Grading criteria for the final assignment

The grading is based on a typical 0-5 scale. The grade is based on a total of 50 points:

25 points for demonstrating major analysis steps/ functionality:

  • Finding relevant datasets

  • Reading and manipulating data

  • Analyzing data

  • Visualizations (maps, graphs)

  • Is the code written in a modular way (avoid repetition eg. using functions and for-loops)

  • Does everything work as it should

  • The “level of difficulty” in the analysis task is taken into account in the assessment (however, choose a challenge that you feel comfortable of doing)

25 points for the report and overall documentation of the work

  • is there a general description in about the research problem / purpose of the tool?

  • is the usage of the tool / available functions described and demonstrated clearly?

  • are all input data and output results (maps, graphs) presented and explained clearly?

  • Is the code easy to read and well-formatted (following the PEP8 guidelines)

Extra points available for other merits in the work:

  • something in the work is exceptionally well done

  • some problem in the code is solved in a “smart” way

  • the visualizations are exceptionally good

  • the written parts is thorough and relies on published research on the topic

This grading criteria applies to submissions which contain the following sections:

  1. Introduction - Research questions (Overview: What are you studying/research questions? What data do you use? What methods?)

  2. Data acquisition (Fetching data, subsetting data, storing intermediate outputs etc.)

  3. Data analysis (Analytical steps required to produce the results)

  4. Visualization (Visualizing main results and other relevant information as maps and graphs)

  5. Results / conclusions (What did your analysis reveal?)

You can write your code into python script files and /or jupyter notebook files. You can freely organize your final work into one single file, or several files (for example, write your own functions into a separate .py file and apply them in one or several jupyter notebook .ipynb files.

The workflow should be repeatable and well documented. In other words, anyone who gets a copy of your repository should be able to run your code, and read your code.