Data Science for Strategic Advantage Session 1

Gaining an overview of data science and mastering the fundamental concepts of frequentist statistics

Created byYukio (幸夫) Sonoda (園田)

Last updated 11/2025

English

What you'll learn

Session 1 introduces frequentist statistics, covering populations, samples, distributions, CLT, estimation, and hypothesis testing.
Frequentist statistics, born 100 years ago, rely on assumptions like normality. This course explains these foundations and their practical limitations.
Hypothesis testing is debated; ASA warned of p-value misuse and advised avoiding “statistically significant.” This course teaches proper interpretation
Select courses and practice real-world exercises to gain data analysis skills for better decisions and actions.

Course content

6 sections • 11 lectures • 2h 24m total length

World Map of Data Science21:29
What is Data Science？:
Data science is a powerful weapon for decision-making and action. The goal of this course is not only to understand analytical methods, but also to acquire the practical ability to solve real-world problems.
The World Map of Data Science:
From statistics, which emerged a century ago, to the latest deep learning techniques, we will map a wide variety of analytical methods to their application domains. In doing so, we will draw a world map of data science and gain a bird’s-eye view of the entire field
About Me and My Connection to Data Analysis6:57
Instructor's Profile:
The instructor’s background and qualifications will be presented, along with an explanation of their experience and involvement with various data analysis techniques.

History of Statistics10:12
History of Statistics:
We will explore the origins of statistics in ancient civilizations and introduce the scholars and theories that established modern statistics in the early 20th century.
Purpose of Statistical Analysis5:15
Purpose of Statistical Analysis:
The goal of inferential statistics is to estimate the mechanisms underlying natural and social phenomena from observed data. In this course, you will learn the key concepts and terminology used in statistical analysis.
Population and Sample10:12
Population and Sample:
In statistics, the term generally refers to frequentist statistics. This approach explains how the characteristics of an entire phenomenon (the population) can be estimated from observed data (the sample), following the principles of frequentist statistical thinking.
Frequency Distribution and Probability Distribution31:08
Frequency Distributions and Probability Distributions:
We will introduce frequency distributions derived from observed data in natural and social phenomena, and then present representative probability density functions used to analyze them, along with their interrelationships.

Central Limit Theorem and confidence Interval17:30
Central Limit Theorem:
You will study the most important fundamental concept in statistics—the Central Limit Theorem—and experience the behavior of data through random number simulations.
Point Estimation and Interval Estimation:
You will learn how to estimate the population mean using both point estimation and interval estimation. The course provides a detailed explanation of the principle behind 95% interval estimation using the Central Limit Theorem.
Meaning of the 95% Confidence Interval:
We will clarify the true meaning of “95%” and address common misunderstandings about confidence intervals.

The Logic of Hypothesis Testing and Risk Control21:07
Concept of Hypothesis Testing:
We will explain the setup and framework necessary for understanding hypothesis testing.
Example of Hypothesis Testing:
Using a case study that examines whether product improvements have enhanced performance based on prototype data, we will introduce the principle of hypothesis testing through statistical significance.
Type I Risk and Type II Risk:
Hypothesis testing involves two types of risks with opposing characteristics. This course explains both, along with strategies for risk control.
Overview of Testing Methods:
A systematic diagram of hypothesis testing methods will provide a clear view of the overall framework.

The ASA's Statement and Trends in Hypothesis Testing14:55
Statement by the American Statistical Association:
In 2016, the American Statistical Association issued its Statement on p-values and Statistical Significance, warning against widespread misuse. In the same year, the scientific journal Nature published an article titled “Retire Statistical Significance.” This course will explain the content and implications of these publications.
Application and Limitations of Statistical Significance:
We will discuss the correct application of hypothesis testing methods based on statistical significance, highlight common mistakes, and examine the limitations of these approaches.
Trends in Hypothesis Testing:
There is growing interest in Bayesian statistics, which can estimate the probability that a hypothesis is true. However, it may take time before user-friendly analysis software becomes widely available.

Requirements

High school–level mathematics is sufficient, but it is not a prerequisite
To ensure accuracy, mathematical models will be presented. However, the focus will be on intuitive understanding and interpretation, with explanations primarily through graphs and diagrams.

Description

This course contains the use of artificial intelligence.

This course is the English version of the “Practical Data Science Lecture Series,” originally published in Japanese.
Artificial intelligence was used to assist with translation from Japanese to English and for narration of the explanations.
All course structure, slides, and explanatory content are entirely original works by the instructor.

In the first session of this course, you will gain three essential areas of knowledge and skill that will serve as the foundation for your entire learning journey.

1. An overview of the world of data science
We begin by mapping analytical techniques that range from classical statistics to the most advanced methods in artificial intelligence. This “technology map” is designed to give you a bird’s-eye view of the entire lecture series, helping you understand how different methods connect and where they can be applied. With this map in hand, you will be ready to embark on a structured and meaningful exploration of data science.

2. The fundamental concepts of statistics
The starting point of Session One is classical statistics, often referred to as frequentist statistics. While data science today encompasses many diverse approaches, frequentist statistics remain the bedrock of nearly all analytical methods. Here, you will firmly master the fundamental concepts—such as populations, samples, probability distributions, and estimation—that provide the logical framework for more advanced techniques you will encounter later.

3. The principles of hypothesis testing and its application to problem solving
Hypothesis testing is a powerful tool for determining whether differences exist between data, but its logic can be subtle and is often misunderstood. The American Statistical Association has even issued warnings about the misuse of “statistical significance.” In this session, you will learn the correct interpretation of hypothesis testing and discover how it can guide real-world problem solving, from research decisions to business strategy.

By the end of Session One, you will not only understand the theoretical foundations of data science but also appreciate how these principles can be applied to practical challenges.

Who this course is for:

This course is open to anyone who needs data analysis for their studies or work, those aspiring to become data analysts, and anyone interested in data science.
It is suitable for beginners in data analysis as well as intermediate learners who wish to review and strengthen their skills.

Data Science for Strategic Advantage Session 1

What you'll learn

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Course content

Introduction: Data Science for Problem Solving and Decision Making2 lectures • 28min

Foundations of Frequentist Statistics4 lectures • 57min

Central Limit Theorem1 lecture • 18min

The Logic of Hypothesis Testing1 lecture • 21min

The ASA’s Statement on Statistical Significance and p-values1 lecture • 15min

Summary of Session 12 lectures • 6min

Requirements

Description

Who this course is for: