Research Guides: Inclusive and Responsible Dataset Usage: Training

USC Libraries Research Guides

Content Mining
This guide provides information about available text mining resources and tools and whether or not the Libraries subscription databases support content mining.
Statistics & Data: Datasets
Social science numeric sources available through the USC Libraries and on the Internet.
Data Science in Computer Science Research Guide
Data Reference Toolkit
This research guide will walk you through the steps of finding, using, and managing data in a research project.
Teaching with Data: Teaching Resources
Resources and strategies for faculty teaching undergraduates

Courses & Tutorials

Coding Train: Working with Data & APIs in Javascript
This video series is a friendly introduction to coding with data from Daniel Shiffman, using Javascript and weather APIs.
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This course is for aspiring developers who want to learn how to work with data in web applications. How do you retrieve, collect, and store data? The course will be taught through a series of creating three data projects. The first will be client-side only and examine how to load data with fetch() and present on a web page. Viewers will learn about handling asynchronous events with Promises and how to render data to the DOM as well as draw to HTML5 canvas with p5.js. The second and third project will introduce "full stack" development adding server-side programming with node.js for data persistence and API authentication.
Data Analysis and Visualization with Python for Social Scientists
This is an introduction to Python designed for participants with no programming experience.
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These lessons can be taught in a day (~ 6 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.
Elements of AI
Two courses, Introduction to AI and Building AI, that aim to demystify AI, what it is, what can and cannot be done, and how to use it. The courses include practical exercises and are created by the University of Helsinki and MinnaLearn.
Ethics of AI
The Ethics of AI is a free online course created by the University of Helsinki. The course is for anyone who is interested in the ethical aspects of AI – we want to encourage people to learn what AI ethics means, what can and can’t be done to develop AI in an ethically sustainable way, and how to start thinking about AI from an ethical point of view.
Fast.AI Practical Data Ethics
This course from Fast.AI covers disinformation, bias and fairness, foundations of data ethics and practical tools, the field including venture capital and metrics, privacy and surveillance, and algorithmic colonialism. It is adapted from a course originally taught at the University of San Francisco Data Institute.
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Desired Learning Outcomes
Understand the impacts of data misuse, including unjust bias, surveillance, disinformation, and feedback loops. Understand the contributing factors to these impacts. Identify different types of bias.
Develop literacy in investigating how data and data-powered algorithms shape, constrain, and manipulate our commercial, civic, and personal experiences.
Analyze new scenarios and potential products to try to identify and mitigate potential risks.
Have a toolkit of ethical techniques and practices to implement in their workplaces
The Illustrated VQGAN
Background and technical details on how text-to-image generating AI are made
Neural Networks
3Blue1Brown's Grant Sanderson offers an approachable technical overview of neural networks with lots of examples.