Essential information

Syllabus with course description and policies etc.

All readings are available online, either on the open web or through the NYU Library EZproxy.

Please turn in homework using this Google form.

Unit A: Computational building blocks

Session 01 (2024-09-09): Introduction

• Syllabus and schedule
• Lecture: What is data and what is it good for?
• Exploratory data analysis: an example

Some interesting data-related projects in the worlds of art and design:

• Stephanie Posavec and Georgia Lupi, Dear Data
• Jia Zhang, censusAmericans
• Mimi Ọnụọha, Library of Missing Datasets
• Tempestry Project
• Sam Lavigne and Tega Brain, New York Apartment
• Lam Thuy Vo, Quantified Breakup
• The Markup, e.g. See the neighborhoods internet providers excluded from fast internet

Places to look for interesting datasets:

• Data Is Plural newsletter
• Allison’s ‘dataset’ bookmarks
• NYC Open Data
• Google Dataset Search

Reading assigned: What is data?

• Garvey, Ellen Gruber. “‘Facts and FACTS’: Abolitionists’ Database Innovations.” “Raw Data” Is an Oxymoron, edited by Lisa Gitelman, The MIT Press, 2013, pp. 89-102.
• Keyes, Os. “Counting the Countless.” Real Life, 8 Apr. 2019.

Optional but recommended:

• O’Connor, Maureen. “Heartbreak and the Quantified Selfie.” The Cut, 2 Dec. 2013.
• Spector, Alfred Z., et al. “Foundations of Data Science.” Data Science in Context: Foundations, Challenges, Opportunities, Cambridge University Press, 2022, pp. 7–28.

Before the next session:

Please install Python on your computer. Once Python is installed, use the pip command to install Jupyter Notebook. Note that this will probably require doing a little bit of work at the command line (Terminal on macOS, PowerShell on Windows). Make sure that you can launch Jupyter Notebook on your machine before we begin next session.

NOTE: If you already have a working installation of Python 3 on your computer, you don’t need to install it again! Just use the version that you already have installed.

Here are two good tutorials on YouTube:

• Install Python and Jupyter Notebook on Windows 11
• Install Python and Jupyter Notebook on macOS (more technical users may want to install with Homebrew, though note that Homebrew only supports the most recent three major versions of macOS)

And here’s a more general tutorial for all platforms.

Linux users and users of other UNIX-alikes: In this class, you can probably get away with using your distribution’s default Python 3. However, you may want to research a tool like pyenv or asdf to make it easier to have multiple versions of Python available on your machine at once (e.g., your distribution’s Python alongside the latest version of Python).

Another option for many platforms is Anaconda (though please read the licensing terms).

Note that (as far as I know) there is no satisfactory option for installing Python on iOS or Android. If you only have access to iOS and/or Android, you may be better off using a web-hosted service like Python Anywhere (you will need their $5/mo service, which includes access to Jupyter Notebooks). You can also use Google Colab in a pinch.

Session 02 (2024-09-16): Python basics

• Reading discussion
• Jupyter Notebook introduction
• Python basics: Expressions, types and strings. I’ll be using these notes.

Session 03 (2024-09-23): Strings, lists and loops

• Python: Lists and loops
• How to download notebooks

Exercise #1 assigned: Python basics. Download the notebook to your own computer, open it in Jupyter Notebook, and follow the instructions. We’ll review the exercise and discuss how to upload your completed notebook next week.

Unit B: Tools for data

Session 04 (2024-09-30): Dataframes with Pandas

• How to share your notebooks
• Review exercise #1
• Lecture: “Tidy data”
• Python: Pandas, part 1

Reading assigned: Forms of data.

• Broman, Karl W., and Kara H. Woo. “Data Organization in Spreadsheets.” The American Statistician, vol. 72, no. 1, Jan. 2018, pp. 2–10.
• Onuoha, Mimi. “The Point of Collection.” Data & Society: Points, 31 Oct. 2016.
• Rawson, Katie, and Trevor Muñoz. “Against Cleaning.” Curating Menus, 6 July 2016.

Session 05 (2024-10-07): Pandas, continued

• Reading discussion
• Lecture: Basics of descriptive statistics
• Python: Pandas (part 2)

No homework this week, but if you’re looking for more practice with Python and Pandas, try Julia Evans’ Pandas Cookbook.

Session 06 (2024-10-15): Data structures

NOTE: This is a Tuesday! (Monday 2024-10-14 is “Fall Break”)

• Python: Still more pandas
• Python: Dictionaries, sets, tuples
• Python: JSON files

Exercise #2 assigned: Fun with Pandas.

Midterm project assigned (due Session 08).

Session 07 (2024-10-21): Regular expressions

• Review exercise #2
• Python: Regular expressions

Session 08 (2024-10-28): Midterm presentations

• Midterm presentations

Session 09 (2024-11-04): Clustering and correlation

• Python: Clustering and correlation.

Reading assigned: Corpora and databases.

• Heller, Nathan. “What the Enron Emails Say About Us.” The New Yorker, 17 July 2017.
• Ramsay, Stephen. “Databases.” A Companion to the Digital Humanities, 2004.,
• Voida, Amy, et al. “Homebrew Databases: Complexities of Everyday Information Management in Nonprofit Organizations.” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2011, pp. 915–24.

Unit C: Databases

Session 10 (2024-11-11): Structured Query Language (SQL)

• Reading discussion
• Lecture: Relational databases
• Python: Introduction to SQL

Session 11 (2024-11-18): SQL, part 2

• Python: SQL (part 2)

Exercise #3 assigned: SQL practice.

Session 12 (2024-11-25): Sharing data projects

• Review exercise 3
• Final project proposal
• Python: Interactive stuff in Jupyter Notebooks
• Python: Going live on Binder! My prepped gist, more info
• NYC Dog Licensing Dataset

Session 13 (2024-12-02): Final project presentations 1

• Final project presentations

Session 14 (2024-12-09): Final project presentations 2

• Final project presentations