LibGuides: Data Science: Home

About This Guide

This guide provides resources related to data science.

The guide is designed primarily for undergraduate students majoring or minoring in Data Science. However, anyone interested in aspects of data science - from working with Python and R, to finding and cleaning data, to visualizing results - may find it useful.

Just as data science is ever changing, this guide will be updated regularly to reflect new tools, packages, tutorials, and data science examples. If you think anything important is missing from this guide, please email the Data Science Librarian.

Accessing Electronic Resources

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Featured Resources

O'Reilly for Higher Education
O’Reilly for Higher Education includes more than 35K computer science, engineering, business book titles from 200+ publishers. Plus 30K+ hours of video, proven learning paths, case studies, expert playlists, audio books, and more. This is a great resource for books and tutorials related to data science, including programming with Python and R. Access with Single Sign On after choosing University of California San Diego from the list of institutions.
Titles are html only.
Replaces the Safari Tech Books Online collection.

Learning Data Science by Sam Lau; Joseph Gonzalez; Deborah Nolan
ISBN: 9781098113001
Publication Date: 2023-10-24
As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide.

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