Sessions are from 10am-12pm and 1pm-3pm in Bobst Lower Level 1, Rm. 39 at the Elmer Holmes Bobst Library.
Finding and organizing project files, with emphasis on specifics of Mac and Windows systems
Key shortcuts and skills around command-line tools on Mac and Windows systems–automate the boring stuff
File naming, bulk renaming, and description
Setting up personal storage environment and cloud-storage clients, establishing backup systems
Installation of open-source software and understanding software dependencies
Understanding of how data files (text, tabular) store (encode) characters and sustainable ways to keep files usable over many years
Introduction to common formats for storing and collecting data (tabular, JSON, SQL)
Cleaning and transforming untidy data
Special case studies of spatial data files, image files, time-based media files–how they work and why they are more complex
Identify the ethical issues and data privacy risks associated with generative AI use
Think critically about generative AI use to support research and analyze the quality and accuracy of AI outputs
Explore NYU supported generative AI offerings
Evaluate Generative AI Tools for Academic Research
Sessions are from 10am-12pm and 1pm-3pm in Bobst Lower Level 1, Rm. 39 at the Elmer Holmes Bobst Library.
What is Python, how does it work, where to compose Python scripts
Using the course’s cloud-based Python environment; how to set up Python on your computer
Fundamentals data types (integers, floating point numbers, strings, booleans, dictionaries, lists)
Indexing data structures
Conditional statements and logical operations
Loops
Functions
Practical applications of Python example
Hands-on activity with instructor support on launching a personal Python project
Sessions are from 10am-12pm and 1pm-3pm in Bobst Lower Level 1, Rm. 39 at the Elmer Holmes Bobst Library.
Introducing Pandas library for tabular/dataframe work.
Creating, filtering, sorting, and modifying Pandas dataframes
Understanding of basic principles of data visualization
Python visualizations using matplotlib and the capabilities of Jupyter Notebooks
Here is a sampling of projects NYU graduate students have worked on using the types of skills covered in this track:
Extraction of Word Embeddings from a Corpus: Framing Democracy: Characterizing China's Negative Legitimation Propaganda using Word Embeddings
Automated extraction of semantic motifs from a large text corpus: Who Kisses Whom: Gendered Interaction in American Novels 1880-2000
DH Project using Python/Flask: Demystifying the Digitization of Texts: New Textual Analysis for the Medieval History of Islamic Mysticism, A Corpus of Digitally Neglected Texts