In this course, you’ll write data analysis reports in Jupyter notebooks, combining programming in Python with visualizations and written explanations. You’ll turn in a report for many of the homeworks, and your two main projects will also take the form of Jupyter notebook reports.
Good data analysis requires the combination of clear writing, well-functioning code, and solid quantitative reasoning. You can follow this list of criteria for crafting good reports, and I’ll use the same criteria when I assess your work.
Focus and Organization: All questions in the report are answered and all required sections are complete. Report is organized and easy to follow.
Writing: Writing is clear, with all visualization and statistical output explained completely, accurately, and in terms of the data.
Code: Code sections are complete and all code runs without errors. Code is well-commented and easy to read.
Statistics & Analysis: All analysis is accurate and given in context, both in terms of the data and of the relevant statistical concepts.
Visualization: Visualizations are clear, accurate, and well-labeled. Good visualizations should be readable on their own but also well-explained in writing.
For more on what I’m looking for in the writing, visualization, and analysis portions of the reports, you can refer to the How to Explain in CIS241 guide. (Even though it’s not for our class, the general ideas are still relevant here.)