The School of Information is the interdisciplinary place to gain data science training, regardless of your major or field of study. Faculty across many U.A. departments have data science expertise (e.g., Biosystems Engineering, Computer Science, Ecology and Evolutionary Biology, Electrical and Computer Engineering, Management Information Systems, Mathematics). But our faculty in the School of Information are prepared to talk across disciplines to provide the basic data science tools every student needs today. That is, the School of Information provides a methodological hub for interdisciplinary data science at U.A., training students and doing research on such activities as data curation, database management, information ethics and policy, along with statistical and computational data analysis. Many of our students continue their data science training in their primary domain or field of study, if not majoring in Information Science. Our curriculum is embedded into or available as electives in other departments, such as Neuroscience & Cognitive Science, Applied Physics, and Bioinformatics. These domain spokes all across the campus can be considered your primary research focus, though data scientists across domains have a lot in common.
Courses like those listed below provide data science training for students from all across the campus, and soon the School of Information will provide students with short one-unit courses for those who just want a small introduction or 'refresher' kind of experience. Join us!
Data Science courses with no or very few prerequisites:
eSoc 214: Introduction to Data Science
ISTA 116: Statistical Foundations of the Information Age (a basic course in R)
ISTA 130: Computational Thinking and Doing (a basic course in Python)
ISTA 131: Dealing with Data
ISTA 321: Data Mining and Discovery
Intermediate and advanced courses on Data Science, Data Engineering, and Machine Learning:
ISTA 311: Foundations of Information and Inference
ISTA 322: Data Engineering (Available Fall 2020)*
ISTA 331: Principles and Practice of Data Science
ISTA 350: Programming for Informatics Applications
ISTA 355: Introduction to Natural Language Processing
ISTA 410: Bayesian Modeling and Inference
ISTA 421: Introduction to Machine Learning
ISTA 450: Artificial Intelligence
ISTA 455: Applied Natural Language Processing
ISTA 457: Neural Networks
Please view our course descriptions for more information, and consider our newest course: Data Engineering 322:
This course will be inviting for a wide variety of students from across disciplines, and they will learn how to use industry standard tools and practices to make large data sets usable for scientists and other decision makers. From data collection and preparation, to the creation of big data stores, databases, or systems to make data flow, this course will focus on the practical work needed to prepare big data for analyses across contexts. Students will be introduced to a variety of technical tools for data management, storage, use, and manipulation.