APPLIED BUSINESS INTELLIGENCE*
博士前期課程グローバル・スタディーズ研究科 - グローバル社会専攻
MZBD5140
コース情報
担当教員: MOUSAVI JAHAN ABADI Seyed Mohammad
単位数: 4
年度: 2024
学期: 秋学期
曜限: 金5, 金6
形式: 対面授業
レベル: 600
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, Big Data, and Artificial Intelligence (AI). 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 - Workshops for designing dashboards and metrics for BI - Acquiring skills and abilities of BI by doing exercise and practice hands-on lab works
目標
In this graduate course, students concentrate on learning theoretical concepts, becoming familiar with use cases of BI, and using programming languages and tools for BI (like: Microsoft Power BI, Google Analytics).
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 Analytics, Decision Support Systems, Business Intelligence, Data Science, and Artificial Intelligence
- - [Concept & Theory] (Chapter 1) An Overview of Business Analytics, Decision Support Systems, Business Intelligence, Data Science, and Artificial Intelligence
- - [Concept & Theory] (Chapter 2) Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications
- - [Concept & Theory] (Chapter 3) Nature of Data, Statistical Modeling, and Visualization
- - Introduction of Final Report (Group/Individual Work) - [Hands-On 1] SQL for Database Programming and Analytics
- - [Hands-On 1] SQL for Database Programming and Analytics
- - Team Arrangement and Planning for Final Report (Group/Individual Work) - [Workshop 1] Design of UX (User Experience) Metrics Visualization
- - [Workshop 1] Design of UX (User Experience) Metrics Visualization
- - [Hands-On 2] Implementation of UX (User Experience) Metrics
- - [Hands-On 2] Implementation of UX (User Experience) Metrics
- - [Concept & Theory] (Chapter 4) Data Mining Process, Methods, and Applications
- - [Case Studies 1] BSI: Business Scenario Investigations
- - [Concept & Theory] (Chapter 5) Machine learning Techniques for Predictive Analytics
- - [Concept & Theory] (Chapter 6) Deep Learning and Cognitive Computing
- - [Concept & Theory] (Chapter 7) Text Mining, Sentiment Analysis, and Social Analytics
- - Mid-term exam
- - [Concept & Theory] (Chapter 8) Prescriptive Analytics with Optimization and Simulation
- - [Case Studies 2] LLM (Large Language Models) and OpenAI
- - [Concept & Theory] (Chapter 9) Big Data, Location Analytics, and Cloud Computing
- - [Concept & Theory] (Chapter 9) Big Data, Location Analytics, and Cloud Computing
- - [Hands-On 3] Data Visualization and Self-Service BI
- - [Hands-On 3] Data Visualization and Self-Service BI
- - [Hands-On 4] Artificial Intelligence (AI) Basics
- - [Hands-On 4] Artificial Intelligence (AI) Basics
- - [Use Cases 3] Applications of AI in Retail, Manufacturing, and Healthcare
- - [Use Cases 3] Applications of AI in Retail, Manufacturing, and Healthcare
- - Final report presentation
- - Final report presentation
教科書
It is necessary to have following textbook (printed version or e-Text version) for the lecture.
“Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support”, Global Edition, 11th edition
著者: Ramesh Sharda, Dursun Delen and Efraim Turban
出版社: Pearson Education Limited, 2021
参考書
書籍情報はありません。