Course Name Year Term Period Faculty / Graduate School All Instructors Credits
15052:[2025 Fall]Foundations of Data Engineering (G1) § 15053:RUIR-1807 Foundations of Data Engineering (JD) 2025 Fall 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

With the advancement of ICT (Information and Communication Technology), a wide variety of data is being collected through the Internet from various devices such as smartphones and IoT devices. This course broadly covers the fundamental technologies for analyzing, managing, and protecting this large-scale data (big data) with computers. It includes learning about data representation within computers, the basics of programming and algorithms for instructing computers on how to handle data, and provides an overview of the process of data analysis using computers.
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 Science and AI,” another liberal arts course.
Students watch on-demand materials, check their understanding in each class session, and complete exercises and Project Based Learning (PBL) assignments to ascertain their level of achievement of the attainment objectives.

Student Attainment Objectives

・Students must be able to comprehend an overview of technologies for gathering, processing and storing data.
・Students must be able to comprehend the foundations of data representation for handling data with computers.
・Students must be able to gather necessary data from websites and edge devices.
・Students must be able to comprehend how to extract data from databases.
・Students must be able to visualize data and derive its implications.
・Students must be able to comprehend the foundations of programming.
・Students must be able to process data gathered.
・Students must be able to comprehend the foundations of IT security.
・Students must be able to analyze data and understand the background and implications of events that are occurring based on the results of the analysis.

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

Big Data and Data Engineering
【+R Class】

The advancement of ICT (Information and Communication Technology), Big data

2

Data representation (1)

Data handled using computers, units of information (bits/bytes), binary numbers

3

Data representation (2)

Binary number, hexadecimal number, prefix (KB (kilobyte), GB (gigabyte)), floating point number, character code

4

Data representation (3)

Array, graph, image data, audio data

5

Programming

Variables, assignments, conditional branching and repetition, functions, arguments

6

Algorithms (1)

Algorithms, algorithm representations, data sorting

7

Algorithms (2)

Advanced data sorting (merge sort), data exploration

8

Algorithms (3)

Algorithms and computational complexity, mathematical optimization

9

Data collection

IoT (Internet of Things), edge devices, scraping, web crawlers

10

Data formatting/processing

Data cleansing, filtering processing, annotation, joining processing

11

Database

Relational database, table definition, ER diagram, data manipulation language

12

IT security

Three elements of security (confidentiality, availability and integrity), data encryption and decryption

13

Data analysis in practice (1)

Programming practice

14

Data analysis in practice (2)

Web scraping practice

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

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