| Dozent/in |
MSc, Sandro Cilurzo; MSc, Arthur Habicht |
| Veranstaltungsart |
Vorlesung |
| Code |
HS221198 |
| Semester |
Herbstsemester 2022 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Do, 22.09.2022, 16:15 - 19:00 Uhr, HS 12
Do, 06.10.2022, 16:15 - 19:00 Uhr, HS 12
Do, 20.10.2022, 16:15 - 19:00 Uhr, HS 12
Do, 03.11.2022, 16:15 - 19:00 Uhr, HS 12
Do, 17.11.2022, 16:15 - 19:00 Uhr, HS 12
Do, 01.12.2022, 16:15 - 19:00 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 5 - 30 September 2022. The students themselves are responsible for checking the creditability of the course to their course of study.
Direct link to OLAT course:
https://lms.uzh.ch/url/RepositoryEntry/17250386138 |
| Prüfung |
***IMPORTANT*** In order to acquire credits, resp. to take the examination, registration via the Uni Portal within the examination registration period 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@sedimentum.com
arthur.habicht@sedimentum.com |
| Anzahl Anmeldungen |
7 von maximal 25 |
| Literatur |
The Hundred-Page Machine Learning Book (Andriy Burkov) |