What is a capstone exercise?
A Capstone Exercise is just the final exercise in a chapter. They aren’t meant to be “projects”. They aren’t meant to be larger or more in-depth than a typical exercise. They just happen to come at the end of a chapter. Because each lesson in a chapter builds on previous lessons, we would expect that the final exercise will touch on concepts from the entire chapter, but we don’t necessarily expect it to assess all of the learning objectives taught in the chapter.
All capstone exercises will be remote desktop exercises (a.k.a. RDEs, VM exercises, or Virtual Machine Exercises). To learn how to create remote desktop exercises in Teach, refer to the Remote Desktop Exercises for BI courses article.
Why build capstones?
We ask you to build four Capstone Exercises during the Course Specs process. We do this because:
We want you to get used to using our Teach editor. Building these exercises should help you learn our content guidelines and become familiar with the platform. This is a great time for asking questions and making mistakes!
Writing Capstones is like building a roadmap for your course. If you know where each chapter is supposed to end, it’s much easier to figure out what needs to be in the rest of the chapter.
Capstones can help spotlight potential problems with course structure. As you build a Capstone Exercise, you’ll notice required prerequisites that might be missing, or concepts that are difficult to teach via code. Sometimes, this can lead to big changes in your course outline.
Common Capstone Problems
Covering too much material
The most common challenge in designing a capstone exercise is choosing how much material to include. Most instructors’ first instinct is to build a toy version of a typical data analysis process: load and visualize a dataset, apply some sort of analysis or machine learning, visualize the result. This is a great starting point, but it usually ends up with too much material for a single exercise. Here are some tips for trimming down an exercise:
You can probably do the initial dataset familiarization in your slides or in a previous exercise. In fact, you’ll likely work with the same dataset for an entire chapter, so it would be jarring to “start from scratch” in your final exercise.
Focus on the learning objectives you want to assess. If you want to teach a new methodology (such as “Learner will be able to perform k-means clustering” or “Learner will be able to fit a linear regression model” or “Learner will be able to use a CASE statement to clean data”), you probably want to focus on the “middle” step. If you want to the learner to think about the results of an analysis (such as “Learner will be able to compare the results of a logistic classifier and a random forest classifier” or “Learner will be able to select an appropriate SQL table schema” or “Learner will be able to define an outlier”), you might want to focus on the visualization/exploration of results.
Consider what tasks are most representative of the chapter. Sometimes, you’ll need to cheat a little bit when making a capstone exercise. Although a wrap-up/visualization of results might be the most appropriate final exercise for a chapter, it doesn’t help DataCamp know where your chapter is headed. In this case, it’s best to build the second-to-last exercise, where you actually perform the critical analysis or computation.
Learning objective mismatch
Before writing a capstone exercise, you should identify one or two learning objectives that you want to assess/practice. This will ensure that your capstone is meaningful and representative of the chapter you are teaching. It will also help you decide what code is superfluous and can be moved to the pre-exercise code. We recommend you use your final lesson learning objective as your guide here, but remember to be as narrow as possible with your learning objective for the exercise while keeping the lesson learning objective in your mind.
Missing motivation: All exercises need some motivation. This usually comes from an interesting dataset and a problem to solve. You can build learner engagement by stating a problem in the Context section of your exercise. It’s likely that the dataset you’re working with has been explained in a previous exercise, but you should still mention which dataset you’re using and why you’re about to give these instructions.
Chapter 1 Capstone Success Message: This is probably your most important success message of the course as this is the end of the first chapter, where unregistered students hit a paywall - you want this one to be as encouraging and engaging as possible, to entice students to keep going.
Chapter 4 Capstone Success Message: This will be the final exercise of the entire course. Given that our platform is all about learning by doing and students will have learned a lot of code at this point, it is highly suggested to finish with a non-multiple choice exercise.