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Course Syllabus

Master 2022/2023

Research and Design Seminar "Linguistic Tools for Solving Business Puzzles"

Type: Compulsory course (Applied Linguistics and Text Analytics)
Area of studies: Fundamental and Applied Linguistics
Delivered by: School of Fundamental and Applied Linguistics
Where: Faculty of Humanities (Nizhny Novgorod)
When: 1 year, 2-4 module
Mode of studies: offline
Open to: students of one campus
Instructors: Галочкин Александр Евгеньевич, Yulia V. Balakina, Mariya Khudyakova, Ekaterina Krekhovets, Ерышева Мария Евгеньевна, Леонтович Ольга Аркадьевна
Master’s programme: Прикладная лингвистика и текстовая аналитика
Language: English
ECTS credits: 6
Contact hours: 112

Course Syllabus

Full Syllabus

Abstract

Project seminar Linguistic Tools for Solving Business Puzzles represents an intense multi-functional course with tangible progress. Starting with gathering data online and offline followed with data analysis, students will have to publish results of their work online using WordPress and Tilda. The course presupposes constant interaction with enterprises and work on cases based on real-life day-to-day tasks of modern business.
Learning Objectives

Learning Objectives

  • the development of skills based on qualitative and quantitative methods and techniques that allow to evaluate the characteristics of various communicative situations of political discourse; the development of skills concerning corpus and other computer methods and techniques that allow to evaluate the characteritics of various communicative situations of political discourse; the development of skills using qualitative and quantitative methods and techniques that allow to evaluate individual style features (idiostyle features) of communicants in political discourse; 4. training of masters specializing in political linguistics, to do independent research design and organizational work.
Expected Learning Outcomes

Expected Learning Outcomes

  • Able to choose tools, modern technical means and information technologies to process information for the assigned scientific task in management
  • Using multimedia kits in active learning and project activities
  • Know how to build graphs, how to analyse Big Data using them
  • Use statistics tools to study textual information
  • Use computer tools, making a transition from qualitative to quantitative methods
  • Be able to cope with stress
  • Know how to use search engines
  • Know the rules of academic presentation
Course Contents

Course Contents

  • Teambuilding
  • Projects
  • Conversational design
  • Technical writing
  • Statistics
  • Web-design
  • Stress/time management
  • Data visualization
  • Search engines/data mining
  • Academic project (Master’s thesis)
Assessment Elements

Assessment Elements

  • non-blocking проект
  • non-blocking Activity
Interim Assessment

Interim Assessment

  • 2022/2023 4th module
    0.5 * проект + 0.25 * проект
  • 2023/2024 4th module
    0.5 * 2022/2023 4th module + 0.5 * проект
Bibliography

Bibliography

Recommended Core Bibliography

  • Aishath Nasheeda, Haslinda Binti Abdullah, Steven Eric Krauss, & Nobaya Binti Ahmed. (2019). Transforming Transcripts Into Stories: A Multimethod Approach to Narrative Analysis. International Journal of Qualitative Methods, 18. https://doi.org/10.1177/1609406919856797
  • Chapman, S. J. (2018). Review of Discovering Statistics Using IBM SPSS Statistics, 4th Edition. Journal of Political Science Education, 14(1), 145–147. https://doi.org/10.1080/15512169.2017.1366328
  • Lazega, E., & Snijders, T. A. B. (2016). Multilevel Network Analysis for the Social Sciences : Theory, Methods and Applications. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1119294
  • Гмурман В. Е. - ТЕОРИЯ ВЕРОЯТНОСТЕЙ И МАТЕМАТИЧЕСКАЯ СТАТИСТИКА 12-е изд. Учебник для вузов - М.:Издательство Юрайт - 2021 - 479с. - ISBN: 978-5-534-00211-9 - Текст электронный // ЭБС ЮРАЙТ - URL: https://urait.ru/book/teoriya-veroyatnostey-i-matematicheskaya-statistika-468331
  • Груздев, А. В. Прогнозное моделирование в IBM SPSS Statistics, R и Python: метод деревьев решений и случайный лес : руководство / А. В. Груздев. — Москва : ДМК Пресс, 2018. — 642 с. — ISBN 978-5-97060-539-4. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/123700 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.

Recommended Additional Bibliography

  • Michael Jay Katz. (n.d.). FROM RESEARCH TO MANUSCRIPT From Research to Manuscript A Guide to Scientific Writing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.527E83F3