Exercise Badges

Investigating the association between air pollutant concentrations and temperature changes

This project contains a report analyses the overall and seasonal trends of equivalent Black Carbon with Aethalometer Measurements and Atmospheric Particles-DMPS concentrations in Ispra, Italy. Based on a comparison between concentrations and temperature changes, It will be determined how the impact of air pollutant's concentration correlated with modification of temperature over 5 years in Ispra.

This project provides some structure for your open data project in the MADE module at FAU. This repository contains (a) a data science project that is developed by the student over the course of the semester, and (b) the exercises that are submitted over the course of the semester. Before you begin, make sure you have Python and Jayvee installed. We will work with Jupyter notebooks. The easiest way to do so is to set up VSCode with the Jupyter extension.

To get started, please follow these steps:

1. Create your own fork of this repository. Feel free to rename the repository right after creation, before you let the teaching instructors know your repository URL. Do not rename the repository during the semester.
2. Setup the exercise feedback by changing the exercise badge sources in the README.md file following the patter ![](https://byob.yarr.is/<github-user-name>/<github-repo>/score_ex<exercise-number>). For example, if your user is myuser and your repo is myrepo, then update the badge for exercise 1 to ![](https://byob.yarr.is/myrepo/myuser/score_ex1). Proceed with the remaining badges accordingly.

Project Work

Your data engineering project will run alongside lectures during the semester. We will ask you to regularly submit project work as milestones so you can reasonably pace your work. All project work submissions must be placed in the project folder.

Exporting a Jupyter Notebook

Jupyter Notebooks can be exported using nbconvert (pip install nbconvert). For example, to export the example notebook to html: jupyter nbconvert --to html examples/final-report-example.ipynb --embed-images --output final-report.html

Exercises

During the semester you will need to complete exercises using Jayvee. You must place your submission in the exercises folder in your repository and name them according to their number from one to five: exercise<number from 1-5>.jv.

In regular intervalls, exercises will be given as homework to complete during the semester. Details and deadlines will be discussed in the lecture, also see the course schedule. At the end of the semester, you will therefore have the following files in your repository:

1. ./exercises/exercise1.jv
2. ./exercises/exercise2.jv
3. ./exercises/exercise3.jv
4. ./exercises/exercise4.jv
5. ./exercises/exercise5.jv

Exercise Feedback

We provide automated exercise feedback using a GitHub action (that is defined in .github/workflows/exercise-feedback.yml).

To view your exercise feedback, navigate to Actions -> Exercise Feedback in your repository.

The exercise feedback is executed whenever you make a change in files in the exercise folder and push your local changes to the repository on GitHub. To see the feedback, open the latest GitHub Action run, open the exercise-feedback job and Exercise Feedback step. You should see command line output that contains output like this:

Found exercises/exercise1.jv, executing model...
Found output file airports.sqlite, grading...
Grading Exercise 1
	Overall points 17 of 17
	---
	By category:
		Shape: 4 of 4
		Types: 13 of 13