Welcome to Statistics Fundamentals! 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!

As a science field, statistics is a discipline that concerns collecting data, and mathematical analysis of the collected data, describing data and making inference from the data. Using statistical methods, we can obtain insights from data, and use the insights for answering various questions and decision making.

Statistical Analysis is now applied in various scientific and practical fields. It is essential in both natural science and social science. In business practice, statistical analysis is applied as business analytics such as human resource analytics and marketing analytics. And now, it is an essential tool in medical practice and government policymaking. Besides, baseball teams utilize it for strategy formation. It is well known a SABRmetrics.

However, if we do not use appropriate methods, statistical analysis will result in meaningless or misleading findings. To obtain meaningful insights from data, we need to learn statistics both in practical and theoretical viewpoints. This course intends to provide you with theoretical knowledge as well as Python coding. Theoretical knowledge enables us to implement appropriate analysis in various situations. And it can be a useful foundation for more advanced learning.

This course is a comprehensive program for learning the basics of statistics. It consists of the 9 sections. They cover theory and basic Python coding. Even if you do not have Python coding experience, I believe they are easy to follow for you. But this program is not a Python course, so how to install Python and construct environment is not covered in this course.

This course is designed for beginners, but by learning with this course, you will reach an intermediate level of expertise in statistics. Specifically, this course covers undergraduate level statistics. After enrollment, you can download the lecture presentations, Python code files, and toy datasets in the first lecture page.

I’m looking forward to seeing you in this course!

*In some videos, the lecturer says "... will be covered in later courses", but it should be "later sections."

Table of Contents

1. Introduction

2. Descriptive Statistics:

3. Probability

4. Probability Distribution

5. Sampling

6. Estimation

7. Hypothesis Testing

8. Correlation & Regression

9. ANOVA