Course Details

Course Name
Specialisation in Digital Humanities in the Applied Computer Science M.Sc.
Course Type
Master
Institution
Georg-August-Universität Göttingen
Department
Computer Science
Information
http://www.gcdh.de/en/teaching/studying-dh/dh-ai-msc/
Curriculum
http://www.gcdh.de/en/teaching/dh-related-seminars/
Status
record actively maintained
Course Language
English
Start Date
2014-10-01
recurring
ECTS
120
Lecturer
Prof. Dr. Caroline Sporleder
PID
Disciplines: Archaeology, Arts and Cultural Studies, History, Linguistics and Language Studies, Literary and Philological Studies, Other, Social Sciences
Techniques: Cluster Analysis, Concordancing, Distance Measures, Encoding, Georeferencing, Information Retrieval, Linked Open Data, Machine Learning, Named Entity Recognition, Pattern Recognition, POS-Tagging, Searching, Sequence Alignment, Topic Modeling, Text Mining
Objects: DigitalHumanities, Data, Language, Literature, Map, Metadata, NamedEntities, Projects, Research, ResearchProcess, Software, Text, Visualization, Speech
Access Requirements
Applicants for the Applied Computer Science MSc must hold a Bachelor degree in Computer Science (or equivalent) with at least 180 ECTS-Credits. Furthermore, you need proficiency in German (DSH 2) and English (CEFR B2).
Description
After the completion of the dual-subject Bachelor of Arts in Applied Computer Science plus one discipline within the humanities or social sciences, you can specialise, starting from winter semester 2014/15, in the field of digitial humanities in the Applied Computer Science MSc. Applied computer science deals with the interface between computer science and a field of application. In the Applied Computer Science MSc you will deepen your knowledge in core computer science and at the same time you will learn about subject-specific areas of your chosen specialisation. Usually, the completion of the course takes four semesters (120 ECTS-Credits).
Keywords
Disciplines: Archaeology, Arts and Cultural Studies, History, Linguistics and Language Studies, Literary and Philological Studies, Other, Social Sciences
Techniques: Cluster Analysis, Concordancing, Distance Measures, Encoding, Georeferencing, Information Retrieval, Linked Open Data, Machine Learning, Named Entity Recognition, Pattern Recognition, POS-Tagging, Searching, Sequence Alignment, Topic Modeling, Text Mining
Objects: DigitalHumanities, Data, Language, Literature, Map, Metadata, NamedEntities, Projects, Research, ResearchProcess, Software, Text, Visualization, Speech