In the past year, we have seen a big rise in Data Science oriented courses. These courses are not only popular amongst Computer Science students, but also for non Computer Science students from Business, Econometric or other STEM degrees. With our recent release CodeGrade Orchid, we have added AutoTest caching to CodeGrade, which solves some challenges posed in Data Science assignments:
- In Data Science, as the name suggests, you often work with vast quantities of data. Downloading or uploading these large data sets can cause the configuration of an autograder to slow down, making feedback to students less instant.
- Many R and Python Data Science courses rely on additional packages that are very specific to Data Science and have to be installed in the autograder. A good example is the Python Tensorflow package, which may take more than 10 minutes to install. If you have to do that for each student, you are losing a lot of speed.
While there are a lot of challenges with autograding Data Science, it is a field that especially benefits from autograding. It is often a first encounter with coding or scripting for students, and giving them instant feedback helps motivate them and accelerate their learning. Furthermore, Data Science assignments are often compiled of different subtasks, which are perfectly suited for autograding: students will see visible progress while solving each subtask and get feedback very quickly. We are proud to announce that with the addition of AutoTest caching to CodeGrade, we have solved the challenges to help you benefit even more from the advantages of autograding for your Data Science course. In the rest of this article, we will explain how to turn on AutoTest caching and how exactly it works.
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