Progress update guidelines

Progress update guidelines

Each update will differ in content, but in general the purpose of preparing progress updates is the same in each instance:

  • step back, organize, and reflect on your project work to date;

  • practice communicating your work to a broader audience in writing;

  • draft materials that could be used in a report, manuscript, or slides.

These updates create excellent opporutnities to appreciate and acknowledge your efforts, discuss and affirm a shared view of the project with your team, recalibrate your perspective on what you’re currently doing, set goals for future work, and – since posts will be public – ask for feedback from classmates and anyone else whose perspective you value.

Progress updates should be written for a varied audience (listed from most specialized to least):

  • your peers in the capstone class;

  • your sponsor’s colleagues (researchers, specialists, management, etc.);

  • prospective and future data science capstone students.

You can assume all groups share an interest in data science, but you should also assume that your audience has diverse technical expertise and that most readers will lack the domain knowledge relevant to your project. (Students, for example, may know more about statistical methods than your sponsor’s colleagues.) You can, of course, include some details that may only be meaningful for technical specialists or domain experts, but overall your writing should be accessible to the broader audience described above. To that end, here are some suggestions:

  • avoid overly technical jargon wherever possible by interpreting technical aspects of your updates in the context of your project;

  • when needed, define any domain-specific terminology, technical language, or acronyms;

  • label plots, tables, and figures clearly and provide descriptive captions;

  • avoid showing codes, computer output, and equations unless they are absolutely essential to communicating or illustrating your point;

  • use descriptive headers and subheaders (e.g., ‘Customer-agent conversation data’ instaed of ‘Datasets’, ‘Predicting arrests using machine learning’ rather than ‘Introduction’) to organize your post;

  • use links and references to point readers to background resources.