Universität Wien

136010 UE Introduction to DH Tools and Methods (2021W)

Prüfungsimmanente Lehrveranstaltung
DIGITAL

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Freitag 08.10. 09:45 - 11:15 Digital
Freitag 15.10. 09:45 - 11:15 Digital
Freitag 22.10. 09:45 - 11:15 Digital
Freitag 29.10. 09:45 - 11:15 Digital
Freitag 05.11. 09:45 - 11:15 Digital
Freitag 12.11. 09:45 - 11:15 Digital
Freitag 19.11. 09:45 - 11:15 Digital
Freitag 26.11. 09:45 - 11:15 Digital
Freitag 03.12. 09:45 - 11:15 Digital
Freitag 10.12. 09:45 - 11:15 Digital
Freitag 17.12. 09:45 - 11:15 Digital
Freitag 07.01. 09:45 - 11:15 Digital
Freitag 14.01. 09:45 - 11:15 Digital
Freitag 21.01. 09:45 - 11:15 Digital
Freitag 28.01. 09:45 - 11:15 Digital

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The course is aimed at providing students with the skills necessary to understand the sheer potential of the digital methods for the humanities, using the Python Programming Language for a handful of common tasks in the domain. The course will present a broad overview of methods and tools, specifically covering the following: OCR & Natural Language Processing (NLP) Pipelines, Visualization & Dashboards, Spatial Analysis, Image Analysis, Social Network Analysis (SNA), Sentiment Analysis, SQL and NoSQL Database Management. The course approach is both theoretical and practical, with an intense load of hands-on exercises. The students are expected to have familiarity with digital environments, and previous practice with programming is desired, but not mandatory.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Course evaluation will be a combination of in-class participation (30%), weekly homework assignments (40%), and the final project (30%).

Mindestanforderungen und Beurteilungsmaßstab

Attendance is required; regular participation is the key to completing the course; all students must provide their computing environment; homework assignments must be submitted on time (some can be completed later as a part of the final project, but this must be discussed with the instructor whenever the issue arises); the final project must be submitted on time.

Prüfungsstoff

There is no examination for the course.

Literatur

Learning Python, 5th Edition by Mark Lutz, O'Reilly Media, 2013. ISBN 978-1-4493-5573-9.

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinny, O'Reilly Media, 2012. ISBN 978-1-4493-1979-3

Github Repository - https://github.com/rsouza/Python_Course

Programming historian → relevant courses
https://programminghistorian.org/en/lessons/

TED Talk - https://www.ted.com/talks/reshma_saujani_teach_girls_bravery_not_perfection

Zuordnung im Vorlesungsverzeichnis

DH-S I

Letzte Änderung: Fr 12.05.2023 00:16