While I am going through all the lectures related to the probability, combinatorics etc., I am still confused as to where exactly these concepts gets applied in the real life data science? Is it during data cleansing or identifying missing values? I am curious to know the practical situations where we need to apply these mathematics?
Thanks for your help in getting this doubt cleared.
Thanks for reaching out!
We love questions like these. Generally, the idea here was to introduce people who are unfamiliar with statistics with the basic notions of distributions and show them how many different ways data can behave. Then, we realized that to introduce the Probability Mass Function (which we call the PDF for simplicity) of the Binomial Distribution, you needed to know Combinatorics, so we included that as well.
Then, we felt like any novice needed to learn the basic concepts of Bayesian Inference, sets, conditional probabilities and so on, in order to properly extract insight from summary tables. Of course, all of this combined with statistics enables us to interpret the regression results. Since regressions are a form of supervised machine learning, it made sense (to us at least), that anybody who wants to do proper data science, should be familiar with its foundation. And because probability lays the groundwork for statistics, and statistics, in turn, build the pillars on which data science is built, we decided to start this course from the very beginning.
That being said, if you are familiar with the topics already, feel free to skip lectures, sections and even “parts” of the course. And if you happen to come across an unfamiliar term, please check any of the videos you missed. Chances are, one of these lectures will be devoted to exactly that.
Hope this helps!
The 365 Team