授業の主題
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In this class, Google Colaboratory is used to learn fundamental techniques about Data Science and AI with Python programing throughout group works.
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学修目標（到達目標）
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The goal of this class is to analyze open data with group members, and to add social and scientific value.
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授業概要
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the 1st and the second class:
Google Colaboratory is used to learn fundamental techniques about Data Science and AI with Python programing
the third class:
Things to keep in mind about usages of social and open data
the fourth to the seventh class:
Group working
the eighth class:
Presentation
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講義スケジュール
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| 講義回 | テーマ | 具体的な内容 | 担当教員 |
| 1 | Python quick learning 1 | Data Science fundamental: statistics, Linear regression, Algorithm | 中澤 嵩[NAKAZAWA, Takashi](学術メディア創成センター) |
| 2 | Python quick learning 2 | Data engineering and AI fundamentals: Neural network, Machine learning, Inference, Image analysis, Time series analysis, natural language processing | 中澤 嵩[NAKAZAWA, Takashi](学術メディア創成センター) |
| 3 | Things to keep in mind about usages of social and open data | Data bias, Privacy, Anonymized information | 中澤 嵩[NAKAZAWA, Takashi](学術メディア創成センター) |
| 4 | Group work 1 | Collect data and visualization with Google Colaboratory | 中澤 嵩[NAKAZAWA, Takashi](学術メディア創成センター) |
| 5 | Group work 2 | Data analysis with Google Colaboratory | 中澤 嵩[NAKAZAWA, Takashi](学術メディア創成センター) |
| 6 | Group work 3 | Assessment with Google Colaboratory | 中澤 嵩[NAKAZAWA, Takashi](学術メディア創成センター) |
| 7 | Group work 4 | Preparation for presentation | 中澤 嵩[NAKAZAWA, Takashi](学術メディア創成センター) |
| 8 | Presentation | | 中澤 嵩[NAKAZAWA, Takashi](学術メディア創成センター) |
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評価方法と割合
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評価方法
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“Pass (Academic achievement 60％ or more)” or “Fail (less than 60％)”
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評価の割合
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- ( 50 )% Report
- ( 50 )% Presentation slide
- ( 20 )% Presentation
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ルーブリック
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【授業別ルーブリック】
| 評価項目 | 評価基準 |
| Exemplary | Standard | Needs improvement |
| Data Science Fundamental | Understand fundamental topics of Data Science and describe them in particular. | Understand fundamental topics of Data Science. | Not understand fundamental topics of Data Science. |
| Data Engineering Fundamental | Understand fundamental topics of Data Engineering and describe them in particular. | Understand fundamental topics of Data Engineering. | Not understand fundamental topics of Data Engineering. |
| AI Fundamental | Understand fundamental topics of AI and describe them in particular. | Understand fundamental topics of AI. | Not understand fundamental topics of AI. |
| Programing | Understand features of programing language and describe them in particular. | Understand features of programing language | Not understand features of programing language |
| Exercise | Perform advanced analysis and visualize using some kinds of open data with programing language. | Analyze and visualize using some kinds of open data with programing language. | Not analyze and visualize using some kinds of open data with programing language. |
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授業時間外の学修に関する指示
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予習に関する指示
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Access URL of Google Colaboratory distributed in the lecture, and study your unknown parts.
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予習に関する教材
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オンデマンド教材以外の指示・課題
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復習に関する指示
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Refer to the instruction given in the class
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復習に関する教材
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オンデマンド教材以外の指示・課題
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教科書・参考書
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教科書
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978-4-06-523809-7
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北川源四郎, 竹村彰通編 ; 内田誠一 [ほか] 著
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講談社
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2021
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978-4-06-530789-2
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北川源四郎, 竹村彰通編 ; 赤穂昭太郎 [ほか] 著
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講談社
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2023
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オフィスアワー等（学生からの質問への対応方法等）
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Office hours will be announced at the first week of the class.
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履修条件
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Students entering in 2022 who wish to take "Mathematical, Data Science, and AI Advanced",please ask the Student Affairs Section of your department.
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その他履修上の注意事項や学習上の助言
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Bring Lap top.
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