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In-class exam 2 information (fall 2023)

Exam details:

  • The exam starts at 4:00 PM and ends at 5:15 PM in our usual classroom, L196 Education. Get set up early and get logged in.
  • The exam and any needed files will be available on the course website under week 11.
  • You will need to upload your finished notebook to Canvas. Allow yourself enough time to get your file uploaded before 5:15 PM. You might want to log into Canvas before the exam starts, so you can easily upload it once finished.
  • The exam is open-book and open-Internet. Having to rely too much on books and the Internet will slow you down.
  • You cannot work with others on the exam. You cannot post questions online and solicit answers, e.g., through Chegg or chat GPT.
  • Material from the discussion sections will be tested on the exam.

Some suggestions for studying:

  • Go over the notebooks we have covered in class (everything up to and including maps)
  • Go over the coding practices.
  • You could try doing the practice parts from the class notebooks and the coding practice problems again, but without much outside help. Are there subjects that need more practice?
  • Take the practice exam as if it was a real exam. Keep track of time, and do not discuss the exam with others. Did you find subjects that could use more practice?

Topics (a non-exhaustive list)

This list is meant to help you guide your studying. It is not, however, an exhaustive list of everything that I might ask about on the exam. Anything we have covered in class might show up on the exam.

The exam will focus on topics we have covered since the last exam, but our work is cumulative, so things we have covered in the earlier part of the semester might still pop up. (e.g., you will have to load a data file, deal with messy data, or rename variables)

Time series

  1. Converting variables to datetime objects
  2. Time Indices
  3. Resampling
  4. Plotting with a datetime axis
  5. Slicing with datetime

APIs

  1. The only thing you need to know is how to get data from FRED

MultiIndex

  1. Creating a multiIndex
  2. Indexing with multiIndex (and partial indexing)
  3. MultiIndexing columns and rows
  4. Long vs wide data
  5. Reshaping data: stack/unstack

Data transforms

  1. We have covered: replace, unique, map, and str methods
  2. Try picking a random method from here and see if you can figure it out.

Grouping

  1. "Split-Apply-Combine"
  2. Aggregation and operations on groups

Merging

  1. Merging on one key, merging on several keys
  2. Types of merges: inner, outer, etc.
  3. Note that merging often involves reshaping or cleaning data

Maps

  1. Plotting shape files (I will provide any shapefiles you need)