Learning goals?

[kellieotto]

Check these off when we feel that the notebook has addressed things sufficiently.

• Students should be able to assess the structure and cleanliness of their dataset, including size and shape of data, number of variables of each type
• Students should be able to describe their findings, translate results from code to text using Markdown comments in the Jupyter Notebook, and summarize their thought process in a narrative
• Students should be able to modify the raw data to prepare a clean data set -- including copying data, removing or replacing missing and incoherent data, dropping columns, removing duplicates in Pandas and Jupyter -- and explain and justify their decisions
• Students should be able to assess whether their data is “Tidy” and identify appropriate steps and write and execute code to arrange it into a tidy format - including merging, reshaping, subsetting, grouping, sorting, making appropriate new columns
• Students should be able to identify several relevant summary measures and illustrate data using appropriate plots
• Student should assess the summaries and plots of their data, and appraise the need for repeated or further analysis

[kellieotto]

Describing findings -- see issue #16

Tidy data -- we should add an example with untidy data that needs reshaping (e.g. more than one observation per row)

Summaries and plots -- a skeleton is there but incomplete.

Assess summaries and plots -- also a skeleton. This depends on the previous section.

[kellieotto]

As of now, we've added enough exercises that prompt students to discuss their findings that I feel confident we've achieved learning goals 2 and 6. All that's left is the "tidy data" goal; we've not yet added an additional example for that.