Course Name Year Term Period Faculty / Graduate School All Instructors Credits
15050:[2025 Spring]Foundations of Data Science and AI (G1) § 15051:RUIR-1806 Foundations of Data Science and AI (JD) 2025 Spring Tue7 College　Of　Law,College　Of　International　Relations,College　Of　Policy　Science,College　of　Image　Arts　and　Sciences,College　of　Information　Science　and　Engineering NISHIKADO HIDETO 2

Campus

*

Class Venue

施設なし

Language

English

Course Outline and Method

Due to the advancement of ICT (Information and Communication Technology), a wide variety of data is being collected and utilized in many ways as big data. Consequently, the importance of Data Science, a methodology for processing and analyzing data to extract useful information, is increasingly recognized.

In this course, we will learn the basics of data science, including the fundamentals of data analysis, various methods, and the mathematics necessary for understanding these concepts. Additionally, we will study Artificial Intelligence (AI), which has rapidly developed in recent years through the use of large-scale data, and its foundation in machine learning. The course also includes practical exercises to master the application of these technologies.

This course covers the model curriculum (applied basics level) of the Ministry of Education, Culture, Sports, Science and Technology’s (MEXT), “Mathematics, Data Science and AI Education Program Accreditation System” in conjunction with “Foundations of Data Engineering,” another liberal arts course.
Students watch on-demand materials, check their understanding in each class session, and complete tasks such as Project Based Learning (PBL) assignments to ascertain their level of achievement of the attainment objectives.

Student Attainment Objectives

The objective of this course is for students to understand and acquire knowledge related to mathematics, data science, and AI regarding the following topics.
・Students must be able to comprehend the relationship between a data-driven society and data science.
・Students must be able to comprehend the foundations of mathematics required in data and AI utilization.
・Students must be able to comprehend methods of implementing and designing data analysis.
・Students must be able to analyze data gathered.
・Students mut be able to comprehend changes in AI and its technological background.
・Students must be able to comprehend points of argument that should be considered for AI to be accepted by society.
・Students must be able to comprehend the basic concepts of machine learning (supervised and unsupervised) and deep learning.
・Students must be able to carry out fundamental AI building and operation.

Recommended Preparatory Course

Any Data Science +R Program (Basics) courses being offered in students’ affiliated colleges.

Course Schedule

Lecture/Instructor（When there are multiple instructors) Theme
Keyword, References and Supplementary Information
1

Data-driven society and data science
【+R Class】

Data-driven society, Society 5.0

2

Data description

Data classification (qualitative data, quantitative data), basic statistics (mean value, median value), scatter indicators (variance, standard deviation)

3

Foundations of data analysis (1)

Histgram, box-whisker plot

4

Foundations of data analysis (2)

Scatter plot, covariance, correlation coefficient

5

Foundations of mathematics (1)

Function, composite function, differentiation, integration

6

Foundations of mathematics (2)

Probability, permutations and combinations, probability distribution

7

Data analysis methods (1)

Regression analysis, simple regression model, least squares method, coefficient of determination

8

Data analysis methods (2)

Multiple regression model, logistic regression analysis, cluster analysis

9

AI and society

History of AI, various areas of AI, AI and games, deep learning and real-world problems

10

Foundations of machine learning

Learning model, supervised/unsupervised learning, generalization ability, model selection

11

Foundations of deep learning

Neural network principles, parameter learning

12

Extending neural networks

Convolutional neural networks (CNN), recurrent neural networks (RNN), graph neural networks (GNN), attention mechanisms and transformers

13

Machine learning practice (1)

Development and execution environment for machine learning, cloud computing, Machine learning with neural networks

14

Machine learning practice (2)

Class classification using convolutional neural network

Class Format

全ての授業をメディア授業（オンデマンド型）で実施

Recommendations for Private Study

授業は全てオンデマンド教材を使用して行われます。
自身の修得度に合わせて何度も学習し、各回修得が求められる知識や技能を着実に学習してください。

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

各回設定される小テストを以て総合的に評価する

Grade Evaluation Method （Note）

Advice to Students on Study and Research Methods

Textbooks

Textbooks （Frequency of Use, Note）

Reference Books

Title Author Publisher ISBN Code Comment
応用基礎としてのデータサイエンス　改訂第2版 北川源四郎 他（編）,　赤穂昭太郎 他（著） 講談社 978-4-06-538618-7

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)

Communication Paper,Interactive Sheet,Learning Management System （manaba+R）,Other (Separate instructions will be provided)

Other Comments

【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:https://secure.ritsumei.ac.jp/students/pathways-future/course/curriculum.html/