BUSINESS INTELLIGENCE*
国際教養学部
AIBE4060
コース情報
担当教員: MOUSAVI JAHAN ABADI Seyed Mohammad
単位数: 4
年度: 2024
学期: 春学期
曜限: 金5, 金6
形式: 対面授業
レベル: 400
アクティブラーニング: あり
他学部履修: 可
評価方法
出席状況
リアクションペーパー
レポート
定期試験
定期試験期間中
中間試験
授業期間中
詳細情報
概要
Business intelligence (BI) is a technology-driven process for analyzing data and delivering actionable information that helps executives, managers and workers make informed business decisions. The ultimate goal of BI initiatives is to drive better business decisions that enable organizations to increase revenue, improve operational efficiency and gain competitive advantages over business rivals. BI refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. This course is for students who want to have a look at how BI and data science can be used for supporting Data-Driven Decision Making in organizations and companies. This course covers BI topics, like: Descriptive Analytics, Predictive Analytics, Prescriptive Analytics, and Big Data. During this course, students will do following activities: - Learning concepts and theories about BI - Studying real and practical use cases of application of BI in organizations and companies - Acquiring skills and abilities of BI by doing exercise and practice hands-on lab works
目標
In this undergraduate course, students concentrate on learning theoretical concepts, becoming familiar with use cases of BI, and using programming languages and tools for BI (like: Databases, SQL, Microsoft Power BI, Microsoft Power Query).
授業外の学習
- Review and studying class lecture (70 minutes) - Preparation and submission of homework (individual) and class assignments (60 minutes) - Preparation and submission of final report (group work) (60 minutes)
所要時間: at least, 190 minutes per lecture (week)
スケジュール
- - Course Introduction - [Concept & Theory] (Chapter 1) An Overview of Business Intelligence, Analytics, and Data Science
- - [Concept & Theory] (Chapter 1) An Overview of Business Intelligence, Analytics, and Data Science
- - [Hands-On Setup] Installation and Setup - [Hands-On] SQL for Database Programming and Analytics
- - [Hands-On 1] SQL for Database Programming and Analytics
- - [Concept & Theory] (Chapter 2) Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization
- - [Concept & Theory] (Chapter 2) Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization
- - Introduction of Group Work (Final Report) - [Concept & Theory] (Chapter 3) Descriptive Analytics II: Business Intelligence and Data Warehousing
- - [Concept & Theory] (Chapter 3) Descriptive Analytics II: Business Intelligence and Data Warehousing
- - Team Arrangement and Planning for Group Work (Final Report) - [Hands-On 2] Data Visualization and Self-Service BI (Tool: Microsoft Power BI)
- - [Hands-On 2] Data Visualization and Self-Service BI (Tool: Microsoft Power BI)
- - [Concept & Theory] (Chapter 4) Predictive Analytics I: Data Mining Process, Methods, and Algorithms
- - [Concept & Theory] (Chapter 4) Predictive Analytics I: Data Mining Process, Methods, and Algorithms
- - [Hands-On 3] ETL and Data Modeling (Tool: Microsoft Power Query & Power BI)
- - [Hands-On 4] Data Visualization and Self-Service BI (Tool: Microsoft Power BI)
- - [Hands-On 4] Data Visualization and Self-Service BI (Tool: Microsoft Power BI)
- - Mid-term exam
- - [Concept & Theory] (Chapter 5) Predictive Analytics II: Text, Web, and Social Media Analytics
- - [Concept & Theory] (Chapter 5) Predictive Analytics II: Text, Web, and Social Media Analytics
- - [Concept & Theory] (Chapter 6) Prescriptive Analytics: Optimization and Simulation
- - [Concept & Theory] (Chapter 6) Prescriptive Analytics: Optimization and Simulation
- - [Hands-On 5] AI-Powered BI: Improving Forecasts and Decision Making with Machine Learning (Tool: Microsoft Power BI)
- - [Hands-On 5] AI-Powered BI: Improving Forecasts and Decision Making with Machine Learning (Tool: Microsoft Power BI)
- - [Concept & Theory] (Chapter 7) Big Data Concepts and Tools
- - [Concept & Theory] (Chapter 7) Big Data Concepts and Tools
- - [Concept & Theory] (Chapter 8) Future Trends, Privacy and Managerial Considerations in Analytics
- - [Concept & Theory] (Chapter 8) Future Trends, Privacy and Managerial Considerations in Analytics
- - Final report (group work) presentation
- - Final report (group work) presentation
教科書
It is necessary to have following textbook (printed version or e-Text version) for the lecture.
“Business Intelligence, Analytics, and Data Science: A Managerial Perspective”, 4th edition
著者: Ramesh Sharda, Dursun Delen and Efraim Turban
出版社: Pearson, 2018
参考書
書籍情報はありません。