到達目標
・To learn mathematical data science, AI, information literacy, security, etc., and to acquire basic knowledge that can be used in daily life and work in the future information society.
・To be able to make appropriate human-centered judgments by studying social situations and actual examples in society, and to be able to explain and utilize the knowledge and skills acquired.
・To acquire the ability to think about things from multiple perspectives through cooperative learning with students from departments other than their own.
(1) Handling of data
(2) Analysis of data
(3) Relationship between corporate activities and mathematical data science/AI
ルーブリック
| Ideal Level | Standard Level | Unacceptable Level |
| Achievement 1
(Data Handling) | Able to handle data appropriately and explain the details of its usage. | Able to handle data appropriately | Unable to handle data properly |
| Achievement 2
(Data Analysis) | Able to analyze real data appropriately and explain the results correctly. | Able to analyze real data and explain the results. | Unable to analyze real data and explain the results. |
| Achievement 3
(Relationship between corporate activities and mathematical data science/AI) | Students can fully investigate the company in charge, compose a report based on appropriate interviews, and fully consider the relationship between corporate activities and mathematical data science/AI from multiple perspectives. | Students can fully investigate the company in charge, compose a report based on interviews, and consider the relationship between corporate activities and mathematical data science/AI from multiple perspectives. | Students cannot investigate the company in charge, compose a report based on interviews, and consider the relationship between corporate activities and mathematical data science/AI from multiple perspectives. |
学科の到達目標項目との関係
ディプロマポリシー DP3
説明
閉じる
ディプロマポリシー DP4
説明
閉じる
MCCコア科目 MCCコア科目
説明
閉じる
教育方法等
概要:
・Through "Data Science I" and "Data Science II", students learn the literacy of information technology, mathematical data science, AI, and security that technical NIT students should learn regardless of their humanities and sciences.
・Not only knowledge, students learn the importance of data science in society through actual examples and practice exercises using real data to acquire basic knowledge for discovering and solving problems in the real world and learning how to use data appropriately.
授業の進め方・方法:
The class consists mainly of lectures and exercises using actual data. In the industry-academia collaboration education, teams consisting of students from all departments are formed as much as possible, and the teams investigate and interview the companies in charge, discuss the relationship between the data and AI applications, and write a report.
注意点:
Examination, presentation, and others (report, etc.) will be evaluated comprehensively.
Each evaluation will consist of 40% examination, 20% presentation, and 40% others.
A grade of 60 points or more is required to receive credit.
A student whose grade is less than 60 points may take an additional examination upon request.
If the student is approved for credit after the additional examination, the grade will be 60.
The class plan is subject to change according to the student's level of understanding.
授業の属性・履修上の区分
授業計画
|
|
週 |
授業内容 |
週ごとの到達目標 |
| 後期 |
| 3rdQ |
| 1週 |
Using Teams & Industry-University Cooperative Education 1 |
Students understand how to use Teams, and how to proceed with corporate research activities and what to keep in mind.
|
| 2週 |
Using Teams & Industry-University Cooperative Education 2 |
Students conduct research on the company, and hold a meeting with Teams.
|
| 3週 |
Using Teams & Industry-University Cooperative Education 3 |
Students interview the companies and write a report on the results and their relationship with data and AI applications.
|
| 4週 |
Data science 1 |
Student can acquire data appropriately and understand how to handle them and what to keep in mind.
|
| 5週 |
Data science 2 |
Students understand the types of data and can create appropriate graphs.
|
| 6週 |
Data science 3 |
Students can understand frequency distributions and histograms through exercises on real data.
|
| 7週 |
Data science 4 |
Students can understand how to sort data through exercises on real data.
|
| 8週 |
Data science 5 |
Students can understand the representative values (mean, median, and mode) of data through exercises on real data.
|
| 4thQ |
| 9週 |
Data science 6 |
Students can understand the variability (variance, standard deviation) of data through exercises on real data.
|
| 10週 |
Data science 7 |
Students can understand box plots and scatter plots through exercises on real data.
|
| 11週 |
Data science 8 |
Students can understand correlation and correlation coefficients through exercises on real data.
|
| 12週 |
Data science 9 |
Students can understand the least-squares method through exercises on real data.
|
| 13週 |
Data science 10 |
Students can understand the regression line through exercises on real data.
|
| 14週 |
Data science 11 |
Students can understand the coefficient of determination through exercises on real data.
|
| 15週 |
Data science 12 |
Students can understand the analysis of data and causal relationships through exercises on real data.
|
| 16週 |
Questionnaire |
Students reflect on their learning and fill out questionnaires.
|
モデルコアカリキュラムの学習内容と到達目標
| 分類 | 分野 | 学習内容 | 学習内容の到達目標 | 到達レベル | 授業週 |
| 基礎的能力 | 工学基礎 | 情報リテラシー | 情報リテラシー | データサイエンス・AI技術の概要を説明できる。 | 2 | |
| データサイエンス・AI技術が社会や日常生活における課題解決の有用なツールであり、様々な専門領域の知見と組み合わせることによって価値を創造するものであることを、活用事例をもとに説明できる。 | 2 | |
| データサイエンス・AI技術を利活用する際に求められるモラルや倫理について理解し、データを守るために必要な事項を説明できる。 | 2 | |
| データサイエンス・AI技術の利活用に必要な基本的スキル（データの取得、可視化、分析）を使うことができる。 | 3 | |
| 自らの専門分野において、データサイエンス・AI技術と社会や日常生活との関わり、活用方法について説明できる。 | 2 | |
評価割合
| Report | Presentation | Exam | Behavior | Portfolio | Other | 合計 |
| 総合評価割合 | 50 | 10 | 30 | 0 | 10 | 0 | 100 |
| Basic Proficiency | 20 | 5 | 20 | 0 | 10 | 0 | 55 |
| Specialized Proficiency | 20 | 0 | 10 | 0 | 0 | 0 | 30 |
| Cross Area Proficiency | 10 | 5 | 0 | 0 | 0 | 0 | 15 |