Data Science for studying language and the mind

Fall 2024

Announcements

There are 3 options for those wishing to take the final exam:

  • Thursday, December 19, 2024: 12:00pm to 2:00pm in DRLB A8

If you can’t make that official time, we can proctor 2 alternatives:

  • Tuesday, December 10 from 12:30pm to 2:30pm in the Linguistics Department
  • Thursday, December 12 from 12:30pm to 2:30pm in the Linguistics Department

Those with accommodations can request to take the exam with Weingarten any day between Dec 10th and Dec 19th.

The final is in person, on paper, closed book/note/computer/phone, exactly like exams 1 and 2. The final will cover Sampling Distribution and Hypothesis testing from exam 1, and all of exam 2. Final exam study guide

Welcome to Data Science for Studying Language & the Mind! The Fall 2024 course information and materials are below. Course materials from previous semesters are archived here.

Syllabus

Course description: Data Sci for Lang & Mind is an entry-level course designed to teach basic principles of statistics and data science to students with little or no background in statistics or computer science. Students will learn to identify patterns in data using visualizations and descriptive statistics; make predictions from data using machine learning and optimization; and quantify the certainty of their predictions using statistical models. This course aims to help students build a foundation of critical thinking and computational skills that will allow them to work with data in all fields related to the study of the mind (e.g. linguistics, psychology, philosophy, cognitive science, neuroscience).

Prerequisites: There are no prerequisites beyond high school algebra. No prior programming or statistics experience is necessary, though you will still enjoy this course if you already have a little. Students who have taken several computer science or statistics classes should look for a more advanced course.

Instructor: Dr. Katie Schuler (she/her)

TAs: Brittany Zykoski and Wesley Lincoln

Lectures: Tuesdays and Thursdays from 12 - 1:29pm in COHN 402.

Labs: Hands-on practice and exam prep guided by TAs.

  • 402: Fri at 3:30p in WILL 201 with Brittany
  • 403: Thu at 1:45p in WILL 321 with Wesley
  • 404: Thu at 3:30p CANCELLED
  • 405: Fri at 12:00p in WILL 316 WILL 306 with Wesley
  • 406: Thu at 5:15p in TBD with Brittany

Office Hours: You are welcome to attend any office hours that fit your schedule. The linguistics department is located on the 3rd floor of 3401-C Walnut street, between Franklin’s Table and Modern Eye.

  • Katie Schuler: 11:30-12:30 on Fridays in 314C
  • Brittany Zykoski: 10:30-11:30 on Wednesdays in 325C
  • Wesley Lincoln: 2-3 on Mondays in 325C

Grading:

  • 40% Homework (equally weighted, lowest dropped)
  • 60% exams (equally weighted, final is optional to replace lowest exam)

Collaboration: Collaboration on problem sets is highly encouraged! If you collaborate, you need to write your own code/solutions, name your collaborators, and cite any outside sources you consulted (you don’t need to cite the course material).

Accomodations: We will support any accommodations arranged through Disability Services via the Weingarten Center. Please make arrangements as soon as possible (1-2 weeks in advance).

Extra credit: There is no extra credit in the course. However, students can submit any missed problem set or exam by the end of the semester for half credit (50%). To ensure fair treatment, all students will receive a 1% “bonus” to their final course grade: 92.54% will become 93.54%.

Regrade requests Regrade requests should be submitted through Gradescope within one week of receiving your graded assignment. Please explain why you believe there was a grading mistake, given the posted solutions and rubric

Resources

In addition to our course website, we will use the following:

Other helpful materials and resources:

Please consider using these Penn resources this semester:

Materials

Study guides

Study guides include weekly study guides and additional resources from each week, including slides, demos, and further reading.

Problem sets

There are 6 problem sets, due to Gradescope by noon on the following Mondays. You may request an extension of up to 3 days for any reason. After solutions are posted, late problem sets can still be submitted for half credit (50%). If you submit all 6 problem sets, we will drop your lowest.

Exams

There are 2 midterm exams, taken in class on the following dates. Exams cannot be rescheduled, except in cases of genuine conflict or emergency (documentation and a Course Action Notice are required). However, you can submit any missed exam by the end of the semester for half credit (50%). You may also replace your lowest midterm exam score with the optional final exam.

Lab exercises

Lab exercises are intended for practice and are not graded.

Schedule

Week Begins Topic Practice Due on Monday
1 Aug 26 R Basics Lab 1
2 Sep 2 Data visualization Lab 2
3 Sep 9 Data import, tidy, wrangle Lab 3 Problem set 1
4 Sep 16 Sampling distribution Lab 4
5 Sep 23 Hypothesis testing (Thu)
Exam 1 review (Fri)		 Lab 5 Problem set 2
6 Sep 30 Exam 1 (Tue in class)
Fall break (Thu)
7 Oct 7 Model specification Lab 6
8 Oct 14 Applied model specification Lab 7
9 Oct 21 Model fitting Lab 8 Problem set 3
10 Oct 28 Model accuracy Lab 9
11 Nov 4 Election day & conference (no class) No lab Problem set 4
12 Nov 11 Model reliability (Tue)
Classification (Thu)		 Lab 10
13 Nov 18 Exam 2 review (Tue)
Exam 2 (Thu)		 Problem set 5
14 Nov 25 Thanksgiving break (no class)
15 Dec 2 Model extrapolation (Tue)
Multilevel models		 Lab is Pset 6 support
16 Dec 9 Last day of classes (no class) Problem set 6
17 Dec 19 Final exam at noon (optional)