日本語 English
| 開講年度/ Academic YearAcademic Year |
20232023 |
| 科目設置学部/ CollegeCollege |
全学共通科目・全学共通カリキュラム（総合系）/University-wide Liberal Arts Courses (Comprehensive Courses)University-wide Liberal Arts Courses (Comprehensive Courses) |
| 科目コード等/ Course CodeCourse Code |
多彩な学び/主題別/多彩な学び/主題別多彩な学び/主題別 |
| テーマ・サブタイトル等/ Theme・SubtitleTheme・Subtitle |
Data science, Sports data analysis |
| 授業形態/ Class FormatClass Format |
対面（全回対面）/Face to face (all classes are face-to-face)Face to face (all classes are face-to-face) |
| 授業形態（補足事項）/ Class Format (Supplementary Items)Class Format (Supplementary Items) |
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| 授業形式/ Class StyleCampus |
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| 校地/ CampusCampus |
新座/NiizaNiiza |
| 学期/ SemesterSemester |
春学期/Spring SemesterSpring Semester |
| 曜日時限・教室/ DayPeriod・RoomDayPeriod・Room |
月1/Mon.1 Mon.1 ログインして教室を表示する（Log in to view the classrooms.） |
| 単位/ CreditsCredits |
22 |
| 科目ナンバリング/ Course NumberCourse Number |
CMP2431 |
| 使用言語/ LanguageLanguage |
英語/EnglishEnglish |
| 履修登録方法/ Class Registration MethodClass Registration Method |
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| 配当年次/ Assigned YearAssigned Year |
配当年次は開講学部のR Guideに掲載している科目表で確認してください。配当年次は開講学部のR Guideに掲載している科目表で確認してください。 |
| 先修規定/ Prerequisite RegulationsPrerequisite Regulations |
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| 他学部履修可否/ Acceptance of Other CollegesAcceptance of Other Colleges |
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| 履修中止可否/ Course CancellationCourse Cancellation |
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| オンライン授業60単位制限対象科目/ Online Classes Subject to 60-Credit Upper LimitOnline Classes Subject to 60-Credit Upper Limit |
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| 学位授与方針との関連/ Relationship with Degree PolicyRelationship with Degree Policy |
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| 備考/ NotesNotes |
・F科目上級（外国語による総合系科目） ・定員20名 ・他に特別外国人学生が履修 ・この授業は英語で実施する ・履修者はTOEIC®700点相当以上の英語力を有していることを前提に授業を実施する |
This course covers major problems in data science as well as applied use cases in sports science. Students will learn the basic problem-solving methods using data science approaches.
This course will provide you knowledge regarding data science-based problem-solving skills. During this semester, you will learn (1) major data science problems, (2) and data science methods to solve such problems, (3) applied use cases in sports science.
| 1 | Introduction and overview of the course |
| 2 | A framework for data analysis |
| 3 | Data analytics usecases and usecase definition (1) |
| 4 | Data analytics usecases and usecase definition (2) |
| 5 | Data collection technologies in sports and wellness (1) |
| 6 | Data collection technologies in sports and wellness (2) |
| 7 | Descriptive statistics |
| 8 | Exploratory data analysis |
| 9 | Basics of machine learning：unsupervised learning |
| 10 | Basics of machine learning：supervised learning |
| 11 | Machine learning in sports and wellness |
| 12 | Class presentation and discussion (1) |
| 13 | Class presentation and discussion (2) |
| 14 | Summary of the entire course |
板書 /Writing on the Board
スライド（パワーポイント等）の使用 /Slides (PowerPoint, etc.)
上記以外の視聴覚教材の使用 /Audiovisual Materials Other than Those Listed Above
個人発表 /Individual Presentations
グループ発表 /Group Presentations
ディスカッション・ディベート /Discussion/Debate
実技・実習・実験 /Practicum/Experiments/Practical Training
学内の教室外施設の利用 /Use of On-Campus Facilities Outside the Classroom
校外実習・フィールドワーク /Field Work
上記いずれも用いない予定 /None of the above
Instructions will be on Canvas LMS after course registration is complete.
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
Tests(30%) 最終レポート(Final Report)(30%) Class presentation(20%) Active participation to the class(20%) |
| 備考 (Notes) | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Introduced in a classroom as needed |
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Introduced in a classroom as needed |
データサイエンスが解決する主な課題やスポーツにおける応用事例を学び、データサイエンスの課題解決の方法論に関する理解を深める。
This course covers major problems in data science as well as applied use cases in sports science. Students will learn the basic problem-solving methods using data science approaches.
本講義では、データサイエンスの分野における一般的な課題とそれらを解決するデータサイエンスの手法を学ぶとともに、スポーツにおける応用事例についても理解を深める。
This course will provide you knowledge regarding data science-based problem-solving skills. During this semester, you will learn (1) major data science problems, (2) and data science methods to solve such problems, (3) applied use cases in sports science.
| 1 | Introduction and overview of the course |
| 2 | A framework for data analysis |
| 3 | Data analytics usecases and usecase definition (1) |
| 4 | Data analytics usecases and usecase definition (2) |
| 5 | Data collection technologies in sports and wellness (1) |
| 6 | Data collection technologies in sports and wellness (2) |
| 7 | Descriptive statistics |
| 8 | Exploratory data analysis |
| 9 | Basics of machine learning：unsupervised learning |
| 10 | Basics of machine learning：supervised learning |
| 11 | Machine learning in sports and wellness |
| 12 | Class presentation and discussion (1) |
| 13 | Class presentation and discussion (2) |
| 14 | Summary of the entire course |
板書 /Writing on the Board
スライド（パワーポイント等）の使用 /Slides (PowerPoint, etc.)
上記以外の視聴覚教材の使用 /Audiovisual Materials Other than Those Listed Above
個人発表 /Individual Presentations
グループ発表 /Group Presentations
ディスカッション・ディベート /Discussion/Debate
実技・実習・実験 /Practicum/Experiments/Practical Training
学内の教室外施設の利用 /Use of On-Campus Facilities Outside the Classroom
校外実習・フィールドワーク /Field Work
上記いずれも用いない予定 /None of the above
Instructions will be on Canvas LMS after course registration is complete.
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
Tests(30%) 最終レポート(Final Report)(30%) Class presentation(20%) Active participation to the class(20%) |
| 備考 (Notes) | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Introduced in a classroom as needed |
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Introduced in a classroom as needed |