Statistics Fundamentals (3/9) Probability

Welcome to Statistics Fundamentals 3, probability. This course is for beginners who are interested in statistical analysis. And anyone who is not a beginner but wants to go over from the basics is also welcome!

This course is the third chapter of Statistics Fundamentals, a comprehensive program for learning the basics of statistics. This series will have these 9 courses, including this one. This course contains theoretical explanations of probability and Python coding tutorials. They cover Python basics and thus are easy to follow. But this program is not a Python course. So installation and preparation of related environments are not covered in this course. If you are an absolute beginner in statistics, I recommend taking our first course named Introduction.

Probability represents how likely an event occurs in general or in a given condition. Usually, we carry out various decision makings based on probability. In some cases, the probability is objective, but in other cases, it is our subjective evaluation. Using the theory of probability and necessary data, we can calculate and analyze the probabilities of various kinds of events. And using probability theory, we can calculate the probabilities of the cases where multiple events combined intricately. Probability theory is a useful framework for advanced analysis, such as multivariate statistical analyses, and predictive analyses using machine learning.

This course contains the following topics.

    1. Permutation and Combination

    2. Set Theory

    3. Probability

    4. Conditional Probability.

Permutation and combination relates to calculating the number of patterns and proportion of an event in a certain situation. And they are essential for understanding set theory.

Set theory relates to defining a set which is a collection of objects. And it relates to counting the frequency of the elements included in a set.

The main parts of this course are probability theory and conditional probability. Here, essential constructs such as statistical independence and Simpson’s paradox will be explained. They are useful to understand and calculate probability accurately. 

Our lectures in conditional probability will cover Bayes’ Theorem. It is now applied in various practical fields, such as predictive analysis and natural language processing. In this course, we explain Bayes’ Theorem by using a case of employee churn prediction.

This course's contents are essential for carrying out univariate and multivariate analysis. Some of the lectures contain mathematical explanations, but you will find they are easy to follow. I’m looking forward to seeing you in this course!

*Course Image: Magic Creative from Pixabay