| Dozent/in |
Sandro Cilurzo, MSc; Arthur Habicht, MSc |
| Veranstaltungsart |
Vorlesung |
| Code |
HS251017 |
| Semester |
Herbstsemester 2025 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Do, 18.09.2025, 16:15 - 20:00 Uhr, HS 12
Do, 16.10.2025, 16:15 - 20:00 Uhr, HS 12
Do, 30.10.2025, 16:15 - 20:00 Uhr, HS 12
Do, 13.11.2025, 16:15 - 20:00 Uhr, HS 12
Do, 27.11.2025, 16:15 - 20:00 Uhr, HS 12
Do, 11.12.2025, 16:15 - 20:00 Uhr, HS 12 |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
bi-weekly |
| Inhalt |
The field of data science has experienced a renaissance due to innovations in algorithms and widespread availability of affordable storage and compute capabilities. As a consequence, the growing, global stream of data has emerged as a significant economic factor.
Nonetheless, many companies struggle to make use of their data. A significant reason for this is a lack of experience in organizing data and software as well as managing a data science team in a collaborative setting.
This course sets off, where most data science courses end. It addresses technical and organizational challenges that are typically accompanied by operating data-driven software products in “production”.
In this context, the course aims to provide solutions for the aforementioned challenges. This includes toolkits and architectures that: - render the management of data science projects more efficient
- allow for versioning of data, software and runtime environments, in order to ensure reproducibility of data-driven systems
- improve collaboration and knowledge transfer among members of a larger data science team
- facilitate the deployment of data-driven products |
| Lernziele |
- Understanding of the larger complexity of data-driven software compared to “traditional” software
- A firm grasp of the typical life cycle of machine learning projects in industry
- An overview of existing toolkits that address the challenges of data-driven products
- Knowledge in a subset of those toolkits that cover different areas, such as:
- code versioning (f.e. Git)
- data versioning (f.e. DVC)
- runtime versioning (f.e. Docker)
- testing frameworks
- experiment- and knowledge management (Weights & Bias, MLflow, DVC)
- production environments for machine learning models
- The students are expected to be able to create a workflow for the development of complex data science products |
| Voraussetzungen |
- Experience with Python or R scripts
- Experience in training machine learning models (e.g. linear regression)
- First experiences with the command line (Unix and Windows) |
| Sprache |
Englisch |
| Begrenzung |
max. 25 participants |
| Anmeldung |
To attend the course / exercise, registration via e-learning platform OLAT is required. Registration is possible from 1 – 14 September 2025. The students themselves are responsible for checking the creditability of the course to their course of study.
Direct link to OLAT course: to follow
|
| Prüfung |
***IMPORTANT*** In order to acquire credits, resp. to take the examination, registration via the Uni Portal within 1 - 14 September 2025 is ESSENTIALLY REQUIRED. Further information on registration: www.unilu.ch/wf/pruefungen |
| Abschlussform / Credits |
Written paper / Project report / 6 Credits
|
| Hinweise |
Project work |
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
sandro.cilurzo@doz.unilu.ch / arthur.habicht@doz.unilu.ch |
| Anzahl Anmeldungen |
0 von maximal 25 |
| Literatur |
The Hundred-Page Machine Learning Book (Andriy Burkov) |