From aabc4909fc9d3405d54982562148d884d195237f Mon Sep 17 00:00:00 2001 From: Christina Koch <christinakconnect@gmail.com> Date: Wed, 6 Jan 2021 17:26:36 -0600 Subject: [PATCH] adding common challenging concepts --- _extras/guide.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/_extras/guide.md b/_extras/guide.md index c3e1fecb..80385e24 100644 --- a/_extras/guide.md +++ b/_extras/guide.md @@ -33,6 +33,20 @@ If you've written up a diagram of the data analysis pipeline (raw data -> clean data -> import and analyze -> results -> visualization), it can be helpful to identify that you're now somewhere between clean data and analysis. +## Common Difficult Concepts + +* Making the leap from a research question to a query (seen in some of the challenges) +* Data import + * Not necessarily a concept, but we always have at least a handful of people that + struggle with the table import step -- especially in the virtual context with window changing + * Data type options in SQLite (Integer, text, blog, real, numeric) when importing + from CSV. (Maybe have a table of SQLite date types in the student matieral). +* Good to make sure that a comparison is drawn between joins in different +languages, e.g. SQL vs tidyverse +* HAVING, and why it's different to WHERE.... - especially when teaching online +* given the dataset is _relatively_ complex, some students (and instructors) find it difficult to remember what data is where, especially when they're from a totally different domain +* How nulls behave in different circumstances (when did a NULL change your result and how do you know?) + ## Lesson outline ### 00-sql-introduction