Explain how to read a boxplot

Box-plots are frequently used during the MOOC but a person without a minimal formation in statistics might not understand how to read them.
I think the soon-to-be-added notebook on score distributions gives us a good opportunity to add a brief explanation/illustration on how to do so.

Using the same example as presented in #416 to illustrate overlapping score distribution, I propose adding both the following code and figures:

```python
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

plt.rcParams["figure.figsize"] = (10,7)
plt.rcParams.update({'font.size': 16})
np.random.seed(12)

model_1 = pd.DataFrame([0.65,0.7,0.71,0.73,0.78],columns=['model_1'])
model_2 = list(np.random.normal(0.74, 0.07, 5))
model_2 = pd.DataFrame(model_2,columns=['model_2'])
models = pd.concat([model_1,model_2], axis=1)

bins=[0.6,0.64,0.68,0.72,0.76,0.8,0.84]
fig, ax1 = plt.subplots()
ax1.hist(models, bins=bins, label=['model_1', 'model_2'])
ax1.set_ylabel("frequency")
ax1.set_xlabel("test_score")
ax1.legend()
plt.tight_layout()
_ = plt.title("Overlapping score distributions")
```


```python
color = {"whiskers": "black", "medians": "black", "caps": "black"}

models.plot.box(vert=False, color=color)
_ = plt.title("Computation of multiple scores using a boxplot")
```


Having both things on the same notebook would show how useful a box-plot is when comparing several distributions at once.

What do you think?