Кто читает:: Школа лингвистики

Статус:: Курс обязательный

Когда читается:: 1-й курс, 3, 4 модуль

Преподаватель

Губбиотти Алессандро

Full Syllabus

Abstract

First year: The course is devoted to modern methods of data analysis, as applied to linguistic data, including methods of statistical inference and explanatory data analysis with visualizations. We begin with theoretical background in mathematical statistics and discuss limitations of statistical methods and their applicability to linguistical problems. From practical point of view, we use R system to do actual analysis with real datasets. We also discuss different visualization techniques using popular library ggplot2. Second year: Preprocessing of linguistic data in Python is designed to further the students’ knowledge of natural language processing and to polish their programming skills. The course aims to provide the students with the programming and natural language processing knowledge and competencies necessary to plan and conduct research projects of their own leading to the M.Sc. dissertation and scientific publications.

Learning Objectives

Within this course you will: ● learn about the principal steps of a quantitative research in linguistics; ● learn about the possibilities and limitations of quantitative approaches as applied to different research questions; ● learn to formulate research questions and develop them into testable hypotheses; ● explore the possibilities of data collection and different approaches to sampling; ● learn to evaluate the quality of a quantitative approach; ● study the most common corpus, experimental, and mixed design of the linguistic studies and learn to evaluate research plans, discover and prevent the associated threats to data validity; ● practice in preparing your quantitative data for analysis, evaluating the quality of your data; treating missing data; ● learn about the possibilities and limitations of conventional statistical techniques and criteria, as well as some popular contemporary multivariate statistical methods; ● learn to choose and apply in practice a set of appropriate statistical tests for your research question.
to further the students' programming skills

Expected Learning Outcomes

are able to account for basic types of data used in linguistic research
are able to apply basic quantitative methods for analysing linguistic data
are able to apply different techniques for presenting both qualitative and quantitative linguistic data in scholarly writing
are able to critically discuss the limitations of commonly used methods for answering research questions about language
are able to critically evaluate linguistic data presented in previous research
are able to reason on how to interpret linguistic results, including how to evaluate what kind of information a given method can offer and how to estimate the potential range of variables that can affect results in linguistic research

Course Contents

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Python: Different data formats
Python: preprocessing
Python: vector representations
Python: graphs
Python: API

Assessment Elements

homework assignments
project
assignment 1
assignment 2
assignment 3

Interim Assessment

2022/2023 4th module
0.65 * homework assignments + 0.35 * project
2023/2024 2nd module
0.3 * assignment 1 + 0.4 * assignment 3 + 0.3 * assignment 2

Bibliography

Recommended Core Bibliography

Hetland, M. L. (2017). Beginning Python : From Novice to Professional (Vol. Third edition). New York: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174463
Taieb, D. (2018). Data Analysis with Python : A Modern Approach. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1993344
Wickham H. ggplot2: elegant graphics for data analysis. Second edition. Cham: Springer, 2016. 260 p.

Recommended Additional Bibliography

Stowell, Sarah (2014). Using R for Statistics. Apress. https://link.springer.com/book/10.1007%2F978-1-4842-0139-8

Магистерская программа «Лингвистическая теория и описание языка»

Linguistic Data: Quantitative Analysis and Visualisation