Course Unit Page

Academic Year 2021/2022

Learning outcomes

The student is able to autonomously identify a problem issue of professional import, and to plan and carry out a research project that contributes to solve it; s/he is able to report orally and in writing about project planning and execution, complying with the standards of the field in terms of communication of research.

Course contents

The course is connected to the compulsory company/institution internship and is intended to provide the students with the basic prerequisites for conducting research in a professional setting, as well as with guidance for the implementation of their own research projects, to be realised during the internship. Within the taught part of the course the students will learn about what it means to conduct research on language, and about how to use some of the computational tools that can help them in the process. Core issues in scientific research and computational thinking will be covered first, followed by those in industry-based research.

The specific topics will be the following:

0.Introduction to the course

1. Scientific thinking

  • Science and scientific research
  • Types of (scientific) research
  • Stages of scientific research
  • Researching language
  • Communicating research
  • PhD Research

2. Computational thinking

  • Into computers and programming
  • Regular expressions
  • Programming in Python
  • Jupyter notebooks
  • Methods and classes
  • Dealing with text

3. Industry thinking

  • Invited talks by industry representatives


Lecture slides and other materials used during class will be made available.


Scientific thinking

Litosseliti, L. (Ed.) (2010). Research Methods in Linguistics. London: Continuum. or Litosseliti, L. (Ed.) (2018). Research Methods in Linguistics. 2nd edition. London: Bloomsbury Publishing.

Phakiti, A., P. De Costa, L. Plonsky & S. Starfield (Eds) (2018). The Palgrave Handbook of Applied Linguistics Research Methodology. London: Palgrave MacMillan.

Podesva, R. J. & D. Sharma (2013). Research Methods in Linguistics. Cambridge: Cambridge University Press.

Puskas, G., M. Miličević Petrović & T. Samardžić (2019). Introduction to research in linguistics: theory, logic, method [Part 2: Methodology, Part 3: Technical skills]. Online course. Freely available (upon registration) at https://phil.openedx.uzh.ch/courses/course-v1:PHIL+Movetia101+2046/about

Stefanowitsch, A. (2020). Corpus linguistics: A guide to the methodology. Berlin: Language Science Press. Freely available

Zanettin, F. & C. Rundle (Eds) (2022). The Routledge Handbook of Translation and Methodology. London: Routledge.

Computational thinking

Silvio Peroni. Computational Thinking and Programming.

Industry thinking

European Language Industry Survey (2020).

Miličević Petrović, M., Bernardini, S., Ferraresi, A., Aragrande, G., and Barrón-Cedeño, A. (2021). Language data and project specialist: A new modular profile for graduates in language-related disciplines.

Teaching methods

A combination of lectures and interactive seminars for the taught part, individual work and consultations for the projects


As concerns the teaching methods of this course unit, all students are required to attend the online Modules 1 and 2 on Health and Safety.

Assessment methods

The course will be evaluated on the basis of two projects:

1. Pilot project (20%). At the beginning, the student will be assigned a small project in which they will be asked to solve a practical problem during the course. The student will produce a final report.

2. Final project (80%). The topic of the final project will be defined jointly by the student, the company in which they will carry out the internship and the academic tutor. The student will produce a written report and will give an oral presentation of the implemented research project.

Grading scale

  • 30 - 30L Excellent. The student has acquired all targeted concepts, and has conducted a methodologically sound project that s/he clearly presented in the report and the oral presentation.
  • 27 - 29 Above average. The student has a very good command of the targeted concepts, with some minor errors or inconsistencies in project implementation and/or presentation.
  • 24 - 26 Generally sound. The student has a generally good command of the targeted concepts, but with larger gaps or inconsistencies in project implementation and/or presentation.
  • 21 - 23 Adequate. The student has just an adequate command of the targeted concepts and displays significant shortcomings in project implementation and/or presentation.
  • 18 - 20 Minimum. The student has only grasped the basic targeted concepts and was only partially successful in implementing a project.
  • < 18 Fail. The student does not reach a minimum threshold of knowledge and was unsuccessful in implementing a project.

Teaching tools

The course materials will be made available on virtuale. The students will also need to use Jupyter notebooks for Python.

Office hours

See the website of Luis Alberto Barron Cedeno

See the website of Adriano Ferraresi

See the website of Maja Milicevic Petrovic