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. For more about Workspace, please see here. 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.
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.
(Optional but recommended) A visual representation of what the solution will look like, e.g., a markdown table displaying the column names, order of rows, and data types.
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.
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).
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).N.B. (As of 1st March 2023 only Python solution files are supported).
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.
Resources
This section provides links to DataCamp resources that may help learners in solving the project. For example:
Lesson videos covering appropriate functions.
Cheat sheets for a package learners may use.
A tutorial for a specific technique.