Grading criteria for the final assignment
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**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.