Timeline for How much of data wrangling is a data scientist's job?
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Apr 9, 2019 at 1:54 | history | edited | Toros91 | CC BY-SA 4.0 |
organised better
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| Apr 4, 2019 at 13:58 | comment | added | PythonGuest | True. But, also, don't overoptimise. Choose your priorities wisely. If importing the data is a one -off, don't spend days looking for how to reduce the import time from 2 hours to 30 minutes. Etc. | |
| Apr 4, 2019 at 13:35 | comment | added | Jason | Excellent point about finding / buidling an ETL solution. Just need to add: pick a setup you are comfortable with and can easily read / debug. In the early stages of automating tasks, this is even more important than finding the fastest data-slurp tool. If it's gigs of text, it'll likely often run overnight, and your fluency with a tool / framework / language can make the difference between waking up to good data or something you have to start again. Just a single do-over can wipe out any efficiency benefits. Better to be steady with fewer bugs than to go fast and stumble. | |
| Apr 4, 2019 at 13:17 | vote | accept | Victor Valente | ||
| Apr 4, 2019 at 12:40 | history | edited | Stephen Rauch♦ | CC BY-SA 4.0 |
Include Questions
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| Apr 4, 2019 at 12:30 | review | First posts | |||
| Apr 4, 2019 at 12:40 | |||||
| Apr 4, 2019 at 12:29 | history | answered | PythonGuest | CC BY-SA 4.0 |