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ALD_Syllabus

Syllabus for Analyzing Linguistic Data: LIN392


Course Information

  • Course: Analyzing Linguistic Data, LIN392
  • Course unique number: 40895
     
  • Semester: Spring 2012
  • Course website: http://analyzing-linguistic-data.utcompling.com

Instructor Contact Information

  • office hours: Tuesday 3:30-5pm, Wednesday 11-12:30am
  • office: Calhoun 514
  • phone: 471-9022
  • fax: 471-4340
  • email: bannard at mail dot utexas dot edu
  • office hours: Monday 1-3pm, Tuesday 10-11am
  • office: Calhoun 512
  • phone: 471-9020
  • fax: 471-4340
  • email: katrin dot erk at mail dot utexas dot edu

Prerequisites

Graduate standing.

Syllabus and Text

This page serves as the syllabus for this course.

Official course textbooks:

We will also make use of other readings, which will be made available on the course website.

Exams and Assignments

Assignments will be updated on the Assignments page. A tentative schedule for the entire semester is posted on the Schedule page. Readings and exercises may change up a week in advance of their due dates. There is an end-of-term project for the course, where students will be expected to choose a dataset that they intend to analyze.

Philosophy and Goal

Many research topics in linguistics require or can benefit from sophisticated statistical analysis of language datasets. This course will introduce fundamental concepts that will enable students to formulate quantitatively-oriented research questions and answer them with appropriate visualization, modeling and testing. Students are expected to learn these techniques, apply them to data sets provided in languageR and by us, and generalize them to a dataset of their own choice.

We use the R programming language, which allows much more flexible and customizable ways of performing such exploration and analysis, compared to statistical packages based on point-and-click interfaces (like SPSS). It also forms a strong basis for using more complex modeling techniques than are covered in this course—including writing one's one code to do so.

Content Overview

This course provides hands-on introduction to statistics for language, using the R programming language. Using data from existing linguistic studies, we will study the following topics:

  • data exploration through visualization
  • probability distributions
  • mean and standard deviation of a single dataset
  • comparing pairs of datasets and hypotheses:testing for statistical significance
  • regression modeling
  • clustering for data exploration

Course Requirements

  • Homeworks (12% each): 5 homeworks will be assigned during the course
  • Project proposal (5%)
  • Project progress report (5%)
  • Project presentation (5%)
  • Project final report (25%)

Extension Policy

Homework must be turned in on the due date in order to receive credit. Extensions will be considered on a case-by-case basis and only if the student asks for the extension before the deadline.

Points will be deducted for lateness. By default, 10 points (out of 100) will be deducted for lateness, plus an additional 5 points for every 24-hour period beyond 2 that the assignment is late. For example, an assignment due at 11am on Tuesday will have 10 points deducted if it is turned in late but before 11am on Thursday. It will have 15 points deducted if it is turned in by 11am Friday, etc.

Late submissions will not be accepted if they are more than one week past the deadline. No points will be received in this case.

The greater the advance notice of a need for an extension, the greater the likelihood of leniency.

Academic Dishonesty Policy

You are encouraged to discuss assignments with classmates. But all written work must be your own. Students caught cheating will automatically fail the course. If in doubt, ask the instructor.

Notice about students with disabilities

The University of Texas at Austin provides appropriate accommodations for qualified students with disabilities. To determine if you qualify, please contact the Dean of Students at 512-471-6529 or UT Services for Students with Disabilities. If they certify your needs, we will work with you to make appropriate arrangements.

UT SSD Website: http://www.utexas.edu/diversity/ddce/ssd

Notice about missed work due to religious holy days

A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.


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