A Workspace project is designed for learners to apply skills they have learned in a course or multiple courses to solve an end-to-end business problem. It is the final stage of the ALPA Loop (below).
Projects provide learners with the opportunity to hone their skills and knowledge by working through a typical data science workflow. Examples include cleaning data in preparation for predictive modeling, performing statistical tests to confirm or reject hypotheses, or querying a database to answer business questions.
The purpose of projects is to allow learners to work on their decision making skills alongside applying skills and techniques they have learned on DataCamp. Therefore, it is important that projects are designed in a way that enables learners to identify the approach they would like to take and, ultimately, solve the project using an approach of their choice rather than simply requiring them to read and follow a sequence of steps.
Additionally, projects serve as a way for learners to prepare for DataCamp Certification, where they will be required to complete a Practical Exam using Workspace.
As an example, the Analyzing Crime in LA project requires learners to answer questions about crime data for the LAPD through the use of techniques including subsetting, aggregation, and interpretation of tables/plots. It is designed based on the application of the following Knowledge, skills, and abilities (KSAs):
Order data in a dataset
Interpret tables and charts to understand counts and proportions
Learners may use any approach they choose to complete the project, it only matters that they are able to produce the correct answers at the end.