| Academic Year |
2025Year |
School/Graduate School |
Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program |
| Lecture Code |
WSN21601 |
Subject Classification |
Specialized Education |
| Subject Name |
計算統計情報環境論 |
Subject Name
（Katakana） |
ケイサントウケイジョウホウカンキョウロン |
Subject Name in
English |
Computational Statistics |
| Instructor |
SUMIYA TAKAHIRO |
Instructor
(Katakana) |
スミヤ タカヒロ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, First Semester, 1Term |
| Days, Periods, and Classrooms |
(1T) Thur5-8 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Online (simultaneous interactive), Online (on-demand) |
| 20% Lecture, 80% Hands on |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
B
:
Japanese／English |
| Course Level |
5
:
Graduate Basic
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
Relational database; data analysis; data mining |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
We will discuss methods of statistical data analysis which require enormous amounts of calculations but can be realistically implemented due to the modern advanced information environment, such as bootstrapping, data mining, and visualization of data by high-resolution displays. This semester, we will focus in particular on data mining. |
| Class Schedule |
Introduction
SQL (1): Relational database management system and basic SQL query.
SQL (2): Basic SQL query (hands-on)
SQL (3): Aggregate functions and normalization of tables
SQL (4): Aggregate functions and normalization of tables (hands-on)
Data mining, Association rule (1): The basket data and apriori algorism
Data mining: Association rule (2): Execution of apriori algorism, pre-process of data
Data mining: Association rule (3): Execution of apriori alogrism, analysis by R
Simulation-based Methods (1) Simulation in Python
Simulation Techniques (2) Simulation in Python
Simulation Techniques (3) Jackknife and Bootstrap Method
Simulation Techniques (4) Jackknife and Bootstrap Method (hands-on)
Visualization of multi-dimensional data
Summary |
Text/Reference
Books,etc. |
Kyoritsu Shuppan, Deta Mainingu ("Data Mining"), FUKUDA et al. |
PC or AV used in
Class,etc. |
Handouts, Visual Materials, Microsoft Teams, moodle |
| (More Details) |
Textbooks, handouts, computer |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Class will advance accumulating assignments using computers. Be sure to have a good command of the assignments. |
| Requirements |
|
| Grading Method |
Assignment |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
Used languages: Japanese, English, or both Japanese and English The language(s) of the students will be considered, and the language will be determined in the first lesson. |
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. |