授業の目的 【日本語】
Goals of the Course(JPN) | | | このコースでは、データを探索、視覚化、および結論を引き出す方法を紹介します。 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、サンプリング変動、推論、回帰分析などの統計の重要なトピックを紹介します。 また、科学計算と再現性のある研究のためのプログラミング言語としてPythonを紹介します。 統計やプログラミングのバックグラウンドは必要ありません。 |
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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 Python as a 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フレームワークに基づいて、データを探索、視覚化、および結論を引き出す方法を学びます。
- 記述統計、サンプリング変動、推論、および回帰分析に関する統計分析の基本原則を学びます。
- 計算および再現可能な調査ワークフローを使用して、定量的な調査結果を整理および伝達する方法を学びます。 |
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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 divided into 3 broad topics. 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
Part I ) Introduction to data science with Python
02. Setting up a computational environment in the cloud
03. Importing, transforming, and plotting data
04. Principles of Python programming for data science
05. Review of the data science workflow using a case study
Part II ) Principles of statistical analysis
06. Descriptive statistics
07. Interactive exploratory data analysis
08. Sampling variation and inference for discrete variables
09. Sampling variation and inference for continuous variables
10. Correlation analysis
11. Review of statistical principles using a case study
Part III ) Introduction to regression analysis
12. Univariate regression analysis
13. Multivariate regression analysis
14. Regression analysis for categorical data
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. However, a course in basic statistics and regression analysis is highly recommended. |
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成績評価の方法と基準
Course Evaluation Method and Criteria | | | Research tasks and problem sets (80%), final video presentation (20%). 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 | | - Wooldridge, J. (2020) Introductory Econometrics: A Modern Approach, 7Edition. CENGAGE, Asia Edition. ISBN-13: 9789814866088
- Grekousis, G. (2020). Spatial Analysis Methods and Practice: Describe – Explore – Explain through GIS. Cambridge: Cambridge University Press. doi:10.1017/9781108614528. E-book: [https://www.cambridge.org/core/books/spatial-analysis-methods-and-practice/4C135005A621335D06CC63EFF17E3913](https://www.cambridge.org/core/books/spatial-analysis-methods-and-practice/4C135005A621335D06CC63EFF17E3913)
The following open ebooks are available for free on the internet
- Heiss, F. and Brunner, D. (2020). Using Python for introductory econometrics. Available at [http://www.upfie.net/](http://www.upfie.net/)
- Rey, S., Arribas-Bel, D., and Wolf, L. (2022) Geographic Data Science with PySAL and the PyData Stack. Available at https://geographicdata.science/book/intro.html |
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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.gg/NKb5GJJmkd). Learning materials, problems sets, and other resources will be distributed via DISCORD. The invitation link to discord will be issued in the first class.
- Students should create a (free) account in DEEPNOTE (https://deepnote.com/sign-in?token=ec5ee6cd). This computing environment will be used for some of the problem sets of the class. Here is a short overview of this powerful data science platform: https://pub.towardsai.net/introduction-to-deepnote-real-time-collaboration-on-jupyter-notebook-18509c95d62f
- Students should create a (free) student account in NOTION (https://www.notion.so/Notion-for-students-teachers-adc631df15ee4ab9a7a33dd50f4c16fe). The following video tutorial on how to use notion is highly recommended: https://youtu.be/ONG26-2mIHU
- Students should create a (free) student account in LOOM (https://www.loom.com/education). All students will explain the solutions to the problem sets through LOOM. |
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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. | | 原則として対面で行う。例外的に、遠隔授業（同時双方向）を行う。
遠隔授業は Teams、Zoom等で⾏う。
※履修登録後に授業形態等に変更がある場合には、NUCＴの授業サイトで案内します。
In principle, lecture and seminar course subjects are offered in-person.
Online participation in classes (interactive communication classes via Teams, Zoom, etc.) may be permitted by the instructor under exceptional circumstances.
*Guidance will be posted on NUCT if there are any changes in the class format, etc. after registration. |
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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.gg/NKb5GJJmkd . 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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