OVERVIEW OF DATA SCIENCE
国際教養学部
AINF1003
コース情報
担当教員: SCHWIER Corrine
単位数: 2
年度: 2024
学期: 秋学期
曜限: 水3
形式: 対面授業
レベル: 100
アクティブラーニング: あり
他学部履修: 不可
評価方法
授業参加
レポート
中間試験
授業期間中
小テスト等
その他
*In principle, makeup quizzes and exams are not conducted without prior consent from the professor. -Students are required to attend and actively participate in all classes. -Students may miss one class for any reason (medical, personal, train delay, or other). If you miss a class because of illness, or for some other good reason (e.g., attending a funeral), inform me by e-mail in advance of your absence when possible. -Two absences: 10% reduction in final grade. -Three absences: automatic “F” for final grade. -Failing to participate (including, among other activities, incomplete homework, lack of class contributions, engaging your mobile phone) will result in a reduction in your final grade. -If you miss a class, you are responsible for finding out what you missed and how you can prepare for the next class. *I reserve the right to modify this syllabus. Any changes will be announced by email and reflected on our course Moodle page.
詳細情報
概要
With rapid digitization, increased globalization, and industrial markets undergoing major changes through significant technological advances, data science plays an increasingly important role in society and our daily lives. In this course, we will deepen our understanding of what data science is, why we need data, and what it means to use data for explanations through concrete examples. In order to cultivate a basic understanding of what can be done with data, this course will cover the principles of approaches to data analysis and the preprocessing of data for analysis. In addition, this course will provide an opportunity to touch on legal and ethical issues in data science and will discuss the use and collection of data including personal information from various perspectives. This lecture is not a training course for data scientists, rather, it is a general overview of a wide range of data science themes to gain “awareness” about the “data-driven society” and "ultra-smart society" that are currently evolving. This course employs active-learning principles and will present opportunities for students to engage with classmates in discussions and group work. Some learning opportunities, when appropriate, may include peer evaluation. Note, however, as an introductory course, domain specific skills in maths, statistics, and computer programming are not necessary nor required. We will learn the necessary skills and strategies together.
目標
Through critically reading, reflective writing, effective collaboration, and deep discussions, students will work toward developing an understanding of data science that will provide a foundation for later coursework and life beyond university. Through the course materials and interactions, students successfully completing this course can expect to … • meaningfully discuss the basic concepts of data science • view ideas objectively and base judgments on evidence • distinguish the functions of (and the purpose and meaning of) basic methods of data analysis • identify and evaluate the key points for utilizing data • apply appropriate problem-solving strategies with data • write thoughtful reflections • collaborate effectively with peers
授業外の学習
• Review previous class materials (60 minutes) • Reading, watching content videos, note taking (90 minutes) • Searching for relevant sources for assignments (40 minutes)
所要時間: 190 minutes per lecture
スケジュール
- Course organization
- Introduction: Data and Artificial intelligence (AI)
- Lecture: Application of data science
- Lecture: Different types of data
- Lecture: Data science-related tools and software
- Lecture: Basics of data collection
- Lecture: Ethical issues in data science
- Lecture: Improving data literacy: Analysis
- Lecture: Basics of measurements In-class Midterm Exam
- Lecture: Numerical summary of the data
- Lecture: Distributions
- Lecture: Visualization and manipulation of the data
- Student Presentations I
- Student Presentations II
教科書
Required readings will generally be available through the class Moodle page.
参考書
書籍情報はありません。