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Data-Driven Business

  • Teacher(s):   J.Marewski  
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Spring Semester 2019-2020, 4.0h. course (weekly average)
  •  sessions
  • 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 Marketing

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

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

 

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.

Contents

Timeline and pedagogical rationale

Tentative 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 related fields. 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 data-ethics project towards the middle of the course and finalize that project at the end of the course by writing an essay (individual essay assignment; see below). This essay will allow them to creatively put the acquired knowledge and skills into practice.

Teaching method:

As mentioned 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.

Preparation and participation requirements:

Participants are expected to verbally contribute to the class discussion. They are also expected to deliver group presentations.

Participants are expected 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 expeted 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.

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.

References

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.

Required journal articles:

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

Pre-requisites

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:

1. Individual verbal participation (30%)

2. Team project (30%)

3. Individual essay assignment (40%)

Except for the grades for the team project, all grades are assigned as a function of individual performance. The team project is group work. The same grade applies to all team members.

The participation grade (30%) 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 other class materials. 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, evaluation modalities will resemble as closely as possible those of the first attempt, albeit with constraints related to the nature of the evaluations.

1. Individual verbal participation: An oral examination may replace the verbal participation grade.

2. Team project: A new project can be done; however, if not all members of a team need to take a second attempt a new group may have to be formed; or if that is not possible, the team project can be replaced by an individual assignment.

3. Written assignment: The individual essay submitted initially can be improved or the student can start over by writing a new essay.



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