シラバス Syllabus

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 NUC406_N22A
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 2022 UG Nisshin Term1

授業の概要 Course Overview

Misson Statementとの関係性 / Connection to our Mission Statement

To push to the "New Frontier," businesses will need the full toolkit of innovation, superior customer service delivery, value-adding collaborations, and value chain maximization. But these may not be enough to compete with “asset light" giants such as Tencent, Rakuten, Amazon, and Spotify. These companies use big data and analytics as the "tip of the spear," their secret sauce to drive decision- and strategy-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 tend to be awed by the science. But we should not feel awed.

到達目標 / Achievement Goal

In this course students will get past the "awe" and 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.

Students start with a brief refresher on statistical concepts, and then jump to descriptive, predictive and prescriptive tools such as CLV, Monte Carlo simulations, SPC-SQC, OLS/Logistic regressions, and recommender systems. These tools are the simple precursors of 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


LG1 Critical Thinking
LG2 Diversity Awareness
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. Recommender systems
7. Logistic Regression

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

This is a 1400 minute, 2-credit course. We do intense math and stats drills. Be prepared to review and use multiple Excel and stats principles. Bottom line: this course is quantitative. Classes are one morning a week for seven weeks. Before each class students read problem sets and mini-cases. (Students are expected to bring their laptops with at least Excel installed.) Students are expected to meet with learning teams to share their inputs and sharpen the analysis. Students come to class ready to hear part-lecture, but to also participate actively in discussion to solve problems. There will be one midterm and one final exam.

授業スケジュール Course Schedule


Introduction and Stats refresher

- 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/

- https://www.youtube.com/watch?v=yZvFH7B6gKI


MOnte Carlo Simulations (prescriptive analytics)

Read note and prepare the East_West Bank case

Case-readings: (by Prof. Ricardo A Lim, NUCB case bank)
- Note on Monte Carlo Simulation
- East-West Bank


Customer Lifetime Value

Read the note and do the exercises

Case-readings: (by Prof. Ricardo A Lim, NUCB case bank)
Customer Lifetime Value (Summary and problem sets)


Statistical Process Control (SPC) / Statistical Process Quality (SPQ)

MIDTERM in session 2

Case-readings: (by Prof. Ricardo A Lim, NUCB case bank)

SPC-SQC note and problem sets


Regression and Predictive Analytics

Case-readings: (by Prof. Ricardo A Lim, NUCB case bank)
- Correlation and Regression and problem sets
- Accompanying xls file: happiness, sharks, corruption


Logistic Regression

Read the note on Logistic Regression
Prepare and analyze the Orangia Highways case

HBSP: Orangia Highways (A) KEL185

Case-readings: (by Prof. Ricardo A Lim, NUCB case bank)
- Note on Logistic Regressions
- (with Accompanying xls file)


Collaborative Filtering / Recommender Systems

Case-readings: (by Prof. Ricardo A Lim, NUCB case bank)
- Collaborative Filtering (Recommender Systems)

FINAL EXAM in session 2

成績評価方法 Evaluation Criteria

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 35 %
クラス貢献度合計 Class Contribution Total 35 %
予習レポート Preparation Report 0 %
小テスト Quizzes / Tests 40 %
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 25 %
期末試験 Final Exam 0 %
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

50% of the grade consists of results from two exams. These will be one-hour sit-down exams, a mix of multiple choice and caselet problem-solving questions. Throughout the course we will also use the discussion method, where students volunteer to solve problems and freely discuss their approaches in class. I will also be assigning students to five-person learning teams throughout the term. The learning team will be integral to learning and discussion. At the end of the course positive peer assessment, where team members rate the performance of the best performances of their peers.

使用ケース一覧 List of Cases


教科書 Textbook

  • N/A「N/A」N/A(N/A)

参考文献・資料 Additional Readings and Resource

Additional readings

(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

授業調査に対するコメント Comment on Course Evaluation

Spring 2022 will be my first time to implement the course.

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