This post outlines the in-person part of Exam 1. See the Practicum 1 or Practicum 1 Update posts for details on the other parts.

• Modules covered: 2 – 5
• When: Wednesday 10/16, during regular class time
• Is in-person only
• Bring a calculator.
• It is a paper exam taken during class.
• We will print and provide a reference sheet for you at the exam. See what it is in the exam Box folder.
• You may bring one piece of standard-sized paper as a cheatsheet and can put things on the front and back.
• There will be multiple versions.
• Code on the exam
  • It will have no code writing and focus more on thinking like a data scientist.
  • It will have code reading (so know what these functions do), in particular:
    • The results of calling the describe function on a data set.
    • The results of a seaborn function call: catplot, displot, or relplot.
  • You will not be tested on regular expressions on the paper exam.
  • The data set used for this exam is Seaborn’s taxis data set. We recommend familiarizing yourself with the columns’ meanings.

Study Exams

• Canvas Exam 1 Study Quiz
  • Worth 2 class engagement points
  • Includes randomized question pools for all questions that can be auto-graded of all past exams.
• Study Exam in exam Box folder
  • You may see a question in here that is duplicated from the Canvas quiz, that’s because part of it is not auto-gradeable and we wanted to ensure you saw what the question will look like on the actual exam.
  • Solutions for the exam in Box will be released on the Friday before the exam. This is to encourage everyone to try the study exams before looking at the solutions.

Grading Scale and Points Allocation

For the questions that do not have a clear correct or incorrect answer or where partial credit is warranted, the following rubric will be used.

• E (Exemplary) – Work that meets all requirements and displays full mastery of all learning goals and material. And the code is clean and easy to read (see the study exam for examples of what this means).
• S (Satisfactory) – Work that meets all requirements and displays at least partial mastery of all learning goals as well as full mastery of core learning goals.
• N (Not yet) – Work that does not meet some requirements and/or displays developing or incomplete mastery of at least some learning goals and material.
• U (Unassessable) – Work that is missing, does not demonstrate meaningful effort, or does not provide enough evidence to determine a level of mastery.

The number of points earned is distributed across the problems based on the number of learning goals they are testing. The rubric will be converted to points as follows:

• E = full credit
• S = E_full_credit – some small value resulting in around E_full_credit*0.9
• N = E_full_credit * 0.6
• U = E_full_credit * 0.2
• Blank = 0