While some DataCamp courses are introductory and therefore require no prerequisite knowledge, most courses have content that needs a bit more. We want our students to be prepared (and not overwhelmed), so we ensure that all courses beyond the absolute beginner level have DataCamp courses listed as prerequisites on the course landing page. It can be useful to see which courses already exist on DataCamp. Listed below are some common questions and problems instructors have when choosing prerequisites. Read on and then browse the course library to see if anything looks like a suitable prerequisite.
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You need enough prerequisites to deter unqualified students from starting your course, but don't have so many that you deter qualified students from starting. In practice, two or three prerequisites is usually appropriate.

Usually no. In the first exercise where the student draws a plot, keep it simple, explain things gently, and link to an appropriate plotting course. If the course makes heavy use of plotting throughout, you may wish to consider having a plotting course as a prerequisite.
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If you don't have enough prerequisites, then students without enough background experience will take your course, and are likely to give it low ratings. Be honest about the knowledge you assume of students who take your course will have.

If you have too many prerequisites, then students will be put off taking your course, you will get fewer course completions and ultimately less money. It's okay to be thorough about listing prerequisites, but they will likely be cut down to the best three before launching your course.
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From a course on clinical trials analysis. These prerequisites suggest that students need basic R programming skills, basic statistical skills, and basic exploratory data analysis skills.

From a course on XGBoost. These prerequisites make it clear that the course follows on from the scikit-learn course.