授業科目名 年度 学期 開講曜日・時限 学部・研究科 全担当教員 単位数
15998:IR18‐AE114 Data Literacy for Research Design(R) § 15999:RUIR-1805 Data Literacy for Research Design(R) 2024 秋セメスター 金5 国際関係学部 KOVACS MATE 2

キャンパス

衣笠

授業施設

諒友館RY201号教室

授業で利用する言語

English

授業の概要と方法

This course provides the fundamental background required for understanding and handling research data as well as an introduction to various ways data is collected, analyzed, and modeled in contemporary information systems research. The topics begin with a background of the information age and the role of data in that history up to the present. These historical and current developments are followed by the foundations of data collection through experiment, measurement, sampling, and other typical means, including linking these to the scientific method, scientific processes, and research. The next section covers basic methods of tabulating, visualizing, and completing initial statistical analysis of data.

Increasingly, data science and the various systems employed to collect, handle, and use them are pushing the envelope of ethical boundaries. Before delving into contemporary means of big data analysis, this course gives a background in ethics for information science. Finally, students gain an understanding of how the topics learned during this course can be understood through examples drawn from the burgeoning field of Artificial Intelligence.

This course is designed to allow students of all backgrounds to approach the material from a non-engineering point of view and thereby give them a structure on which to understand current developments in data science. In principle, all classes are to be conducted web-based (live-streamed via Zoom).

受講生の到達目標

Students who successfully complete this course will:

1) understand and able to describe the information age and the role of data in contemporary systems,
2) able to describe data in the context of its collection for, and in relation to scientific methods and data-driven research,
3) able to utilize basic forms of data tabulation, compiling, sorting, and applying basic statistical measures,
4) understand the concepts of ethics and how they apply to data science, information systems, and data analysis and modeling.

事前に履修しておくことが望まれる科目

授業スケジュール

授業回数/
担当教員（複数担当の場合） 		テーマ
キーワード・文献・補足事項等
Week1(web-based)

Introduction, course structure and requirements, definitions.

Introduction to the main course topics: the information age; understanding data as a product of science, engineering, and research; fundamentals of data and data analysis; ethics of research data and data science; applications of the concepts to contemporary data analysis.

Week2(web-based)

Introduction: Development of information technology and its impact on society

Introduction, current status of information technology in households and individuals, current status of information technology in enterprises.

Week3(web-based)

Applications of research and data science in industry

Database marketing, big data, one-to-one marketing, web marketing, knowledge management.

Week4(web-based)

Epistemology

Empirical, deductive, hypothesis testing vs. inductive, hypothesis generating approach to science. Falsification vs. verification. Realism vs. constructivism.

Week5(web-based)

Experimental design and types of models

Basic assumptions, design components, causation (control groups). Quasi-experimental designs. Description vs. analysis-test vs. post-test (stimulus). NTV-model: N number of cases, T points in time, V variables.

Week6(web-based)

Theory and Hypotheses

What is the role of theory? What is a hypothesis? Process: definition of variables, measurement, operationalization, data collection, data analysis. Deterministic vs. probabilistic statements.

Week7(web-based)

Data storage and tabulation

Tabulation of data, sorting of data, tabular data, common data formats, principles of databases.

Week8(web-based)

Data representation and visualization

Data representation, data comparison, data visualization, Charts and simple descriptive statistics with the help of spreadsheets. Linear and non-linear relations between two variables. Variance and co-variance. Whisker plots.

Week9(web-based)

Data types and distributions

Types of data, distribution and representative values of data, variation of data, regression and correlation, correct understanding of statistical information.

Week10(web-based)

Principles of information ethics

Historical, legal, and social background of ethics, solving ethical problems.

Week11(web-based)

Ethics topics for research, data, and information systems

Rights and responsibilities, ethical considerations of rights to privacy, freedom of information.

Week12(web-based)

Principles of information security

Confidentiality, integrity, access, non-repudiation, authentication, and their implementation.

Week13(web-based)

Introduction to principles of AI

AI background and objectives, models of inference and learning, applications of AI in the real world and how it benefits society.

Week14(web-based)

Examples of AI research and application

Understanding the principles of the course based on specific research and application of AI and Big Data systems, value added by AI in real systems, data handling for AI, ethical responsibilities and considerations.

Week15(web-based)

Course summary and review

Review of weeks 1-14 and attainment objectives

授業実施形態

All classes will be conducted via Zoom (online).

授業外学習の指示

To successfully complete the course, students are expected to spend an average of 2-3 hours every week in addition to the class hours to study course materials and work on their reports.

成績評価方法

種別 割合(%) 評価基準等
定期試験（筆記）

レポート試験
（統一締切日を締切とするレポート）

上記以外の試験・レポート、平常点評価
（日常的な授業における取組状況の評価）		 100

Participation - 5%;
In-class short tests - 45%;
Final report - 50%.

成績評価方法(備考)

Besides in-class short tests, students are required to submit a report and a poster by Week 15 (details will be given to the students at Week 10). Students who do not participate in more than 5 classes without a legitimate reason will automatically receive an "F" for the course.

受講および研究に関するアドバイス

教科書

教科書(使用頻度、その他補足)

Instead of a textbook, the necessary materials will be distributed or the web pages to be referred will be introduced.

参考書

参考書(使用頻度、その他補足)

参考になるwwwページ

授業内外における学生・教員間のコミュニケーションの方法

学生との直接対話,その他(教員より別途指示）

備考

For any consultations regarding the course, students should request appointments via e-mail sent to kovacsm@fc.ritsumei.ac.jp .
【科目ナンバリング・カリキュラムマップはこちらから/Click here to see the Curriculum-Map and Course-Numbering】
URL:http://www.ritsumei.ac.jp//students/pathways-future/course/curriculum.html