| Course Name | Year | Term | Period | Faculty / Graduate School | All Instructors | Credits |
|---|---|---|---|---|---|---|
| 12537:[2025 Fall]IR18-AE114 Data Literacy for Research Design (R) § 12538:RUIR-1805 Data Literacy for Research Design (R) | 2025 | Fall | Thu3 | College Of International Relations | KOVACS MATE | 2 |
Campus
Class Venue
Language
Course Outline and Method
Information for “+R-Jugyo” will be posted on the “Course News” on manaba+R by September 20. Be sure to check the “Course News” on manaba+R.
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).
Student Attainment Objectives
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.
Recommended Preparatory Course
Course Schedule
| Lecture/Instructor（When there are multiple instructors) | Theme |
|---|---|
| Keyword, References and Supplementary Information | |
| 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. |
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| 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. |
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| Week10(web-based) | Principles of information ethics |
Historical, legal, and social background of ethics, solving ethical problems. |
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| 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. |
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| Week14(web-based) | Examples of AI research and application + Course review (+R-Jugyo 20min VOD class) |
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.
|
Class Format
All 14 classes will be conducted via Zoom (online). The last class will include a 20 mins VOD (+R-Jugyo).
Recommendations for Private Study
Grade Evaluation Method
| Kind | Percentage | Grading Criteria etc. |
|---|---|---|
| Final Examination (Written) | ||
| Report Examination (A report to be submitted by the unified deadline) |
||
| Exams and/or Reports other than those stated above, and Continuous Assessment (Evaluation of Everyday Performance in Class) |
100 | In-class short tests - 50%;
|
Grade Evaluation Method （Note）
Advice to Students on Study and Research Methods
Textbooks
Textbooks （Frequency of Use, Note）
Reference Books
Reference Books （Frequency of Use, Note）
Web Pages for Reference
How to Communicate with the Instructor In and Out of Class(Including Instructor Contact Information)
Other Comments
URL:https://secure.ritsumei.ac.jp/students/pathways-future/course/curriculum.html/