Introduction
This is my second course in MIT MicroMasters® Program in Statistics and Data Science. I learned probability theory when I was a graduate school student in a stochastic process course. The course helped me review many key probability concepts such as Random variables, Bayesian inference, Central limit theorem, Poisson processes, Markov chain, etc.
Overall, the course uses many real-life applications to illustrate the probability model so it is not too academic and boring even though it is with a lot of calculations and formulas. If you’re interested in what is probability truly, I recommend you take a look at the course.
Same as the last course, this course also required the students to do exercises, problems and exams; however, there is no coding project in this course. Following is my scores.
Suggestion
In this course, you have to do many calculations with pencil and paper. Try to use technology resources can help you to save a lot of time.
I found a useful website for calculations when I tried to solve course exercises including integration and differentiation provided by Microsoft. https://lnkd.in/g6iRAYUg
Summary
Everything is uncertain in our personal life and the business world, so it’s better to know how to model it to make better decisions. For example, whether you need to buy a lottery. The expectation value is negative. Therefore, you better not buy a lottery and you should spend the money on learning new skills.
My certification:
