Episode 4, Exercise 3 Image coordinates, Slicing and Projections

### Brief description
1. Use example data-sets with different imaging modalites to map the physical meaning of an array axis. E.g. Time is first axis then Y and X. Or ZYX. (Optional) Mention typical ordering patterns such as TZCYX and singleton dimensions and how to deal with them.
2. Introduce:
- numpy indexing to retrieve single pixel values / or together with `:` to effectively bind a dimension
- python slices (_start_, _stop_) and their use in numpy arrays to access spatial subregions (2D)
- mixture the two: to select axis aligned sub-slices from 3D data
3. Add the _step_ to effectively sub-sampling arrays. Exampels: Temproral and spatal. Negative step for mirroring
4. Intruduce mean, max, median projections and give examples applying them to example data sets.

### Learning objective(s)
[covered](https://github.com/carpentries-incubator/bioimage-analysis-python/wiki/Learning-Objectives#image-coordinates-slicing-and-projections)

### Volunteer(s)
@sommerc

Start with the issue brief and the linked learning objectives for Episode 4, Exercise 3. Create the exercise around image coordinates, NumPy indexing and slicing, subsampling, mirroring, and mean, max, and median projections using example imaging datasets; it is done when all listed learning objectives are covered.

Written by the indexing model from the issue text.