授業の目的 【日本語】
Goals of the Course(JPN) | | | このコースでは、データの探索、可視化、およびデータからの結論の導き方について紹介します。 このコースは、最新のデータサイエンスのフレームワークに基づいており、記述統計、サンプリングのばらつき、推論、回帰分析などの統計学の主要なトピックを紹介する。また、科学的計算と再現可能な研究のためのプログラミング言語であるRを紹介します。 統計学やプログラミングの知識は必要ありません。 |
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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, inference, and regression analysis. The course also introduces R as programing language for scientific computing and reproducible research. No statistical or programming background is necessary. |
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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。 |
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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. |
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授業の内容や構成
Course Content / Plan | | The structure of the course consists of 15 sessions. After each session students are expected to complete short research tasks and problem sets related to the main topics discussed in class.
01. Introduction and overview
02. Setting up a modern computational environment
03. Importing, transforming, and plotting data
04. Principles of programming for data science
05. Review of the data science workflow using a case study
06. Interactive exploratory data analysis
07. Exploratory spatial data analysis
08. Correlation analyses
09. From correlation to causation analysis
10. Sampling variation and inference
11. Review of statistical principles using a case study
12. Univariate regression analysis
13. Multivariate regression analysis
14. Regression diagnostics
15. Review of regression analysis using a case study |
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履修条件・関連する科目
Course Prerequisites and Related Courses | | | There is no precondition to take this course. |
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成績評価の方法と基準
Course Evaluation Method and Criteria | | | Programming exercises (50%) and data science project (50%). To receive credit for this course, students are expected to achieve an overall evaluation equal or superior to C- or C (where applicable). |
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教科書・参考書
Textbook/Reference Book | | - Llaudet, E., & Imai, K. (2022). Data Analysis for Social Science: A Friendly and Practical Introduction. Princeton University Press.
- Wooldridge, J. (2020) Introductory Econometrics: A Modern Approach, 7Edition. CENGAGE, Asia Edition. ISBN-13: 9789814866088 |
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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. |
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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 |
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使用言語
Language(s) for Instruction & Discussion | | |
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授業開講形態等
Lecture format, etc. | | 対面で実施します。
Classes will be held in-person. |
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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). |
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