Introduction to Data Science (A)

授業科目名

(英語名)

Introduction to Data Science (A)

科目区分

全学共通科目

-

対象学生

国際商経学部

学年

1年

ﾅﾝﾊﾞﾘﾝｸﾞｺｰﾄﾞ

IACBG1GCA7

単位数

2単位

ナンバリングコードは授業科目を管理する部局、学科、教養専門の別を表します。詳細は右上の？から別途マニュアルをダウンロードしてご確認ください。

授業の形態

講義・演習 (Lecture/Seminar)

開講時期

2024年度後期

(Fall semester)

担当教員

疋田　浩一

所属

非常勤講師

授業での使用言語

英語

関連するＳＤＧｓ目標

目標9

ｵﾌｨｽｱﾜｰ・場所

In the classroom after class

連絡先

xxgtj326@guh.u-hyogo.ac.jp

対応するディプロマ・ポリシー(DP)・教職課程の学修目標

二重丸は最も関連するDP番号を、丸は関連するDPを示します。

学部DP

ー

研究科DP

ー

全学DP

4-1◎／4-2◎

教職課程の学修目標

ー

講義目的・到達目標

To acquire the fundamental knowledge and skills in the field of Data Science in this course.
To learn and acquire necessary analytic skills for data analytics while conducting practicum.
To acquire the mindset for the data scientist equipped with data analytical skills.

授業のサブタイトル・キーワード

keyword:
Data Science, Microsoft Excel, Python

講義内容・授業計画

Content:
By experiencing and understanding data analysis through PC training, acquire basic knowledge.

Course plan:
#1 Orientation
Course Overview
ICT Services for Academic Learning on Campus (1)
-- K-Drive, Z-Drive, etc. --
-- Setting up ActiveMail --
#2 ICT Services for Academic Learning on Campus (2)
-- Password and Security Issues --

#3 Excel (1)
-- Data Preparation with Excel --
#4 Excel (2) -- Functions & Charts --#5 Excel (3) -- Tally a Questionnaire --#6 Excel (4) -- Final assignment for Excel --
#7 Data Science in New Normal
-- Changing Society --
-- New Trends in Data Science and AI --
#8 Front End of Data Science and AI
-- Data needed in the Society --
-- Realm of Data Science and AI in the Society --
-- Technology for Data Science and AI --
-- Front End of Appication of Data Science and AI --
#9 Information Ethics
-- Ethics in Data Science and AI Use --
#10 Information Security
-- How to protect Data and Compliance Issues --
#11 Minterm exam

#12 Python (1)
-- Python and Jupyter notebook Set-up --
#13 Python (2)
-- Programming in Python --
#14 Python (3)
-- Understanding Data, Statistical Analysis --
#15 Python (4)
-- Analyzing Data, correlation and causation --

教科書

I distribute documents every time.

参考文献

Asakura Shoten, Publisher. Introduction to Data Science. Data Science with Python using business data
Kodansha. Data Science for Liberal Arts Students

事前・事後学習（予習・復習）の内容・時間の目安

The times for preparation and review are as follows.
Details will be provided by the person in charge as appropriate.

Preparation:
Review all the contents up to the last time and prepare for the next time (15 hour in 15 weeks)

Review:
Submission of each assignment (16 hours in 2nd half of this course)
Review with materials distributed (21 hours in 1st half of this course, 8 hours in 2nd half)

アクティブ・ラーニングの内容

Not applicable

成績評価の基準・方法

Students must attend all 15 classes. Only medical excuses will be accepted.
Midterm exam: 40%, Excel assignments 40%, and Python assignment: 20%

課題・試験結果の開示方法

Basically, give feedback during class explanations or using E-mail.

履修上の注意・履修要件

All classes in this course will be carried out in the hybrid style of face-to-face and online on zoom.

We will deliver online class for the students who cannot enter Japan or should stay still at home for medical treatment. In the online classroom, you need wi-fi network connection and a PC on which MS-Office for desktop and zoom application are installed.

The final class method is decided after the course registration, and we'll inform you at the time.
Assignments are issued based on the in-class learning activities in the practicum. Thus, it is the student’s responsibility to complete and submit the late assignments before the following class.
It should be warned that such bad learning attitudes in the classroom as disturbing other students’ learning will lead to an "Failed" for the course.

実践的教育

Not applicable

備考

There are no assignments in this course that can be supported by generative AI.

英語版と日本語版との間に内容の相違が生じた場合は、日本語版を優先するものとします。

シラバス情報