Statistics Fundamentals (2/9) Descriptive Statistics

person icon Takuma Kimura

Statistics Fundamentals (2/9) Descriptive Statistics

Basic Statistics for Data Science

updated on icon Updated on Sep, 2023

language icon Language - English

person icon Takuma Kimura

architecture icon Development,Data Science and AI ML,Data Analysis


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Course Description

Welcome to Statistics Fundamentals 2, descriptive statistics! 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 second 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 descriptive statistics and Python coding tutorials. They cover Python basics and thus are easy to follow. But this program is not a Python course. So to install Python and prepare related environments, please refer to other materials. If you are an absolute beginner in statistics, I recommend taking our first course named Introduction.

Descriptive statistics is the process of summarizing the data by various metrics. By using knowledge of descriptive statistics, we can understand and explain the data effectively. Specifically, we can use various metrics that are very useful to capture the whole picture of the data and relative positions of a particular datapoint.

Conceptually, descriptive statistics and data visualization are different, but they are closely related. We can understand and display data in more effective ways by combining them rather than using only one of them. So this course covers data visualization also.

This course contains the following topics: central tendency, variability, relative position, and data visualization.

Central tendency is a central value of a dataset, and it is a representative value of the dataset. The average is one of them, but it is just one of them. Other than average, there are various types of center values. We need to use appropriate types of center values depending on data characteristics.

Variability measures represent how much disperse a dataset is. Variability is important. For example, even if the two countries' average income is the same, income equality can be widely different between the two countries.

While variability relates to the whole dataset, relative position relates to each case, each data point. It represents how high or low a datapoint is relative to other datapoints in the dataset.

Data visualization is an effective way to understand data intuitively. And by visualizing data, we can explain the results of our analysis clearly. 

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


What will you learn in this course:

  • Central Tendency
  • Variability
  • Relative Position
  • Data Visualization


What are the prerequisites for this course?

  • None
Statistics Fundamentals (2/9)  Descriptive Statistics


Check out the detailed breakdown of what’s inside the course

Display Data
7 Lectures
  • play icon Frequency Table 04:15 04:15
  • play icon Create Frequency Table with Python 06:26 06:26
  • play icon Stem and Leaf Diagram 02:32 02:32
  • play icon Stem and Leaf Diagram with Python 01:41 01:41
  • play icon Histogram 09:21 09:21
  • play icon Create Histograms with Python 05:30 05:30
  • play icon Dot Plot 01:28 01:28
Central Tendency
11 Lectures
7 Lectures
Relative Position
5 Lectures
Data Visualization
16 Lectures

Instructor Details

Takuma Kimura

Takuma Kimura

Profile Summary:

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.

Professional Details:

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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