COMPUTATIONAL ANALYSIS OF LANGUAGE DATA
| Title | ΥΠΟΛΟΓΙΣΤΙΚΗ ΑΝΑΛΥΣΗ ΓΛΩΣΣΙΚΩΝ ΔΕΔΟΜΕΝΩΝ ΚΑΙ ΣΩΜΑΤΩΝ ΚΕΙΜΕΝΩΝ / COMPUTATIONAL ANALYSIS OF LANGUAGE DATA |
| Code | ΘΕΓ828 |
| Faculty | Philosophy |
| School | Philology |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Common | Yes |
| Status | Active |
| Course ID | 600016035 |
Programme of Study: Glōssologías
Registered students: 1
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| THeōrītikī kai Efarmosménī Glōssología | Elective Courses belonging to the selected specialization | Winter/Spring | - | 10 |
| Istorikī kai Valkanikī Glōssología | Mandatory Elective Courses | Winter/Spring | - | 10 |
| Academic Year | 2018 – 2019 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600131782
|
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600131782
- eLearning (Moodle):
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction)
Learning Outcomes
- Usage of descriptive statistics' indices
- Formulation of research hypotheses and conduction of hypothesis testing for:
- one mean
- two means
- three or more means (one way ANOVA)
- Modeling data with simple and multiple linear regression
- Using data visualization techniques
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
- Work in an international context
- Work in an interdisciplinary team
- Generate new research ideas
- Design and manage projects
- Advance free, creative and causative thinking
Course Content (Syllabus)
Being able to conduct a qualitative linguistic research exploiting state-of-the-asrt statistical methods requires a set of analytical tool that every linguists should be able to know how to handle, nowadays. The course presents basic and advanced methods of language data analysis through the usage of computational means, such as the R programming language as well as other text-analytical software tools. To this end, students will become acquainted with the two stages of processing and analysing language data: a) Language data wrangling and b) formulation of research hypotheses and drawing conclusions based on the most wide-spread statistical methods of language data analysis.
Keywords
language data analysis, statistical methods, R programming language
Educational Material Types
- Notes
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Laboratory Work | 100 | ✓ | ✓ | ✓ | |
| Interactive Teaching in Information Center | 27 | ✓ | ✓ | ✓ | |
| Project | 150 | ✓ | ✓ | ✓ | |
| Written assigments | 100 | ||||
| Total | 377 |
Student Assessment
Student Assessment methods
- Labortatory Assignment (Formative, Summative)
Bibliography
Additional bibliography for study
Gries, S. Th. (2009). Quantitative corpus linguistics with R. Berlin/Boston, MA: De Gruyter
Gries, S. Th. (2013). Statistics for linguistics with R. Berlin/Boston, MA: De Gruyter
Levshina, N. (2015). How to do linguistics with R. Amsterdam/Philadelphia, PA: John Benjamins
Last Update
05-02-2020
