Episode 2

### Brief description
I think the whole Episode 2 can be pooled into one big exercise that logically builds up to answer all learning objectives. I swapped point 2.1 Types of Images and 2.2 Image as Matrix. I would like to get input on what not to forget.

### Learning objective(s)

- [Image as a Matrix](https://github.com/carpentries-incubator/bioimage-analysis-python/wiki/Learning-Objectives#image-as-a-matrix)
Provide 3 images (8 and 16 bit grayscale, RGB). All load RGB image and explore the shape. Then each student loads different grayscale image to their neighbor and after looking at shape (explanation where the last part went) will be asked to explore the dtype, data. Print pixel values, min, max and mean
DATA: Would suggest using a mock image of not-too-many pixels for easy work and explanation

- [Types of Images](https://github.com/carpentries-incubator/bioimage-analysis-python/wiki/Learning-Objectives#types-of-images)
Ask students to look and discuss with neighbor if/why their numbers differ. Explain difference in value range. Brings us to the last point.

- [Bit Depth and pixel data type](https://github.com/carpentries-incubator/bioimage-analysis-python/wiki/Learning-Objectives#bit-depth-and-pixel-data-type)
Having a look at the data size, value range and calculations with 8 vs 16 bit grayscale to showcase potential issues - Here I would like to get input on all the possible problematic parts to keep in mind not to forget any

### Volunteer(s)
@terezbelinova will gladly take it