Assignments

This page contains information about the assignments and final project.

Timeline

One important thing is (of course) when the various assignments are due. Below is an overview

• Assignment 1
  • Posted: During lecture 4.
  • Due: (find this info in the assignment itself)
  • Peergrading due: (one week after the handin time)
• Assignment 2
  • Posted: During lecture 8.
  • Due: (find this info in the assignment itself)
  • Peergrading due: (one week after the handin time)

Assignment 1 & 2

The lectures in this class run over 8 weeks. Each week, we will post a number of exercises. After a set of lectures, we will post an assignment. The assignment is a subset of the exercises. This means that, if you solve the exercises each week, the assignments will be easy. Since Assignments 1 and 2 will be written reports (IPython notebooks), summarizing the work contained in exercises preceding it.

Formalia regarding assignment 1-2

We will be grading your .ipynb file, it should be uploaded via http://peergrade.io/

• Assignments should be handed in in groups; see https://github.com/SocialComplexityLab/socialgraphs2021/wiki/Groups for details.
• For the delivery:

give the file any name, e.g. Assignment1.ipynb (it will be anonymized by the system anyway)

make sure that your code runs and renders all images, prints, etc. before you save your file and upload. We recommend restarting the kernel under 'Kernel' and then clicking Cell --> Run all before uploading.

double check that your file renders correctly before you upload it to http://peergrade.io/. Remember that you'll be annoyed to get bad evaluations because no-one could see your plots.

• Remember that the Notebook should be anonymous, so don't include your name and student ID.
• To help us navigate the Notebook, it's a good idea to repeat the question you're answering.
• Try to control the length of your notebook. While grading, we look at how you prioritize material and express yourself clearly and succinctly.
• Read the text carefully - make sure you understand the question. And make sure that you answer all sub-questions, etc. (It's easy to miss something, so be thorough).
• Do not solve all exercises in a single code cell. Split your code according to the questions
• The notebook is designed to contain your code, so do include it. But do keep it short & neat (minimize long outputs, etc.)
• Format your plots properly. Axes must be labeled, use %matplotlib inline, etc.
• Make sure there is text explaining each figure. Use scientific papers as a gold standard for how to comment on figures.
• Make sure that you use references when they're needed and follow academic standards.
• Be precise, write in objective language (avoid: "I think ...", "In my opinon...", etc.) - if you make an observation, support it with data.