| Academic Year |
2024Year |
School/Graduate School |
Education and Research Center for Artificial Intelligence and Data Innovation |
| Lecture Code |
8J020001 |
Subject Classification |
Specialized Education |
| Subject Name |
データエンジニアリング基礎 |
Subject Name
（Katakana） |
データエンジニアリングキソ |
Subject Name in
English |
Basics of Data Engineering |
| Instructor |
OKAMURA HIROYUKI,HAYASHI YUSUKE |
Instructor
(Katakana) |
オカムラ ヒロユキ,ハヤシ ユウスケ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
2nd-Year, Second Semester, Intensive |
| Days, Periods, and Classrooms |
(Int) Inte：Online |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| Lecture, Practice |
| Credits |
1.0 |
Class Hours/Week |
|
Language of Instruction |
B
:
Japanese／English |
| Course Level |
2
:
Undergraduate Low-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
2nd year |
| Keywords |
Data representation, programming, algorithms, database, security |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | This is a required subject for AI/Data science advanced basic program. This class provides basic knowledge and skill for data engineering. |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
The objectives of this class are for students to understand the basic concepts of data engineering, and to learn the programming for data processing with Python. |
| Class Schedule |
lesson1 Big data and data engineering
lesson2 Data representation
lesson3 Programming
lesson4 Algorithm 1
lesson5 Algorithm 2
lesson6 Database
lesson7 Data processing
lesson8 Security
Quiz |
Text/Reference
Books,etc. |
There is no textbook. |
PC or AV used in
Class,etc. |
|
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
There are on-demand videos and practices at every lecture. |
| Requirements |
This is a required subject for AI/Data science advanced basic program. This class provides basic knowledge and skill for data engineering. |
| Grading Method |
Submitted reports and results of quizzes. |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
This lecture is opened for the 3rd terms. We will upload class materials (slides, videos, assignments and quizzes) for each lesson to the LMS every week. The submission deadline for assignments and quizzes should be submitted until the materials on the next lesson are published. The detailed date for uploading will be announced through Momiji, Moodle and Teams. |
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |