Course review: Probability and statistics for Data Science by Alison

I have just completed Alison’s course entitled “Mathematics for Data Science” and found it to be quite a complex subject. In a previous blog post I reviewed module 1 of the course, “Linear algebra for data science”, with the link for that post being here:- https://medium.com/@tracyrenee61/course-review-linear-algebra-for-data-science-by-alison-c62577a36d53

I found both modules of Alison’s course to be quite difficult and intense, and I have been given better explanations in other courses. Nevertheless, I decided that it would be best to review module 2 of the course. In this review, I have taken key concepts learned and have converted them to Python when applicable.

Steps to solving a probability problem:-

1. Define the sample space: Determine all the possible outcomes of the experiment or random process being considered.
2. Define the event of interest: Specify the event for which you want to find the probability.
3. Assign probabilities to the outcomes: Decide how to assign probabilities to each outcome in the sample space.
4. Find the probability of the event: Use the definition of probability to find the probability of the event of interest. This may involve counting the number of outcomes in the event and…