Course Name Year Term Period Faculty / Graduate School All Instructors Credits
18163:IR18-AE114 Data Literacy for Research Design (R) § 18164:RUIR-1805 Data Literacy for Research Design (R) 2022 Fall Thu5 College　Of　International　Relations KOVACS MATE 2

Campus

KIC

Class Venue

KOSHINKAN　KS201

Language

English

Course Outline and Method

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. Understanding of and ability to describe the information age and the role of data in contemporary systems.
2. Ability to describe data in the context of its collection for, and relation to scientific methods and data-driven research.
3. Ability to utilize basic forms of data tabulation, compiling, sorting, and applying basic statistical measures.
4. Understanding of 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

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

Class Format

[BCP Level 1 or 2]
In principle, all classes are to be conducted web-based (live-streamed via Zoom). Details will be announced on the manaba+R course page.

[BCP Level 3 or 4]
In principle, all classes are to be conducted web-based (live-streamed via Zoom). Details will be announced on the manaba+R course page.

※For more information on Ritsumeikan University's BCP Level, please refer to the following URL, Point No.1 “Measures to prevent spread of the novel coronavirus (COVID-19).”
http://en.ritsumei.ac.jp/news/detail/?id=475

Recommendations for Private Study

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.

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

Participation and in-class activity;
In-class short tests;
Final report.

Grade Evaluation Method （Note）

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

The evaluation method does not depend on the BCP level.

Advice to Students on Study and Research Methods

Textbooks

Textbooks （Frequency of Use, Note）

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

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)

Talk with Students,Other (Separate instructions will be provided)

Other Comments

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