# Syllabus#

CSC 31167: Foundations of Data Science

Instructor: Filipa Calado

Email: fcalado@gradcenter.cuny.edu

Zoom address: https://nyu.zoom.us/my/filipa.calado

Office hours: https://www.bit.ly/calado_office

This course introduces the fundamental concepts and computational techniques of data science to all students, including those majoring in the Arts, Humanities, and Social Sciences. The course will focus on the critical approach to studying data, which emphasizes the importance of understanding and addressing the ways in which power and privilege in social systems shape the collection, analysis, and interpretation of data. This approach centers marginalized identities and experiences and the intersectionality of gender, sexuality, race, and class as factors that shape data creation, our methods for analyzing data, and the conclusions we can draw from it. Students will explore the ways in which a critical approach data science can be used to reinforce or challenge existing power structures and promote social justice.

This course begins by contextualizing race, gender, and sexuality as identity formations that are constituted by power structures. Students will then move to deconstructing the role that power and privilege have in shaping data collection and analytical methods, and the need to actively work to counteract these biases in the way we handle and interpret data. This course grounds discussion of intersectionality, power, and privilege with practical experimentation, introducing students to programmatic methods of data analysis with Python. Students will learn methods for inferential and compuational thinking by analyzing text-based data in Python. As they learn to code with Python, students will examine how bias infiltrates computational processes, examining firsthand how the necessity for standards and rules that enable computation can stymie the expression of real-world and human complexity and how statistical methods tend to overlook specificities and generalize difference. By the end of the course, students will have a grasp of how these computational methods for working with text contribute to the bias and toxicity of machine learning methods such as those used to create Large Language Models like *ChatGPT*.

The course is designed for students who are new to statistics and programming. Students will make use of the Python programming language, but no computer science pre-requisites are required.

Prerequisites: [NA] Co-Requisites: [NA] Credits/Hours: 3 Credits/3 Hours

This course satisfies Pathways Math and Quantitative Reasoning requirement.

Course Learning Outcomes:

Interpret and draw appropriate inferences from quantitative representations, such as formulas, graphs, or tables.

Use algebraic, numerical, graphical, or statistical methods to draw accurate conclusions and solve mathematical problems.

Represent quantitative problems expressed in natural language in a suitable mathematical format.

Effectively communicate quantitative analysis or solutions to mathematical problems in written or oral form.

Evaluate solutions to problems for reasonableness using a variety of means, including informed estimation.

Apply mathematical methods to problems in other fields of study.

Required Texts/Readings:

There is only one book for the course, which is free online: Lauren Klein and Catherine D’Ignazio, Data Feminism. https://data-feminism.mitpress.mit.edu/

Technology:

In-class lessons and homeworks are done in Jupyter notebooks. The notebooks assume a Python 3 installation with the standard modules from an Anaconda installation such as NLTK, Pandas, Numpy and Matplotlib. If you have trouble installing python, there are backup solutions, and please reach out to me.

Assignments overview:

Homework assignments (20%) - Short coding assignments meant to get you to demonstrate your comprehension of in-class lessons. Includes a short written component. Will be graded on effort rather than accuracy. Prompts are posted on the class website.

Participation (30%) - Students are expected to be actively engaged in class activities. This means paying attention to lessons and participating in class discussions. Students are expected to come to class having done the reading and being prepared to give their opinions. For a student who comes to every class (not counting excused absences), is actively listening and engaged, and speaks up at least once during class, they will get 100% on participation.

Final project (30%) - Group projects centered on posing a research question and doing exploratory analysis of a dataset. Includes a coding and written component, and groups will present their process and preliminary findings in the last week of class. Instructions will be distributed during the final unit.

Exams (20%) - Midterm and final exam which will assess students’ understanding of data analysis procedures as applied to their own research interests. Format will be jupyter notebooks.

Grade distributions:

Homework assignments (20%)

Exams (20%)

Participation (30%)

Final projects (30%)

Course overview:

Unit 1: Introduction to Python programming

Unit 2: Introduction to text analysis with NLTK

Unit 3: Introduction to machine learning with Transformers