日本語 English
| 開講年度/ Academic YearAcademic Year |
20242024 |
| 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 |
FD507/FD507FD507 |
| 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) |
|
| 授業形式/ Class StyleCampus |
演習・ゼミ/SeminarSeminar |
| 校地/ CampusCampus |
池袋/IkebukuroIkebukuro |
| 学期/ SemesterSemester |
春学期/Spring SemesterSpring Semester |
| 曜日時限・教室/ DayPeriod・RoomDayPeriod・Room |
木1･D603/Thu.1･D603 Thu.1･D603 |
| 単位/ CreditCredit |
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 |
抽選他/Exceptional Lottery RegistrationExceptional Lottery Registration（定員：20人/ Capacity：20） |
| 配当年次/ Grade (Year) RequiredGrade (Year) Required |
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 |
|
| 他学部履修可否/ Acceptance of Other CollegesAcceptance of Other Colleges |
|
| 履修中止可否/ course cancellationcourse cancellation |
〇（履修中止可/ Eligible for cancellation） |
| 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 |
|
| 学位授与方針との関連/ Relationship with Degree PolicyRelationship with Degree Policy |
各授業科目は、学部・研究科の定める学位授与方針（DP）や教育課程編成の方針（CP）に基づき、カリキュラム上に配置されています。詳細はカリキュラム・マップで確認することができます。 |
| 備考/ NotesNotes |
|
| テキスト用コード/ Text CodeText Code |
FD507 |
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. Students will also learn the basic usage of Python, a programming language, and Tableau, a data visualization tool.
※Please refer to Japanese Page for details including evaluations, textbooks and others.
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。また、プログラミング言語であるPythonやデータ可視化ツールのTableauなどに触れ、データ分析の演習も行う。
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. Students will also learn the basic usage of Python, a programming language, and Tableau, a data visualization tool.
| 1 | Introduction and overview of the course |
| 2 | A standard process of data analysis |
| 3 | Data analysis use cases and use case definition (1) |
| 4 | Data analysis use cases and use case definition (2) |
| 5 | Data collection technologies in sport and wellness (1) |
| 6 | Data collection technologies in sport and wellness (2) |
| 7 | Descriptive statistics |
| 8 | Exploratory data analysis (1) |
| 9 | Exploratory data analysis (2) |
| 10 | Basics of machine learning |
| 11 | Machine learning/AI in sport and wellness |
| 12 | Class presentation and discussion |
| 13 | AI application development |
| 14 | Neural network and large language model |
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 after course registration is complete.
| 種類 (Kind) | 割合 (%) | 基準 (Criteria) |
|---|---|---|
| 平常点 (In-class Points) | 100 |
Tests(30%) Lab assignments(20%) In-class presentation(50%) |
| 備考 (Notes) | ||
| Introduced in a classroom as needed | ||
| その他 (Others) | |||||
|---|---|---|---|---|---|
| Introduced in a classroom as needed |
Students need to bring his/her laptop computer.
・F科目上級（外国語による総合系科目）
・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®L&R 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
・2016年度以降入学者：多彩な学び
・2015年度以前入学者：主題別A