You will need to write a list of learning objectives for the course. These are not shown to the students, but they will be used to ensure your vision for the course aligns with DataCamp's vision. Ideally, you should be testing higher levels of thinking (according to Bloom's taxonomy, see below). You will need to write at least one learning objective per lesson. Read on for some helpful tips and tricks.
What is Bloom's taxonomy?
Bloom describes six levels of understanding (bigger numbers are better).
Knowledge: recalling learned information (name, define, recall).
Comprehension: explaining the meaning of information (restate, locate, explain, recognize).
Application: applying what one knows to novel, concrete situations (apply, demonstrate, use).
Analysis: breaking down a whole into its component parts and explaining how each part contributes to the whole (differentiate, criticize, compare).
Synthesis: assembling components to form a new and integrated whole (design, construct, organize).
Evaluation: using evidence to make judgments about the relative merits of ideas and materials (choose, rate, select).
For further reading, Wikipedia has a thorough overview of the framework.
Have testable learning objectives
Rather than writing "Student will understand X," a good learning objective should specify what the student will do to demonstrate what they know.
The three elements of a good learning objective are:
What you want the student to master.
What level of understanding you want them to have.
What they will do to demonstrate their understanding.
Talk about problems, techniques, and technologies
There are three reasons why a student might take a course. They might want to know how to solve a particular problem, they might want to learn a new technique, or they might want to learn to use a new technology (or package). Consequently, each learning objective should relate to at least one of these three ideas.
Make learning objectives specific
If the objectives for the course aren't clear to you, then they really won't be to the students. Try to make the objectives specific enough that as you the course, you can check back to see if the exercises will help work towards those objectives.
From a course on scikit-learn. This nicely clarifies the point of the course.
Learn the key concepts of supervised learning and how to implement them on real-world datasets;
Learn to distinguish regression from classification problems;
Learn to evaluate how well your classification and regression modes perform;
Learn best practices in supervised learning, such as splitting into test/train sets and k-fold cross validation;
Learn how to improve model performance by both preprocessing your data and regularizing your models.
From a course on single cell RNA-Seq analysis. This is exhaustive in describing the objectives.
Explain the difference between bulk and single-cell rna-seq (amplification bias and dropouts)
Exploratory data analysis (biases: library size, batch, cell-cycle)
Make clear what normalization means and why it is needed
Use main dimensionality reduction methods: PCA, tsne, zinbwave
List main methods for clustering and perform clustering on a real dataset
Compare different clustering methods
Understand the difference between parametric and non-parametric tests
Distinguish NB and ZINB distributions
List main methods for DE analysis and perform DE analysis on a real dataset
Explore and visualize results of DE analysis
See also Exercise-level learning objectives.