Statistics Fundamentals (1/9) Introduction

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!

  Statistical Analysis is now applied in various scientific and practical fields. 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.

 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 the first chapter of Statistics Fundamentals, a comprehensive program for learning the basics of statistics. This series will consist of the following 9 courses, including this one.

    1. Introduction ( This course!)

    2. Descriptive Statistics

    3. Probability

    4. Probability Distribution

    5. Sampling

    6. Estimation

    7. Hypothesis Testing

    8. Correlation & Regression

    9. ANOVA

This introduction course does not contain coding lectures, but other courses in this program have Python tutorial lectures. They cover basic Python coding, so 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 learners who have not installed Python and related tools, please use other references.

   This course is an introductory course in Statistics Fundamentals and covers the following topics.

       1. What is Statistics?

       2. Type of Statistics

       3. What is Data?

       4. Stevens’ Typology

       5. Independent and Dependent Variables

Photo and Pictures

– bundestag-369049_1920 (clareich, Pixabay)

– digital-marketing-1725340_1920 (Photo Mix, Pixabay)

– doctor-563429_1920 (Darko Stojanovic, Pixabay)

– hit-1407826_1280 (Keith Johnson, Pixabay)

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