Data Science for Strategic Advantage Session 1

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

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.

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 scienceWe 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 statisticsThe 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 solvingHypothesis 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.

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