If you are focusing on using and/or implementing complex algorithms (i.e., not something like logistic regression/linear regression/random forests), an overview of the algorithms you will be using.
A description of your project plan, and an explanation of why the project is interesting and important.
A review about the source of your data. For example, if you are working with a dataset of patients and predicting mortality based on symptoms, you should include an overview of which symptoms are likely to cause mortality from the medical literature.
An exploratory analysis of your dataset. The analysis should be driven and informed by
A more detailed review of related work in data science – you should find and summarize related work. Your summaries should include descriptions of:
The grading will depend on the particular proposal (for example, not all proposals will include algorithm descriptions).
The following is a rough guide
50% of the grade will be allocated to your summaries of prior work. You should read, understand, and summarize roughly 4-5 papers (though this will vary from project to project)
25% of the grade will be allocated for a cogent explanation of the project plan
25% of the grade will be allocated for the exploratory analysis