| 授業名 | Big Data and Analytics |
|---|---|
| Course Title | Big Data and Analytics |
| 担当教員 Instructor Name | Ricardo Lim |
| コード Couse Code | NUC401_N23A |
| 授業形態 Class Type | 講義 Regular course |
| 授業形式 Class Format | On Campus |
| 単位 Credits | 2 |
| 言語 Language | EN |
| 科目区分 Course Category | 専門教育科目 / Specialized Subject |
| 学位 Degree | BBA |
| 開講情報 Terms / Location | 2023 UG Nisshin Term1 |
授業の概要 Course Overview
Misson Statementとの関係性 / Connection to our Mission Statement
Big Data and analytics are the current “new frontier” for business. To gain competitive advantage all about innovation, superior customer service delivery, value-adding collaborations, and value chain maximization. But all these may not be enough to compete with “asset light” world beating companies like Tencent, Rakuten, Amazon, and Spotify. These companies use big data and analytics to drive their decision making.
授業の目的（意義） / Importance of this course
What is the big data and analytics frenzy all about? When we watch Netflix or Tiktok, or merely walk with our smartphone, we are being big-data-dissected and “predicted,” i.e., about what we need to buy or watch next, whether we like it or not. The business press has added to the frenzy with jargon like “Digitization,” “AI,” “algorithms,” etc. We are awed by the science.
In this course students will learn about the foundations of data and analytics. We dive beneath the jargon into the how-to of analytics. We do practical in-class exercises on data analytics. We should no longer feel the intimidation. We must, however, do the math moves in order to appreciate the more sophisticated tools available to businesses.
In this course students will learn about the foundations of data and analytics. We dive beneath the jargon into the how-to of analytics. We do practical in-class exercises on data analytics. We should no longer feel the intimidation. We must, however, do the math moves in order to appreciate the more sophisticated tools available to businesses.
到達目標 / Achievement Goal
The class will get a brief refresher on the a few statistical concepts, and focus on descriptive, predictive and prescriptive tools such as CLV, Monte Carlo simulations, SPC-SQC, OLS/Logistic regressions, and recommender systems. These are the foundations for many big data applications such as AI, Machine Learning, and Deep Learning. We simplify and get into the practical understanding. Students will not be a data scientist overnight from this course; that is more difficult and requires deep study of programming languages and data science concepts such as matrices and vectors. Rather, from this course students will know the foundations of data science and be able to intelligently talk to, and even manage, data scientists.
本授業の該当ラーニングゴール Learning Goals
*本学の教育ミッションを具現化する形で設定されています。
LG1 Critical Thinking
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Managerial Perspectives (BBA)
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Managerial Perspectives (BBA)
受講後得られる具体的スキルや知識 Learning Outcomes
At the completion of the course students should understand concepts such as
1. Core statistical tools
2. Customer lifetime value
3. Simulations
4. Statistical Process Control
5. Correlation and Regression
6. Logistic Regression
7. Recommender systems
1. Core statistical tools
2. Customer lifetime value
3. Simulations
4. Statistical Process Control
5. Correlation and Regression
6. Logistic Regression
7. Recommender systems
SDGsとの関連性 Relevance to Sustainable Development Goals
Goal 4 質の高い教育をみんなに（Quality Education）
教育手法 Teaching Method
| 教育手法 Teaching Method | % of Course Time | |
|---|---|---|
| インプット型 Traditional | 30 % | |
| 参加者中心型 Participant-Centered Learning | ケースメソッド Case Method | 70 % |
| フィールドメソッド Field Method | 0 % | 合計 Total | 100 % |
事前学修と事後学修の内容、レポート、課題に対するフィードバック方法 Pre- and Post-Course Learning, Report, Feedback methods
Big Data and Analytics is a 1400 minute, "quant" course. Students, please expect intense math and stats drills. Be prepared to review and use multiple Excel and stats principles. Before each class students read problem sets and mini-cases. You are expected to have your laptops and Excel at all times. Be ready to hear part-lecture, but to also participate actively in discussion to solve problems. There will be one midterm exam and one final exam.
授業スケジュール Course Schedule
第1日（Day1）
Course Intro and math refresher●使用するケース
See- what is big data? https://www.sas.com/en_ph/insights/big-data/what-is-big-data.html
- What is data analytics? https://www.lotame.com/what-is-data-analytics/
- Watch: https://www.youtube.com/watch?v=yZvFH7B6gKI
第2日（Day2）
Monte Carlo Simulations●使用するケース
East-West Bank (RA Lim)Note on Monte Carlo Simulations
第3日（Day3）
Customer Lifetime Value●使用するケース
Customer Lifetime Value (RA Lim)第4日（Day4）
MIDTERMStatistical Process Control (SPC) / Statistical Process Quality (SPQ)
●使用するケース
Cases by RA Lim1. SPC-SQC Control Charts
2. Boxes dataset
3. Standard tables for SPC
4. Table of control chart constants
第5日（Day5）
Correlation and Regression●使用するケース
Correlation and RegressionHappiness, sharks, etc. data
第6日（Day6）
Logistic Regression●使用するケース
Note on Logistic RegressionsNUCB Logit worksheet / data sheet
Orangia Highways (A) KEL185
第7日（Day7）
Recommender SystemsFINAL EXAM
●使用するケース
Collaborative Filtering (Recommender Systems) (RA Lim)成績評価方法 Evaluation Criteria
*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
| 講師用内規準拠 Method of Assessment | Weights |
|---|---|
| コールドコール Cold Call | 0 % |
| 授業内での挙手発言 Class Contribution | 50 % |
| クラス貢献度合計 Class Contribution Total | 50 % |
| 予習レポート Preparation Report | 0 % |
| 小テスト Quizzes / Tests | 20 % |
| シミュレーション成績 Simulation | 0 % |
| ケース試験 Case Exam | 0 % |
| 最終レポート Final Report | 0 % |
| 期末試験 Final Exam | 30 % |
| 参加者による相互評価 Peer Assessment | 0 % |
| 合計 Total | 100 % |
評価の留意事項 Notes on Evaluation Criteria
About 50% of the grade comes from class participation. Students must present solutions, ask questions, explain difficult concepts, demonstrate techniques. The mid term will cover weeks 1-3. The final will cover weeks 4-7.教科書 Textbook
- N/A「N/A」N/A（N/A）
参考文献・資料 Additional Readings and Resource
(Free, available online) Introduction to Statistics, by David Lane et al. (2003), Rice University. This will be sent to students as a PDF, or can be downloaded free of charge from
PDF: https://onlinestatbook.com/2/index.html.
Word: http://onlinestatbook.com/Online_Statistics_Education.docx
EPUB: http://onlinestatbook.com/Online_Statistics_Education.epub
See also https://davidmlane.com/hyperstat/ for extra resources.
There is also an interactive e-book for IOS and MAC OS X. https://itun.es/us/CJqXO
PDF: https://onlinestatbook.com/2/index.html.
Word: http://onlinestatbook.com/Online_Statistics_Education.docx
EPUB: http://onlinestatbook.com/Online_Statistics_Education.epub
See also https://davidmlane.com/hyperstat/ for extra resources.
There is also an interactive e-book for IOS and MAC OS X. https://itun.es/us/CJqXO
授業調査に対するコメント Comment on Course Evaluation
I have done this course once in Spring 2022.
担当教員のプロフィール About the Instructor
Ricardo A. Lim, Ph.D. is a professor at the NUCB Business School and visiting professor at Ritsumeikan APU, Beppu, Japan. He was a former Dean of AIM, former President of the Association of Asia Pacific Business Schools (a consortium of 80 Asian B-schools), founding member of the Global Network to Advance Management at Yale Business School, and Asia-Pacirfic Advisory Council of AACSB. He teaches information systems, statistics, analytics, and design thinking x lean x agile concepts. He has published in the MIS Quarterly and the Journal of Management Information Systems, and serves as Associate Editor for the International Journal of Business and Economics, Taiwan. He currently consults for education and financial services sectors. Before joining academe he was a senior consultant for the Computer Sciences Corporation in Boston and Siemens Computing in Manila. He has a Ph.D. from the U. of Southern California, an MBA from the U. of Virginia, and a B.Com. from McGill University.
Refereed Articles
- (2023) Determinants of Conspicuous Consumption in Smartphones. Asia Pacific Journal of Information Systems 33(3): 2288-5404
- (2023) A Study of Satisfaction and Loyalty for Continuance Intention of Mobile Wallet in India. International Journal of E-Adoption (IJEA) 15(1): 1937-9633
- (2021) Developing and Testing a Smartphone Dependency Scale Assessing Addiction Risk. International Journal of Risk and Contingency Management 10(4): 2160-9624
- (2021) Business Model Innovation: A Study of Empowering Leadership. Creativity and Innovation Management 1467-8691
- (2021) The Effect of Reciprocity on Mobile Wallet Intention: A Study of Filipino Consumers. International Journal of Asian Business and Information Management 12(2): 1947-9638