This repository hosts the course website of Tilburg University's open education class on "Data Preparation and Workflow Management" (dPrep) - start managing your empirical research projects efficiently!
Incorporate more exercises - Student have requested this and the feedback. One way to do this is to have short questions, e.g. to scrape some thing at the end of the lecture. Or to solve certain problems.
filter required?
aggregation required? (e.g., new variables that can be computed)
recoding required?
merging required?
operationalization required?/new variable required?
etc.
plotting:
axis labeling
title
notes
...
make it a check and discuss (in the lecture) how to go from raw to prepared.
Think carefully about self paced tutorials versus Q and a sessions. There should not be extra material in these tutorials, but a bit of more structured preparation may be necessary.
Hi @andreantonacci, is there a quick way to bring back search on my open edu classes? Would be cool if you could try it out in a feature branch. Thanks!
Wellicht benoemen hoe dit vak zich weerhoudt tot RSM en oDCM (notes)
About you / Covid-19
Vaak is de response rate een stuk hoger bij het gebruik van een poll software. Daarom wellicht een idee om het om te zetten in een aantal korte vragen (bijv. met sli.do):
What's your undergraduate degree? (business administration, marketing, economics, engineering, other)
What stage are you in? (beginner, intermediate, advanced)
What are your long-term career ambitions (go into industry, do a PhD)?
Where are you at right now? (at home, at a friend's place, abroad, etc.)
Home office, on a scale from 1 to 10? (1, 2, ..., 10)
Wanna have the last half-an-hour of this with a borrel? ;) (yes, yes)
Can't find stuff
Discuss other common issues the course addresses
Lost track of files
Excel becomes slow (large datasets)
Add concrete examples (screenshot of messy directory of past research projects, report.pdf -> final_report.pdf -> FINAL_final_report.pdf, etc. )
What's a computation-intensive project
Perhaps make a little bit more concrete (for students who never programmed in R)
For example, running this analysis for my paper would take X days on my local machine (and forces me to keep my computer running 24/7). Thanks to AWS I do the same in X hours.
Course framework
Maybe it makes more sense if you first show the course framework and then the team project?
Canvas versus the web
Sign up for teams through Canvas?
Comments on coding
Mention how we aim to remedy this initial hurdle? (quick feedback loops especially in the first few weeks)
Help
Give concrete example on how they can find help with Google and Stackoverflow (say that I don't know how to do X in R) -> prepare them for what's coming up.
Go over the help workflow:
Read documentation
Google / Stackoverflow
Ask a friend
Google / Stackoverflow
Ask a friend
Ask course instructor (when to use which channel, guidelines on what to include; the better the question the better the answer)
Missing
Briefly touch upon the grading scheme for the course (couldn't fit it in this slide deck)
Live coding sessions where they can help each other out (what to expect, etc.)
Set the scene for what's coming next:
Show them where they can find what to do each week (click through it), discuss learn -> practice -> create
How to right click, download file as...
Briefly mention the difference between R and RMarkdown files
Manage expectations: DataCamp courses are simplistic (copy-paste etc.) and should not take too long, the data challenges are more challenging and will likely take more time, and are more representative of the level we expect in this course.