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In-class exam 1 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 6.
  • 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 visualization)
  • 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?

Extra office hours

  • Monday 10/16 9:00 AM - 10:00 AM
  • By email

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 or in 15-minute Friday notebooks might show up on the exam.

Markdown

  • Fonts (bold, italic, etc)
  • Lists (ordered, unordered)
  • Links
  • Formatted code

Python basics

  • Types (how to find a type, how to convert)
  • Working with lists, strings (including string formatting), and dicts
  • Bools and if statements, conditional statements
  • Loops and list comprehensions
  • Slicing
  • User-defined functions, keyword and positional arguments

Pandas

  • Creating DataFrames (from a dict, from a file, handling messy files)
  • Working with the index
  • Dealing with column names
  • Computation on DataFrames
  • Summary statistics from a DataFrame
  • Taking subsets (row and/or columns) from a DataFrame (using .loc[], using conditional statements)

Matplotlib

  • Creating a plot from a DataFrame
  • Line plots
  • Changing line color, style, alpha
  • Labeling components of a figure
  • Histograms
  • Subfigures

Visualization

  • Audience/message/medium
  • What are line plots / scatter plots / bar plots / histograms / maps good for? (you do not need to make scatter, bar or maps)
  • Using color
  • I might give you a figure and ask you to critique it