Homework

Homework Assignments

Here is a list of HW assignments.

Project Milestones

Here is a list of Project Milestones.

Homework Policies and Grading

Below are very detailed notes on HW policies and grading. The goal is to provide detailed instructions and transparency, so that we can avoid confusion and so that every student is operating from the same set of assumptions.

Hot Tips

The following will help HW go more smoothly and deepen your understanding of the material.

  • Review

    • Before sitting down to start HW, review the last few weeks of material.

  • Start early

    • Start HW early so that you have time to ask questions and absorb the material.

  • Never use AI as your first approach to an exercise

    • You will learn very little and will be unprepared for other interactions with the material (eg: in-class discussions, quizzes, etc).

  • Work together

    • Though your HW must be in your own words, you’re encouraged to work with classmates on HW. Consider organizing a study group!

  • Ask questions

    • Swing by office hours or ask questions on Slack. Do not rely on receiving responses outside of weekdays between 9am & 5pm.

Academic Integrity

You must meet the college’s expectations around academic integrity & other academic policies. In general:

  • do not pass off others’ work as your own

  • do not use the the following:

    • any materials from any other current sections of 112
    • any materials from any past sections of 112
    • solutions (eg: online solutions, solution sets from this course, etc)
    • online forums (eg: where you or others post HW questions)

  • IF you use AI tools:

    • You can use these for brainstorming & outlining, but cannot use them to generate entire discussions or bodies of code.
    • You must cite this work. How? Include a paragraph at the end of your homework explaining what you used the AI for and what prompts you used to get the results.

Timing & Extensions

HW is due by 11:59pm on the corresponding due date. This is to accommodate various work and extracurricular schedules, not to encourage you to stay up late!

  • Extension opportunities:

    • Each student can have a 3-day extension on up to 3 HW assignments. The 3-day extension includes weekends. Thus a HW originally due on a Tuesday could be handed in on Friday with an extension. A HW originally due on a Thursday could be handed in on Sunday with an extension. This policy is in place to provide some flexibility while ensuring that you stay on track to succeed in this course.

  • Extension directions & policies:

    • Extensions should be submitted to the relevant homework link on Moodle.
    • Extensions will be tracked in the Extensions assignment on Moodle.
    • If you use an extension, you should expect a delay in your feedback.

  • Extenuating circumstances:

    • In general, HW that exceeds the extension opportunities will not be graded or accepted for credit. However, there might be additional flexibility in rare extenuating circumstances. Additional flexibility needs to be discussed with me in advance, not assumed.

Grading

You will make mistakes and that’s ok! Instead of every mistake chipping away at your grade, HW will be graded on a 3-point scale:

  • 3 = high pass (earned at least 90% of the total points)
  • 2 = pass (earned 75-90% of the total points)
  • 1 = low pass (earned 60-75% of the total points)
  • 0 = did not pass or did not submit HW

The total points will be calculated based on the following:

Exercises

6 exercises will be graded, and there are 5 points per exercise

  • 5 = 90-100%, 4 = 80-90%, 3 = 70-80%, 2 = 60-70%, 1 = 50-60%, 0 = 0-50%
  • Points will be determined by the completion of the following:
    • presenting a correct and complete solution
    • properly formatting your code
    • supporting answers with appropriate evidence (e.g. R code & output)
    • basing all discussions in the context of the exercise, not general definitions.

Presentation

Presentation is really, really important in data science. For full credit, you must:

  • Utilize the provided qmd template.
  • Type your name in the “author” line at the top of the qmd.
  • Do not otherwise modify the structure of the template (e.g. don’t change any text at the top of the document, section headers, spacing, etc).
  • Submit a readable HTML file (details below).

What to expect if you don’t submit a readable HTML file, and how to avoid it:

  • Scenario 1: You submit an HTML that is unreadable, thus graders cannot grade it.
    • Result: You can use one of your extensions to revise and resubmit the HW. Otherwise (eg: if you do not have any remaining extensions), you will receive a score of 0 on the HW.
    • How to avoid this scenario: Don’t modify the top of the qmd template.
  • Scenario 2: You are unable to render your qmd into an HTML, thus are unable to submit your HW on Moodle.
    • Result: You can use one of your extensions to revise and resubmit the HW. Otherwise (eg: if you do not have any remaining extensions), you will receive a score of 0 on the HW.
    • How to avoid this scenario:
      • Render your qmd as you go so that you can identify errors as they arise. Don’t wait to render until right before HW is due!
      • If your qmd won’t render:
        • Check that you’ve loaded all necessary packages (eg: tidyverse) before the exercises that need them. This needs to be done inside your qmd file.
        • Identify any chunk that has an error, and change it to ```{r eval = FALSE}. This will show what code you’ve tried, but it won’t run the code when rendering the qmd. This way, you could get partial credit.