学部・大学院区分
Undergraduate / Graduate		開・博前
時間割コード
Registration Code		3068700
科目区分
Course Category		共通・基礎
Basic
科目名　【日本語】
Course Title		統計学とデータサイエンス入門
科目名　【英語】
Course Title		Introduction to Statistics and Data Science
コースナンバリングコード
Course Numbering Code		INT1L5105E
担当教員　【日本語】
Instructor		MENDEZ GUERRA Carlos albe ○
担当教員　【英語】
Instructor		MENDEZ GUERRA Carlos alberto ○
単位数
Credits		2
開講期・開講時間帯
Term / Day / Period		春 火曜日 ２時限
Spring Tue 2
授業形態
Course style		講義
Lecture


授業の目的　【日本語】
Goals of the Course(JPN)
本講座では、データの探索、視覚化、および結論の導出方法についての導入が提供されます。この講座は現代のデータサイエンスフレームワークに基づいており、記述統計、抽出変動、統計的推論、回帰分析、因果推論など、統計学の重要なトピックを紹介しています。また、講座では科学的な計算と再現可能な研究のためのプログラミング言語として、R、Python、およびStataも紹介されます。統計学またはプログラミングのバックグラウンドは必要ありません。
授業の目的　【英語】
Goals of the Course
This course provides an introduction to how to explore, visualize, and draw conclusions from data. The course is based on a modern data science framework and introduces key topics in statistics such as descriptive statistics, sampling variation, statistical inference, regression analysis, and causal inference. The course also introduces R, Python, and Stata as programming languages for scientific computing and reproducible research. No statistical or programming background is necessary.
到達目標　【日本語】
Objectives of the Course(JPN)
- 最新のデータサイエンスのフレームワークに基づいて、データを探索し、可視化し、結論を導き出す方法を学ぶ。
- 記述統計、標本ばらつき、推論、回帰分析に関する統計解析の基本原理を学ぶ。
- 計算機による再現性のある研究ワークフローを用いて、定量的な研究結果を整理し、伝える方法を学ぶ。
到達目標　【英語】
Objectives of the Course
- Based on a modern data science framework, learn how to explore, visualize, and draw conclusions from data.
- Learn basic principles of statistical analysis regarding descriptive statistics, sampling variation, inference, and regression analysis.
- Learn how to organize and communicate quantitative research findings using a computational and reproducible research workflow.
授業の内容や構成
Course Content / Plan
(A) Foundations of statistics and data science
01. Introduction and overview
02. Review of inferential statistics
03. Replicable data science in the era of AI

(B) Exploratory data analysis in the era of AI
04. Univariate and multivariate descriptions
05. Exploratory data analysis (EDA)
06. Exploratory spatial data analysis (ESDA)
07. Case study and research projects

(C) Regression analysis and predictive modeling
08. Univariate regression analysis
09. Multivariate regression analysis
10. Regression diagnostics and problems
11. Case study and research projects

(D) Causal inference and research design
12. Data generation processes and causal diagrams
13. Randomization and matching
14. Differences in differences
15. Case study and research projects
履修条件・関連する科目
Course Prerequisites and Related Courses
There is no precondition to take this course.
成績評価の方法と基準
Course Evaluation Method and Criteria
Programming exercises (50%) and short research projects (50%). To receive credit for this course, students are expected to achieve an overall evaluation equal or superior to C- or C (where applicable).
教科書・参考書
Textbook/Reference Book
- Ismay, C., and Kim, A. (2021). Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. CRC Press. Available online at https://moderndive.com

- Huntington-Klein, N. (2022). The Effect: An Introduction to Research Design and Causality. Chapman and Hall/CRC. Available online at https://theeffectbook.net

- Békés, G. and Kézdi, G . (2021). Data Analysis for Business, Economics, and Policy. Cambridge University Press.
課外学習等（授業時間外学習の指示）
Study Load(Self-directed Learning Outside Course Hours)
- Students should create a (free) account in DISCORD(https://discord.com/). Learning materials, problems sets, and other resources will be distributed via DISCORD. The invitation link to discord will be issued in the first class.
注意事項
Notice for Students
For further inquires about this course, send an email to carlos@gsid.nagoya-u.ac.jp or book an appointment with professor using this booking system https://carlos777.youcanbook.me
使用言語
Language(s) for Instruction & Discussion
English
授業開講形態等
Lecture format, etc.
対面で実施します。
Classes will be held in-person.
遠隔授業（オンデマンド型）で行う場合の追加措置
Additional measures for remote class (on-demand class)
- For online learning and communication purposes, we use the following discord server https://discord.com . Links to most of our learning materials are available on this website (access credentials to private channels are issued in the first week of each semester).