基本情報/Basic Information

開講科目名 ／Course	ビッグデータ解析／Big Data Analysis
時間割コード ／Course Code	F371100001
ナンバリングコード ／Numbering Code
開講所属 ／Course Offered by	産業情報学部産業情報学科コースなし／College of Industry and Information Science Department of Industry and Information Science
開講区分 ／semester offered	前期／First Semester
単位数 ／Credits	2.0
学年 ／Year	3,4
主担当教員 ／Main Instructor	真嘉比　愛（非常勤）／Ai Makabi
科目区分 ／Course Group	専門科目　専門科目選択／専門科目 専門科目選択
遠隔授業 ／Remote lecture	No

担当教員情報/Instructor Information

教員名 ／Instructor	教員所属名 ／Affiliation
真嘉比　愛（非常勤）／Ai Makabi	産業情報学科／Department of Industry and Information Science
秋山　解（非常勤）／Kai AKIYAMA	産業情報学科／Department of Industry and Information Science
兼城　大（非常勤）／Dai KANESHIRO	産業情報学科／Department of Industry and Information Science
宮城　将伍（非常勤）／Shogo MIYAGI	産業情報学科／Department of Industry and Information Science

※ポリシーとの関連性 ／*Relevance to Policy	This lecture is related to the 'development of human resources who can play an active role in the real world' and teaches techniques related to data analysis.
授業に関する問い合わせ ／Inquiries about classes	bigdata-analysis-2024 [at] googlegroups.com
学びの準備 ／Prepare to learn	ねらい ／Goal To acquire basic knowledge related to big data analysis. Understand the items to be considered when utilising data in business. メッセージ ／Message Lecture materials will be distributed each time. It is not necessary to purchase the textbook. 到達目標 ／Attainment Targets Be able to learn knowledge related to big data analysis and answer assignments. Be able to reflect on basic data analysis and its results.
学びの実践 ／Learning Practices	授業計画 ／Class Plan テキスト・参考文献・資料など ／Textbooks, references, materials, etc. Materials will be distributed in every lecture 学びの手立て ／Way of learning The course consists of a combination of lectures, assignments, case studies, etc. 評価 ／Evaluation Assessments are based on assignment reports: 90% or above is excellent, 80% or above is outstanding, 70% or above is good, 60% or above is acceptable, and less than 60% is not acceptable.
学びの継続 ／Continuing to learn	次のステージ・関連科目 ／Next Stage and Related Courses (Related subjects) Data Analysis I/II, Management Business Information Theory I/II, Business Information Analysis I/II, Introduction to Artificial Intelligence, Intelligent Information Processing

No.	回（日時） ／Time (date and time)	授業計画 ／Class Plan	時間外学習の内容 ／Content of Overtime Learning	備考 ／Notes
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