A Workspace project is an exciting content format on DataCamp that allows students to apply the skills they learned in courses to an end-to-end, real-world problem. Authoring a Workspace project consists of producing the following:
Workspace
A Workspace notebook containing:
Context about and motivation for the problem learners need to solve.
The Context section (see below) should be 500-2000 characters in length.
It should not contain any external links that would take learners away from the DataCamp platform.
Sample code to get learners started, such as importing required packages.
(Optional) An overview of the data they will be working with, typically in the form of a markdown table.
Data
One or more datasets (e.g.,
.csv,.xlsx, .json, or.parquetfile(s)) that will be used in the project.
Teach Editor
The remaining sections are completed directly in the Teach Editor:
Description
A description of the project that will be displayed on the project's landing page.
It should highlight what problem learners will be solving, any relevant technologies used, and other motivation for the project.
The Description should be 100-350 characters in length.
Instructions
The instructions describe the challenge the learner has to complete and provide details about the format that the answer needs to be provided in. It should focus on information to understand what is expected of them along with answer format.
It does not include information about how to complete the project (this should only be included in the guides and resources (see sections below) or context about the problem.
This section is always present and visible and hence includes information that needs to be referenced most frequently (e.g. how the answer should be presented).
The Instructions should be 200-1000 characters in length and written in bullet point or numbered list format.
Guides
Step-by-step guidance on how to complete the project including:
Details of tasks required.
Functions/methods to use.
Reminders about syntax.
Submission Correctness Tests (SCTs)
Tests that compare the learner’s output to the solution output, enabling the provision of feedback to help learners identify where they might have gone wrong.
Written in the same language as the project, e.g.,
sct.pyfor Python/SQL,sct.Rfor R).
Solution
Solution code, written in the language that the project is authored in, that learners can use to solve the project.
For Python, it should follow the PEP8 style guide, for R it’s the Tidyverse style guide, and for SQL it’s Holywell’s SQL Style guide.
Additionally, it should include code comments that explain why each step is being taken, so that learners can understand the logic.
Resources
This section provides links to DataCamp resources covering the key techniques appear in the solution code. For example:
Lesson videos covering appropriate functions.
Cheat sheets for a package learners may use.
A tutorial for a specific technique.
Here is an overview of the anatomy of a Workspace project:
Instructions
Context
Sample code
Resources
Guides (Overview)
Guides (hints)
Solution code
