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.

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.

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。

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

教育手法 Teaching Method		% of Course Time
インプット型 Traditional		0 %
参加者中心型 Participant-Centered Learning	ケースメソッド Case Method	100 %
	フィールドメソッド Field Method	0 %
合計 Total		100 %

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

*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。

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	25 %
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	25 %
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.

• N/A「N/A」N/A（N/A）

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

Previous ratings of the course were positive.

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