• A
  • A
  • A
  • АБВ
  • АБВ
  • АБВ
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта

Linguistic Data: Quantitative Analysis and Visualisation

Учебный год
Обучение ведется на английском языке
Курс обязательный
Когда читается:
1-й курс, 3, 4 модуль


Course Syllabus


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

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

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

Course Contents

  • №1
  • №2
  • №3.
  • №4.
  • №5.
  • №6.
  • №7.
  • №8.
  • №9.
  • №10.
  • №11.
  • №12.
  • Python: Different data formats
  • Python: preprocessing
  • Python: vector representations
  • Python: graphs
  • Python: API
Assessment Elements

Assessment Elements

  • non-blocking homework assignments
  • non-blocking project
  • non-blocking assignment 1
  • non-blocking assignment 2
  • non-blocking assignment 3
Interim Assessment

Interim Assessment

  • 2023/2024 4th module
    0.65 * homework assignments + 0.65 * homework assignments + 0.35 * project + 0.35 * project
  • 2024/2025 2nd module
    0.3 * assignment 1 + 0.3 * assignment 1 + 0.3 * assignment 2 + 0.3 * assignment 2 + 0.4 * assignment 3 + 0.4 * assignment 3


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