| 年度 |
2024年度 |
開講部局 |
教養教育 |
| 講義コード |
30104001 |
科目区分 |
情報・データサイエンス科目 |
| 授業科目名 |
Fundamental Data Science |
授業科目名
（フリガナ） |
|
| 英文授業科目名 |
Fundamental Data Science |
| 担当教員名 |
NUNES TENDEIRO JORGE |
担当教員名
(フリガナ) |
ナヌッシュ テンデイル ジョージ |
| 開講キャンパス |
東広島 |
開設期 |
1年次生 後期 ４ターム |
| 曜日・時限・講義室 |
(4T) 集中：オンライン |
| 授業の方法 |
講義 |
授業の方法
【詳細情報】 |
|
| Mainly video lectures (including practical assignments) |
| 単位 |
2.0 |
週時間 |
|
使用言語 |
E
:
英語 |
| 学習の段階 |
1
:
入門レベル
|
| 学問分野（分野） |
25
:
理工学 |
| 学問分野（分科） |
01
:
数学・統計学 |
| 対象学生 |
B1 |
| 授業のキーワード |
basic data science, statistical methods, practical exercises using R and Excel |
| 教職専門科目 |
|
教科専門科目 |
|
教養教育での
この授業の位置づけ | Acquire basic knowledge and skills for data science |
|---|
| 学習の成果 | 1. To acquire basic knowledge, skills, and attitudes regarding information science and data science, and thereby to be able to process data and handle information appropriately.
2. To understand and be able to explain information ethics and social challenges for data usage. |
|---|
| 授業の目標・概要等 |
Learn the basics of data science and data analysis |
| 授業計画 |
lesson1: Guidance and Introduction
lesson2: Data acquisition and open data, data science ethics
lesson3: Types of data and descriptive statistics
lesson4: Descriptive statistics
lesson5: Visualizing data in R
lesson6: Correlation and regression
lesson7: Simple regression analysis using Excel
lesson8: Principal component analysis and cluster analysis using R
lesson9: Probability
lesson10: Random variable and probability distributions
lesson11: Basic probability distributions
lesson12: Bivariate probability distributions
lesson13: Methods for data collection
lesson14: Point estimation and interval estimation
lesson15: Interval estimation
Quizzes will be assigned after each lecture. No final exam in the end of the semester |
| 教科書・参考書等 |
Not specified |
授業で使用する
メディア・機器等 |
|
| 【詳細情報】 |
handouts, lecture slides, requisite PC specified by Hiroshima University |
授業で取り入れる
学習手法 |
|
予習・復習への
アドバイス |
Review each lesson using lecture materials. |
履修上の注意
受講条件等 |
Practical exercises will be given in the course. Therefore, a requisite PC specified by Hiroshima University (https://www.hiroshima-u.ac.jp/en/about/initiatives/jyoho_ka/hikkei_pc) is mandatory to have. Please make sure that Excel is correctly installed and ready to use. Be aware that the course contents are largely overlapped between the current course and the following courses: "Fundamental Data Science (30101001)" and "Fundamental Data Science (30830001)". Therefore, once you enroll in this course, you cannot enroll to the other previously mentioned two courses (note that the other two courses are given in Japanese). |
| 成績評価の基準等 |
check tests (Quizzes) |
| 実務経験 |
|
実務経験の概要と
それに基づく授業内容 |
|
| メッセージ |
The course is in on-demand format, using Moodle, Microsoft Teams, and Microsoft Stream. We will notify you about the details of the course (e.g. how to watch lecture videos) via Momiji. |
| その他 |
Days and time of posting lecture materials:
Lesson 1: Nov 29th,
Lesson 2: Dec 4th,
Lesson 3: Dec 6th,
Lesson 4: Dec 11th,
Lesson 5: Dec 13th,
Lesson 6: Dec 18th,
Lesson 7: Dec 20th,
Lesson 8: Dec 20th,
Lesson 9: Jan 8th,
Lesson 10: Jan 10th,
Lesson 11: Jan 15th,
Lesson 12: Jan 15th,
Lesson 13: Jan 22nd,
Lesson 14: Jan 24th,
Lesson 15: Jan 29th
(Each lesson will be posted at 8:30 AM) |
すべての授業科目において，授業改善アンケートを実施していますので，回答に協力してください。
回答に対しては教員からコメントを入力しており，今後の改善につなげていきます。 |