Data Science for Linguists 2021

Course home for
     LING 1340/2340

• Policies
• Term project guidelines
• Learning resources by topic
• Schedule table

Course Policies

The Openness Principle, Your Work, and Privacy

Data science, as a young interdisciplinary field, has fully embraced the principle of open, transparent and collaborative mode of scientific inquiry. The overwhelming popularity of GitHub speaks volumes to this ethos. In this class, we too will adopt the principle of openness in everything we do – be it coding, data development or linguistic research. As a matter of fact, learning the practice of openness is considered one of the central pedagogical aims of this course. However, I recognize that the course is foremost a learning platform and therefore there exist certain expectations of privacy in the work students submit. In order to balance the two considerations, the course will adopt the following, tiered, privacy policies:

  1. Fully public. Individual students’ term projects will be completely open: projects will be developed and submitted via a GitHub public repository, where they are open to the world to view. Same goes with some exercise-type assignments.
  2. Private to the world, shared within class. Submission of homework assignments as well as distribution of data sets with limited access will be done via a private GitHub repository. Students’ work therefore will be fully visible to their classmates but won’t be shared with the world. This promotes collaborative learning among students while ensuring a reasonable level of privacy for their work.
  3. Private. Lastly, CourseWeb will be utilized for the aspects of course work that should remain strictly private between a student and the instructor. Those include posting of grades and feedback on student performance.

Course Requirements

As a rule, there will always be a form of assignment between classes. Make sure to keep up with them.

  1. Homework Assignments (50-80 points each): Most of these assignments will involve some type of problem solving through coding.
  2. To-do Tasks (10 points each): These will be mini tasks that are assigned between classes. They will comprise smaller tasks focused more on practical aspects, such as learning new software, information gathering, and peer feedback.
  3. Readings and Self-Guided Learning: You will have articles to read and various online tutorials and book chapters to study, as outlined in the Learning Resources page.
  4. Term Project (400 total points): In addition to the assignments and readings, you are expected to make steady progress on your term project. See the Schedule Table for the milestone dates and the Term Project Guidelines page for details of this requirement.

Your Grade

Grading scale:

97-100%A+87-89.99 B+77-79.99 C+67-69.99 D+0-59.99 F
93-96.99A83-86.99 B73-76.99 C63-66.99 D
90-92.99A- 80-82.99B- 70-72.99C- 60-62.99D-

Requirements and grade calculation:

RequirementWeightTotal pointsNotes
Term project40%400 points
Homework assignments35%50-80 points each
To-do tasks15%10 points each
Attendance & Participation10%70 points (attendance)
+ 30 points (participation)
2 missed classes allowed;
-5 points for each absence thereafter


Grading Rubrics

  1. Term project: see the Term Project Guidelines page.

  2. To-do tasks: These will be graded on a completion-basis. Full 10 points for satisfactory completion; 5 points for unsatisfactory (70% or below) work; 0 for no credit.

  3. Homework assignments: Most homework assignments will involve Python programming as a component as well as learning objectives that are specific to them. Some important notes:

    • When it comes to the coding component, having a Python script produce the desired, correct output is considered only part of the requirement. That is, correctly functioning code is not enough to garner full points. There are many ways to write a piece of code, and some are more elegant, computationally efficient, better organized and easier to understand than others; unlike in LING 1330/2330, those considerations will be given more weights in this course.

    • Homework submissions are meant to be shared in this course, with students encouraged to learn from each other’s work. To that end, an emphasis will be placed on the communicative and presentational aspect: a Python script and any other work submitted is expected to be accompanied by well-written and clear documentation designed to present the work to an audience in a way that facilitates their understanding.

Collaboration on Assignments

If done properly, working together on assignments lead to a better learning outcome for all parties involved. If done improperly, however, it negatively affects learning AND results in cheating. For your learning benefit, I am allowing group work provided that the following conditions are met:

  1. Equal contribution: one student’s contribution must not exceed 150% of other’s.
  2. Individual work before a study group: do not show up to a study session without having worked on the assignment on your own beforehand.
  3. Individual work after a study group: do not write up your homework assignments while working in group, which leads to copying other’s answers. Always finish up your answers by yourself afterwards, using your own words.
  4. Do NOT pass files: do not, under any circumstances, send or receive script files, even if they originated from the homework template. Doesn’t matter if you intend to modify them afterwards or write your own: building on someone else’s code is cheating.

Additional Notes

Remediation and Justification

Policies on late work are noted above. Exceptions may be made, at the instructor’s discretion, in the case of true emergencies, for which explicit documentation must be provided. Penalties for late work or absences may be waived for documented emergencies. For planned, justified, absences or late work, advance notice must be provided. Valid reasons for missing class include the following:

Academic Integrity

Cheating will not be tolerated. Violations, including plagiarism, will be seriously dealt with, and could result in a failing grade for the entire course. Working together on assignments is allowed strictly under a set of conditions noted above; failing to meet them will amount to a violation of academic integrity and will be handled accordingly. For all other issues of academic integrity, refer to the University Guidelines on Academic Integrity here.


If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and DRS (Disability Resources and Services), 140 William Pitt Union, 412-648-7890or 412-838-7355 (TTY) as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.