Syllabus
Welcome to Data Science for studying Language and the Mind!
Please note that the organization of the course has changed substantially. Read the syllabus carefully to see if the course will be a good fit for you!
Overview
Description
Data Sci for Lang & Mind is an entry-level course designed to teach basic principles of 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.
Sections
| Section | Time | Location | Staff |
|---|---|---|---|
401 LEC | TR 12-12:59 PM | Dr. Kathryn Schuler | |
402 LAB | R 1:45-2:44 PM | June Choe | |
403 LAB | R 3:30-4:29 PM | Ariana Wiltjer | |
404 LAB | F 12-12:59 PM | Ravi Arya | |
405 LAB | F 1:45-2:44 PM | Avinash Goss |
Materials
Readings
Statistical Modeling: A Fresh Approach 2nd Edition by Daniel Kaplan. A free e-textbook designed to make advanced statistical methods accessible to beginners by emphasizing conceptual understanding of the intuition behind them (models).
Computational and Inferential Thinking: The Foundations of Data Science 2nd Edition by Ani Adhikari, John DeNero, David Wagner. Note that this textbook was developed for the UC Berkeley course Data 8: Foundations of Data Science and focuses on python, but we will reference a few chapters.
Tools & Resources
Course website (you are here) for schedule, syllabus, and links to all assigned materials
Canvas site mostly used for posting grades
Ed Discussion for course announcements and online discussion and support
Gradescope for lab assignments, project checkpoints, and exams
R is an evolving summary of resources related to the R concepts we learn in the course (e.g. cheatsheets, extra learning resources) Content updated throughout the course.
Datasets includes a collection of the datasets we use in the course
Exam study guides will include the study guides for the midterm and final
Components
Lecture
Lectures are held in MOOR 216 on Tuesdays and Thursdays at 12pm. You can also join the lectures live on Zoom. We highly recommend attending live in-person, but we will post recordings of the lectures in case you are sick or unable to attend.
At some point during each lecture, we will ask you to do the Lecture check-in survey. We will not use the lecture check-in as part of your grade. We are collecting this data to understand how student engagement styles influence course performance or enjoyment. Data science!
We may also use the survey data to determine whether to permit make-up work. If you are engaged with most lectures, we will be more likely to offer options like extensions or exam re-takes. If you rarely attend or engage with the lecture material, we will be more likely to offer options like taking an incomplete or withdrawing from the course.
Lab
Weekly in-person labs consist of working on lab assignments or project checkpoints with small groups of peers (see sections for available times and locations). Attending lab is a mandatory part of your grade. To receive credit, you must attend in person and make significant progress on the week's assignment.
Lab assignments will be released on Thursday afternoons by 1:45pm. You will be asked to submit your in-progress lab assignment to Gradescope at the end of each lab session to receive participation credit (whether you have finished or not). You will have until Wednesday night at 11:59pm to submit the final version you would like us to grade.
Lab sessions will not be recorded. Late assignments will not be accepted.
Project
Group project details will be released on the Project Guidelines page during week 3.
Exams
The midterm and final will be pen and paper exams designed to test your conceptual understanding of the material covered in the course. Both the midterm and final will be closed book, closed note, and held in person.
The midterm is scheduled during your regular lab section in week 8. The final will be scheduled during finals week (time and date TBD). Note that the final is required to pass the course.
Grading
Components
Course components contribute to your final grade according to the following table. Assignments within each category are weighted equally.
| Component | Grade |
|---|---|
Lecture Check-ins | 0% - not used for grading |
Lab Participation | 10% |
Lab Assignments | 20% |
Project | 25% |
Midterm | 15% |
Final | 30% |
Letter grades
The table below shows the minimum score before rounding for letter grades. Grading is not on a curve: there is room for everyone to do well!
| Letter | Minimum score (before rounding) |
|---|---|
A+ | 97% |
A | 94% |
A- | 90% |
B+ | 87% |
B | 84% |
B- | 80% |
C+ | 77% |
C | 74% |
C- | 70% |
D+ | 67% |
D | 64% |
D- | 60% |
Policies
Late assignments
Late lab assignments will not be accepted. We drop your two lowest lab assignment grades. Project checkpoints will be accepted up to 24 hours late with no penalty.
Regrade requests
If you notice a grading mistake, you must make a regrade request on Gradescope before the regrade deadline. If you ask about grading in person or via email, you’ll be directed to make a formal regrade request in Gradescope.
Accommodations
We are happy to provide accommodations to anyone with documentation from Student Disability Services and to make alternate arrangements when class conflicts with a religious holiday. Please notify your lab section TA as soon as possible to make these arrangements.
Academic integrity
We will follow the rules of the University and the Code of Academic Integrity. It is your responsibility to be familiar with these policies.
Support
Asking for help is a sign of strength! We hope you’ll reach out to us if you need help. We also want you to be aware of Penn’s Academic & Wellness Resources.
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