シラバス参照
講義科目名
【IP】Data Science
科目ナンバリングコード
講義題目
授業科目区分
開講年度
2022
開講学期
夏学期
曜日時限
夏学期 火曜日 3時限
必修選択
選択
単位数
1.0
担当教員
栗田 健一
開講学部・学府
地球社会統合科学府
対象学部等
対象学年
開講地区
伊都地区
その他
(自由記述欄)
履修条件
The prerequisite for this course is a basic understanding of statistics. If you are interested in attending the course, please contact us at the e-mail address: "kurita at scs.kyushu-u.ac.jp".
授業概要
プログラミング言語Rを用いて入門レベルのデータサイエンスを学習する。Rプログラミングを通して、データサイエンスにおいて重要なスキルである、インポート、整理、変換、可視化、モデリング、コミュニケーションを習得することを目的とする。
受講者の要望や学習水準によっては、応用データ分析に関する文献の輪読を行う。
This course will present the lecture of an intoroductly data science using R. Participants learn R to get skills of import, tidying, transforming, visualizasion.
At the request of the students, a reading of the literature on applied data analysis will be conducted.
授業形態
(項目)
■ 講義・演習
□ 実験
■ グループワーク・ペアワーク
□ 学内外実習
■ プレゼンテーション
■ ディスカッション
□ PBL/TBL
授業形態
(内容)
Students will bring their own laptops with R and RStudio installed to each session, and will learn by actually compiling code. Students are required to submit an R Markdown file of their analysis applying the methods learned in the lectures, which can be tested by others. Students will also read a literature review on data analysis to deepen their understanding of data science applications. Students will prepare a report and slides based on their reading of the literature and give a presentation.
使用する教材等
板書、テキスト(紙媒体)、スライド資料(電子媒体)、映像・音声資料
全体の教育目標
This course aims to master importing, tidying, transforming, visualizing, modeling, and communicating, which are essential skills in data science.
個別の教育目標
授業計画
Data Science II: Basic data science and R
Data Science VI: Applied data science
キーワード
Data Science, Causal Inference, Machine Learning, Empirical Analysis
授業の進め方
In the case of exercises:
The instructor will give a lecture on data science. Students understand the lecture's content by checking the code on their PCs. Students will submit a report (R Markdown file) on their application of the analytical methods learned in the lecture and prepare slides summarizing the report results.
In the case of reading or journal club:
Students will read the literature on applied data analysis in a reading or journal club. Each participant is responsible for submitting a report and slide files by the day before the lecture and reporting in the lecture.
テキスト
Cunningham, S. (2021) Causal Inference: The Mixtape, Yale University Press.
Huntington-Klein, N. (2022) The Effect: An Introduction to Research Design and Causality, Routledge.
Imai, K. & Williams, N, W. (2022) Quantitative social science: An introduction in tidyverse. Princeton University Press.
Wickham, H., & Grolemund, G. (2016) R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc.
参考書
学習相談
試験/成績評価の方法等
Evaluation will be based on reports, presentations, and positive attitude in discussions.
その他
添付ファイル
更新日付
2022-07-26 22:01:50.158
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