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BSOS233: Data Science for the Social Sciences

Section Syllabus, Autumn 2025

Course Description

Welcome to BSOS233! My name is Dr. Jacob Coutts (call me Dr. J or Dr. Coutts). Your brilliant graduate lab instructor (GTA) Francis Lavoe and I are excited to teach you all things social, data, and science this semester. This class is an introduction to Python programming and modern methods of data analysis for data science application in the social sciences.

This course emphasizes teaching students who have no previous coding experience how to analyze data and extract meaning in a variety of social science contexts (e.g., psychology, marketing, and political science). You will develop fundamental programming skills and learn statistical/computational thinking through examples and projects with real-world relevance.

Learning Outcomes

After successfully completing this course, you will be able to:

Use Python to apply statistical

analyses to social issues.

Describe the difference between

theoretical and empirical statistical

concepts.

Understand when and why to

apply different data science

techniques to different problems.

Develop and apply functions in

real-world contexts.

Use basic machine learning

models to extract meaning from

data.

Explain to others the importance

of quantitative methodology in

social science settings right now

and in the future.

Required Resources

Ani Adhikari & John DeNero, The Foundations of Data Science.

https://www.inferentialthinking.com/.Application/Software

The textbook is required for this class. The good news? It’s FREE. (Open science and learning is important to me, as is not assigning expensive texts.) Labs, assignments, quizzes, and everything else will be based on or connected to the readings from the textbook, so it’s important to actually read it. I may not be able to cover all the material from the textbook during class, but you are still responsible for knowing it. Readings should be completed before coming to class to optimize your learning experience. Any additional readings (required or optional) for this course will be posted on ELMS.

Course Structure

This course is entirely in-person (both the lecture and the lab). For lecture, we will meet each Monday and Wednesday in TWS 0310 from 10:00-10:50 AM. Lectures will be taught by Dr. J. Labs will be taught by your GTA at the times and places scheduled at the top of the syllabus. Although attendance is not tracked for points in either lecture or lab, you are responsible to know the material we cover each day. Coming to class is important to ask questions, hear others’ questions, and get a head start on or hints about the coursework. There will also be regular opportunities for you to provide feedback for things that you (dis)like about the class in class. (Though you can also bring these comments to our attention over email or during student hours.)

- You will need access to a computer for each class session, as we will often do coding in- class (or be accessing data/software in the cloud).

- We will be doing all our coding in the JupyterHub cloud environment. You can access this via the link on ELMS. You do not need anything installed on your computer for this. We will go over accessing the environment on the first day of class.

Tips for Success in this Course

1. Attend Student Hours. You may have heard about “office hours,” but I feel like this term does not adequately convey what this time is for. It is for YOU. During this time, we’re available for you to ask questions about class, graduate school, careers in data science or statistics, internships, professional development, or whatever other questions you may have. Meetings can be held either in-person or via Zoom—whichever you are more comfortable with.

2. Participate. Although lectures are an integral part of class, the course is just as much based on demonstrations and discussions. We invite you to ask questions about anything that is on your mind. Even the same question multiple times if the answer is still not clear. There are no bad questions, just bad decisions (i.e., not asking questions you have). You will learn a great deal more by clarifying your understanding of the material.

3. Do not fall behind. All the Python programming material builds on itself over the course of the semester. This means that keeping up with all material is crucial to succeeding in this class. If you fall behind on the programming aspect, you may find it hard to catch up. If you are feeling lost or having trouble maintaining pace with the coding or statistical concepts, please let Hansol or me know early on so we can work to get you up to speed. All the homework assignments, labs, and projects will contain at least a little bit of coding in them. This is not something you can avoid.

4. Use ELMS (Canvas) notification settings. Pro tip! ELMS can ensure you receive timely notifications of course updates via email or text. I recommend you enable announcements be sent instantly (or at least daily). I will send out weekly announcements detailing what is to come, as well as intermittent announcements throughout the week.

5. Email the team. The best way to reach me for university-approved absences and other questions, is via email (jj[email protected]). I typically respond to emails during the week within 24 hours. If you email outside of business hours (9:00 AM-5:30 PM), I may not see the email until the next day. Please put “BSOS233” in the subject of all course-related emails to ensure I see them. Check the syllabus and Canvas for due dates and assignment information before emailing. (The above also applies to communication with your GTA.)

6. Be respectful. To me and especially to your peers. I promise to do the same.

Course Breakdown

DataCamp (5% of grade)

• I have created aDataCamp Classroomthat provides you access to the entire DataCamp suite for free(!). You have two assignments assigned. You must complete one of these in full to receive credit in this assignment category. The other one can be completed for extra credit. These are due by midnight on the last day of class, no exceptions. These are somewhat long (~4 hours on average), so do not save them for the last minute.

Weekly Quizzes (10% of grade)

• There will be a short quiz on ELMS every week that must be completed by the start of class on

Monday. The quizzes will generally cover reading material for that week, but they are cumulative. You will be allowed to take the quiz two times before the deadline. I will drop your two lowest quiz scores at the end of the semester.

Lab Assignments (15% of grade)

• Approximately every Friday there will be a lab session focused on teaching you how to code in Python. Part of this will include working on in-class lab assignments that will be due at the end of the day in the lab sessions. These lab assignments will be graded mostly on completion. I will drop your lowest lab grade at the end of the semester.

Homework Assignments (20% of grade)

• There will be a total of five assignments designed to assess your mastery of the topics and techniques in the lectures. The assignments will generally be assigned around two weeks before the due date. I will drop your lowest assignment grade at the end of the semester.

Midterm Exam (25% of grade)

• There will be a cumulative exam about halfway through the semester. More information about this exam will be provided later in the semester.

Final Project (25% of grade)

• This will be due during Finals week, by the end of our assigned final time. More information will be provided about the final project later in the semester.

Grading

Grades will be assigned based on the total percent earned using the following rubric. Grades will be rounded to the nearest 10th of a percent; however, they will not be rounded any further …regardless of how close to the boundary you are. I do not offer extra credit assignments. If you think there is an error with a particular grade, please come talk to one of us during office hours, after class, or schedule an appointment and we can discuss the grade and correct it if need be.