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Data-Driven Business

  • Teacher(s): J.Marewski
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Spring Semester 2018-2019, 4.0h. course (weekly average)
  •  séances
  • site web du cours course website
  • Related programmes:
    Maîtrise universitaire ès Sciences en management, Orientation Behaviour, Economics and Evolution

    Master of Science (MSc) in Management, Orientation Strategy, Organization and Leadership

    Master of Science (MSc) in Management, Orientation Marketing

    Master of Science (MSc) in Management, Orientation Business Analytics

 

Objectives

In digitalized societies, data-driven business opportunities abound. Yet, recognizing and making use of those opportunities requires not only technology, but statistical thinking and an understanding of scientific principles, such as experimentation. Moreover, in addition to recognizing and exploiting data-driven business opportunities, managers need to be aware of risks and ethical traps data-driven businesses can run into. For instance, misconceiving algorithms can not only result in missing out on business opportunities, but also create threats to the very existence of the business itself and/or create serious ethical issues that might even scale up to societal significance. Finally, regardless whether it comes to risk management and data ethics or to strategy, PR, reporting, and internal leadership: managers also need to be able to clearly communicate about the data their business is based upon.

This course offers an introduction into the world of data-driven businesses. Specifically, the course will introduce participants to (i) basic quantitative techniques key to data-driven business, train them in basic principles of (ii) statistical thinking and (iii) experimentation, and (iv) invite them to reflect about the risks and ethical problems data-driven businesses can run into as well as about mitigation strategies. Finally, in introducing basic quantitative techniques, participants will take a first step in learning (v) how to transparently communicate data analyses to others, be it a CEO, a regulator, a client, or other stakeholders.

To help participants develop practice-oriented technical and analytical skills, the course takes advantage of real-world business cases, including data sets, this way giving an insight into how data-driven business intelligence works. This course has an applied, real-world focus; it is fully case-based and the teaching parallels that of EMBA (executive education) courses. In so doing, this course takes an introductory stance; prior expertise in data analytics, risk management, and data ethics is not necessary. Highlight of the course is a custom-made consulting project participants will develop (Assignment 2; see below).

Contents

Timeline and pedagogical rationale


Tentative chronological overview:

Participants will first recapitulate the basics of the data analytics toolbox (e.g., basic statistical methods), and in so doing, focus on how to communicate about data. They will then explore selected advanced contents of the data analytics toolbox in more detail, including methods and notions from fields such as statistics, machine learning, and computer science. Special emphasis will be placed on building and testing robust classifiers. Custom-model building will be encouraged. Throughout the course, statistical thinking and reflection about scientific methods such as experimentation will be stimulated. Likewise, throughout the course the risks and ethical problems data-driven businesses can run into as well as mitigation strategies will be explored. Participants will start to develop a consulting project (Assignment 2; see below) towards the middle of the course and finalize that project at the end of the course. This consulting project will allow them to creatively put the acquired knowledge and skills into practice.

Teaching method:

As described above, the course’s sessions are based on real-world business cases that show how the covered techniques and skills can be used to solve real-world business problems in practice (e.g., predicting future best customers, building ethically-defendable classifiers, etc.).

In employing the case method of teaching the idea is that course participants acquire knowledge by doing. To this end, they take over the role of a decision maker (e.g., a manager) who has to solve real-world business problems. Prior to each session, participants read the cases and try to resolve the associated problem by analyzing the data. During each session, the participant body as a whole then works out a solution to the case, with the instructor moderating and guiding the discussion. After each session, participants can meet in teams to recapitulate the contents taught during the session and to correct their individual case solutions.

Preparation and participation requirements:

Participants are required to try to solve all assigned business cases prior to the sessions in which the corresponding cases will be covered. This pre-session preparation of the cases is essential for verbally participating in the discussion, following the course contents, and acquiring usable skills and knowledge. Participants are also required to go back to the cases after each session in order to recapitulate the case solution developed during the session. Going back to the cases after each session is important, because all cases build on each other, and not understanding a preceding case will make it hard to solve the subsequent cases and follow the course.

To help participants prepare the cases, they are asked to read selected journal articles and chapters from books. These articles and chapters will be announced during the course. If participants wish to do so, they can, of course, also consult other readings and – as in the real world – make use of the numerous statistical resources on the internet.

To further facilitate the learning process and to make up for potentially different levels of prior technical knowledge among participants, there will be an opportunity to work in teams for preparing the sessions.

The participation grade (see below) hinges on the in-depth preparation of the materials for each session. In order to receive satisfactory evaluations, participants are requested to demonstrate via active verbal participation (i.e., speaking up in the discussion) that they have worked their way through the materials and made a serious attempt to solve the cases scheduled for the sessions.

Also the assignment grades (see below) hinge on the in-depth preparation of the corresponding cases. In addition to submitting written reports for the assignments, all participants are expected to be able to verbally expose their case solutions during the sessions.

All grades are individual grades.

References

Materials


Required software:

During the sessions, participants will solve real-world business problems. To each session, participants will have to bring their own laptop. On that laptop, Microsoft Excel should be installed. Access to further statistical software packages (e.g., Matlab, R) is useful, but not necessary.

Required business cases:

Solving business cases is required. References to these business cases and the associated data files will be given during the course.

Required book / chapters:

To stimulate reflection and discussion about data ethics, during the course participants are required to read chapters from the book “Weapons of math destruction: How big data increases inequality and threatens democracy”, by Cathy O’Neil.

In addition, participants might be requested to read certain chapters from other books (to be announced during the course).

Required journal articles:

Compulsory readings for this course are a few selected journal articles. References to those articles will be given during the sessions.

Other useful but not required readings:

Besides the materials mentioned above, participants are, of course, free to additionally consult other resources of their preference – as in the real world.

Pre-requisites

Participation requirements

Prior knowledge in basic descriptive statistics (e.g., computing means, standard deviations, etc) is useful, but not required. Prior knowledge of using Microsoft Excel is useful, but not required. Prior knowledge of Matlab, R or other software is not required.

Evaluation

First attempt

Exam:
Without exam (cf. terms)  
Evaluation:

The final grade depends on individual verbal participation during the sessions (25%), 1 regular individual business case assignment (25%; Assignment 1), and 1 creative individual business case assignment to be written by participants themselves (50%; Assignment 2).

The individual case assignments (25% and 50%) consist of preparing a written report of a solution to a business case. Assignment 1 (25%) is a regular case given to participants and to be analyzed by them. Assignment 2 (50%) requires participants to create a business-case themselves and to analyze the data for that case. Assignment 2 can be best thought of as a consulting project with participants using the data analytics toolbox to develop a (business) case for a company.

Both assignment reports include a “Data analysis” part, comprised of summaries of methodological thoughts, analyses, results, and conclusions and an “Annex” with complete and detailed calculations (e.g., spreadsheets, code). All major calculations are to be documented and explained in the annexed materials. In composing the reports, special emphasis has to be placed on the clarity of communication: reports should be written in a way that stakeholders without special analytics skills would be able to understand them. Moreover, both assignment reports include a “Risks and data ethics” part, that is, a section discussing the risks and the ethical issues that come with the proposed analyses and business project. The report for Assignment 2 additionally includes an “Exposition of the case”, that is, the case itself with a rationale for the proposed business idea. All participants must be prepared to present their assignments during dedicated sessions.

The participation grade (25%) hinges on the in-depth preparation of the cases for each session. In order to receive satisfactory evaluations, participants are requested to demonstrate via active verbal participation (i.e., speaking up in the discussion) that they have worked on the cases and made serious attempts to solve them. Merely being present in the sessions (i.e., attending without speaking up) does not count as verbal participation.

Retake

Exam:
Without exam (cf. terms)  
Evaluation:

In case of a retake, the verbal participation grade (25%) will be retained. Individual Assignments 1 and 2 (25% and 50%) will be replaced by new assignments. Details will be given after the registration time.



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