Syllabus

Titel
2194 Data Science for International Business
LV-Leiter/innen
Thomas Lindner, PhD,MIM(CEMS),BSc
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
11.09.2019 bis 23.09.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Master
Termine
Wochentag Datum Uhrzeit Raum
Donnerstag 03.10.2019 09:00 - 12:00 D1.1.074
Donnerstag 10.10.2019 09:00 - 12:00 D1.1.074
Donnerstag 24.10.2019 09:00 - 12:00 D1.1.074
Donnerstag 31.10.2019 09:00 - 12:00 D1.1.074
Donnerstag 14.11.2019 09:00 - 12:00 D1.1.074
Freitag 15.11.2019 16:00 - 19:00 TC.2.03
Donnerstag 28.11.2019 09:00 - 12:00 D1.1.074
Donnerstag 05.12.2019 09:00 - 12:00 D1.1.074

Inhalte der LV

The course follows the below structure:

  1. Introduction to Analytics, Course Admin, and Introduction to DataCamp
  2. Basic Mathematics for Analytics (online module "Data Science for Managers" due)
  3. Inferential Statistics with R (I) (online module "Introduction to R" due)
  4. Inferential Statistics with R (II) and Application
  5. Project Coaching (online module "Generalized Linear Models in R" due one week before coaching session)
  6. Guest Lecture on Analytics, Artificial Intelligence, Quantum Computing, and the Blockchain (this is a joint session together with class 2196)
  7. Analytics in MNCs
  8. Project Presentations

Lernergebnisse (Learning Outcomes)

In this class, students will learn to understand and apply advanced tools for business analytics. We will develop conceptual and mathematical foundations. Then, we will apply these foundations to analytical questions using R (A language for statistical computing). The course will be accompanied by a learning module in DataCamp, which students can use to acquire basic programming skills, and extend existing knowledge. We will solve simple isolated exercises, as well as more involved issues in business case studies. After completing this course, students will be able to manage and execute data science and analytics tasks, and understand how these tasks contribute to MNC strategy and competitive advantage. Students taking this class are expected to have a basic understanding of statistics, for example evidenced by successful completion of the courses “Managing and Analyzing Data for Business Decisions”, “Machine Learning and the MNC”, or similar courses in other study programs. Alternatively, students can familiarize themselves with basic statistical concepts using self-teaching materials provided before the course starts. No prior experience with programming languages is required.

Regelung zur Anwesenheit

Attendance at all sessions is required.

Lehr-/Lerndesign

The class is a workshop-style course, with many interactive elements. Students are expected to give presentations, provide feedback on each other’s work, and discuss their progress with instructors. The class is also accompanyied by an online teaching module, which forms an inherent part of teaching and student evaluation in this class. This online module is provided by DataCamp, a leading provider of online resources for data science training.

The final case for this class is "Paillasse International SA: Global Market Selection". The case can be downloaded for this class here: https://hbsp.harvard.edu/import/654508. The key question for the final case is "Where should Paillasse invest?". This question is to be answered using data analysis techniques.

Leistung(en) für eine Beurteilung

  • 40% final presentation
  • 20% essay on digital technology
  • 15% homework assignments
  • 10% class participation
  • 10% pre-class test in the first session
  • 5% project proposal

Literatur

1 Autor/in: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Titel:

An Introduction to Statistical Learning with Applications in R


Verlag: Springer
Jahr: 2015
Prüfungsstoff: Nein
Diplomprüfungsstoff: Nein
Empfehlung: Referenzliteratur
Art: Buch

Empfohlene inhaltliche Vorkenntnisse

To prepare for the test in session 1, and to familiarize themselves with basic statistical concepts, students are encouraged to work through the learning materials provided here: http://analytics4exac.net/. The password is "pmba2019". The test in session 1 will be based on these materials.

Zuletzt bearbeitet: 24.09.2019



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