lives in the warehouse.
• The next step will be to feed the raw data into our machine learning model.
• After the outage, they recovered the raw data from backups.
• The sensors provide a continuous stream of raw data about the environmental conditions.
• Don’t modify the raw data; create a derived table instead.
• Interpreting raw data is often more difficult than analyzing a curated dataset.
• Without any preprocessing, what patterns can you see in the raw data?