Analyzing linguistic data: schedule


This schedule is subject to change.

Assignments are due at class time (2pm) on their due date. Please submit assignments online on Canvas unless the assignment tells you otherwise.

Readings can be done either before or after class (unless noted otherwise); they are chosen to support the material covered in class.

Week 1

Week 2

Week 3

  • Feb 3 Python basics: conditions and lists
  • Feb 5 Python basics: loops

  • Feb 7 What can I do for my project?

Week 4

Week 5

  • Feb 17 Descriptive statistics continued

  • Feb 19 We talk in class about your project ideas.

  • Feb 21 Probability distributions and what we can do with them
    • Readings: SE ch. 3

Week 6

  • Feb 24 Probability distributions, samples, and populations

  • Feb 26 Statistical tests: the basic principle; significance thresholds
    • Feb 28  The t-test
      • Readings: SE ch. 7

    Week 7

    • Mar 2 The t-test and confidence intervals
      • Readings: SE ch. 8, 9

    • Mar 4 Python: defining your own functions, and structuring your programs
      • Homework 2 due.

    • Mar 6 Python functions, classes, and objects

      Week 8

      • Mar 9 Comparing more than two data sets: ANOVA
        • Pitfalls of statistical analysis: The problem of multiple testing https://xkcd.com/882/
        • Readings: SE ch. 11, 12

      • Mar 11 ANOVA, continued

      • Mar 13 Different tests for different types of data

      Week 9

      Spring break

      Week 10


      Week 11

      • Mar 30 Logistic regression

      • April 1 Model comparison
        • Intermediate project report due.

      • April 3 Practicing regression

      Week 12

      • April 6 Practicing regression

      • April 8  Python: advanced pandas: sorting data frames
        • Homework 3 due.

      • April 10 Python pandas: grouping, merging, appending data frames

      Week 13

      • April 13 Python: advanced data visualization

      • April 15 Python: advanced data visualization, continued

      • April 17 Python: list comprehensions, and exceptions

      Week 14

      • April 20 Clustering for data analysis

      • April 22 More clustering
        • Homework 4 due.

      • April 24 Clustering and visualization

      Week 15

      • April 27 Classification

      • April 29 Classification continued

      • May 1 Experimenting with classification

      Week 16

      • May 4: Project presentations

      • May 6: Project presentations

      • May 8: Project presentations

      Final report due: tba
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