{"action":"created","issue":{"url":"https://api.github.com/repos/datakid/django-tutorial/issues/1","labels_url":"https://api.github.com/repos/datakid/django-tutorial/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/datakid/django-tutorial/issues/1/comments","events_url":"https://api.github.com/repos/datakid/django-tutorial/issues/1/events","html_url":"https://github.com/datakid/django-tutorial/issues/1","id":61754104,"number":1,"title":"Integrating this lesson with Data Carpentry","user":{"login":"DamienIrving","id":2062210,"avatar_url":"https://avatars.githubusercontent.com/u/2062210?v=3","gravatar_id":"","url":"https://api.github.com/users/DamienIrving","html_url":"https://github.com/DamienIrving","followers_url":"https://api.github.com/users/DamienIrving/followers","following_url":"https://api.github.com/users/DamienIrving/following{/other_user}","gists_url":"https://api.github.com/users/DamienIrving/gists{/gist_id}","starred_url":"https://api.github.com/users/DamienIrving/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/DamienIrving/subscriptions","organizations_url":"https://api.github.com/users/DamienIrving/orgs","repos_url":"https://api.github.com/users/DamienIrving/repos","events_url":"https://api.github.com/users/DamienIrving/events{/privacy}","received_events_url":"https://api.github.com/users/DamienIrving/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":2,"created_at":"2015-03-15T05:12:51Z","updated_at":"2015-03-24T03:07:15Z","closed_at":null,"body":"@datakid I was chatting with @dfflanders the other day and he suggested that this Django lesson could be preceded by a spreadsheets lesson. In other words, you'd do some spreadsheet basics and lead them to a point (via a research data example) where using a spreadsheet gets a little cumbersome/awkward. This would ensure that everyone really gets why using a database via django can be so useful.\r\n\r\nData Carpentry already has a [spreadsheets lesson](https://github.com/datacarpentry/datacarpentry/tree/master/lessons/excel) and I'm thinking this django lesson could potentially belong in the Data Carpentry repo one day, so we certainly don't need to be developing a spreadsheet lesson of our own at this stage."},"comment":{"url":"https://api.github.com/repos/datakid/django-tutorial/issues/comments/85319236","html_url":"https://github.com/datakid/django-tutorial/issues/1#issuecomment-85319236","issue_url":"https://api.github.com/repos/datakid/django-tutorial/issues/1","id":85319236,"user":{"login":"datakid","id":97304,"avatar_url":"https://avatars.githubusercontent.com/u/97304?v=3","gravatar_id":"","url":"https://api.github.com/users/datakid","html_url":"https://github.com/datakid","followers_url":"https://api.github.com/users/datakid/followers","following_url":"https://api.github.com/users/datakid/following{/other_user}","gists_url":"https://api.github.com/users/datakid/gists{/gist_id}","starred_url":"https://api.github.com/users/datakid/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/datakid/subscriptions","organizations_url":"https://api.github.com/users/datakid/orgs","repos_url":"https://api.github.com/users/datakid/repos","events_url":"https://api.github.com/users/datakid/events{/privacy}","received_events_url":"https://api.github.com/users/datakid/received_events","type":"User","site_admin":false},"created_at":"2015-03-24T03:07:15Z","updated_at":"2015-03-24T03:07:15Z","body":"Ok, I've added commit https://github.com/datakid/django-tutorial/commit/37578eb9e76d0e87f48cfe71a4f4c42bcc51fb8f which adds in 5 spreadsheets, all taken from the original data set that this easy tute is based on.  \r\n\r\nThe spreadsheets are ordered from \"almost a direct replica of the database\" (not even any vlookups) through to vlookup applied, data copied and paste-specialed into a \"poor data modelling\". Note that there are no data quality issues in these spreadsheets. It's 100% complexity of modelling and \"what do I do next\", when there is lots of data.\r\n\r\nData quality is another issue entirely.\r\n\r\nIf there are no complaints, I'll close this issue?"}}