We designed our learning platform and product to realize a vision. Live training sessions are an extension of that vision, and play a central role in creating an engaging user experience for learners. The main objective of these guidelines is to ensure that all live training sessions we develop are in sync with this vision and provide our users with the best possible user experience.

General Guidelines to Follow Across Slides and Notebooks

You will need to follow DataCamp's style guidelines for your live training to help create a consistent look and feel for students across our different offerings (courses, projects, assessments, practice pools ...). While you may have your own preferred style, these style guidelines are what DataCamp students are familiar with, and your style may not be understood if it's not clear when you are referring to specific pieces of the content.

Make sure to adhere to all of the following in your course:

Session flow and length

A typical live training session is typically around 2h30m to 3h long. Ideally, they should be within that range to maximize for engagement and attendance rate, with a hard limit at 3h30 minutes. A live session also typically has the following flow:

  • General Introduction with slides (10-15 minutes)
  • Live training including various short breaks for Q&A (minimum 3) - (2h10-2h40m)
  • Closing notes with slides (5-10 minutes)

Use American english

We are an American company, and the USA contains our largest group of students, so all live training materials must be written in American English. It is up to you to ensure this, not your CD.

Good: This standardizes the modeling of colors.

Bad: This standardises the modelling of colours.

Use parentheses after function/method names

This formatting helps distinguish functions and methods from variable names.

Python:

Good: .mean() method // print() function

Bad: mean method // print function

R:

Good: print() function

Bad: print function

Format functions, methods, statements, and package names as inline code

Also, format modules and libraries this way.

Good: The pandas package is a tabular data analysis package

Bad: The pandas package is a tabular data analysis package

Code

Follow these standard style guides, unless you have a really good reason not to.

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