Statistics Fundamentals (3/9) Probability
Basic Statistics for Data Science
Updated on Sep, 2023
Language - English
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
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
What will you learn in this course:
- Permutation and Combination
- Set Theory
- Conditional Probability.
What are the prerequisites for this course?
Check out the detailed breakdown of what’s inside the course
Permutation & Combination
- Factorial 08:43 08:43
- Permutation 03:02 03:02
- Combination 05:56 05:56
Dr. Takuma Kimura is an internationally recognized scholar in business and management fields. His expertise includes research in organizational behavior, and practical business analytics in human resource management and marketing. He engaging in education and consulting of these subjects in universities and industrial companies.
He published more than 10 academic papers in internationally prominent journals such as Journal of Business Ethics, International Journal of Management Reviews, Industrial Marketing Management, etc. His papers record more than 400 citations.
He is awarded as one of the World Top Reviewers from Publons, and as a Recognized Reviewer from European Management Journal.
He is technically skilled for Statistical Analysis, Machine Learning, Data Science, Qualitative Analysis. And he has abundant knowledge in management theory, especially in organizational behavior and psychology.
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