Data Science Course

Key points in a data science course (deep understanding) :

1. Type of variables (numerical, categorical - continuous, discrete)
2. What are basic operations to do once we have a dataset (calculate correlations, distribution plot, counts, info ...)
3. What kind of correlation de we have (Numerical vs numerical, numerical vs categorical, categorical vs categorical)
4. [Why do we use log (logarithmic transformation)] ("https://dev.to/rokaandy/logarithmic-transformation-in-linear-regression-models-why-when-3a7c#:~:text=The%20Why%3A,may%20also%20be%20skewed%20negatively.")
5. [How to interpret correlation coefficient ?] ("https://fr.khanacademy.org/math/be-5eme-secondaire2h2/x741278364a599ec1:statistiques/x741278364a599ec1:nuage-de-points-et-correlation/a/correlation-coefficient-review")
6. Let's see a datacamp full path and get the best out of it.
7. [Very interesting cloud plots and materials below](https://www.r-bloggers.com/2021/07/ggdist-make-a-raincloud-plot-to-visualize-distribution-in-ggplot2/)