Fall 2026 - LING 380 OL01
Practical Skills in Linguistics (1)
Class Number: 5799
Delivery Method: Online
Overview
Description
CALENDAR DESCRIPTION:
Training in a single linguistics-related practical skills topic in each offering. This course can be taken up to a total of three times for credit when taught under different topics. Graded on a pass/fail basis.
COURSE DETAILS:
The course introduces basic concepts and tools for text analysis using the python programming language. It will address data capture and manipulation, data cleaning and preprocessing, and text analysis for linguistics and other social sciences. Topics include:
- Introduction to python
- Ethical acquisition and management of data
- Data cleaning and preprocessing
- Regular expressions, n-grams
- NLTK and spaCy
- Named entity recognition
- Pandas and data processing
- Basics of machine learning
- Natural language processing: sentiment analysis
- Humanities: authorship attribution, style comparison
- Social sciences: quote extraction, processing survey answers, topic modelling
More specifically, students will:
- learn core concepts of programming (variables, functions, objects);
- learn to install and use packages for text analysis (NLTK, spaCy);
- be able to collect and store a dataset using existing python packages;
- clean and normalize language data;
- perform natural language processing analysis on language data, for linguistic analysis and for other humanities and social sciences.
COURSE DELIVERY:
Online. This is a 1-credit course with about 1 hour of instructional material delivered through Canvas.
Grading
NOTES:
COURSE GRADING:
Note this course is a pass/fail course. A pass grade will be awarded if the student:
• participates in class activities
• completes weekly online discussions
• completes weekly programming labs
• submits assignments that follow the guidelines
REQUIREMENTS:
PLATFORM(S) USED:
Canvas
TECHNOLOGY REQUIRED:
Computer for checking Canvas and doing class assignments and projects. Computer capable of running python and where new software can be installed.
Materials
REQUIRED READING:
Taboada, M. (2026) Python for Text Analysis. https://maitetaboada.github.io/python_text_analysis/. DOI: 10.5281/zenodo.18156442
Department Undergraduate Notes:
Students should familiarize themselves with the Department's Standards on Class Management and Student Responsibilities.
Please note that a grade of “FD” (Failed-Dishonesty) may be assigned as a penalty for academic dishonesty.
All student requests for accommodations for their religious practices must be made in writing by the end of the first week of classes or no later than one week after a student adds a course.
Registrar Notes:
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.
To learn more about the academic disciplinary process and relevant academic supports, visit:
- SFU’s Academic Integrity Policy: S10-01 Policy
- SFU’s Academic Integrity website, which includes helpful videos and tips in plain language: Academic Integrity at SFU
RELIGIOUS ACCOMMODATION
Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.