LibGuides: Computer Science Research Guide: Data Science & Big Data

Data Science & Big Data

The continuing explosion in “Big Data” is revolutionizing industry and research. Companies are responding by looking for data scientists and information technology experts capable of analyzing and extracting useful patterns from lots of data. Data science involves mathematics, statistics, data mining, machine learning, databases, and more.

We have gathered some resources below to assist your data science coursework and research.

Library Resources

ACM Digital Library
A key collection of citations and journal articles in computer science from the Association for Computing Machinery covering topics specific to data science such as data management, data structures, data visualization, computer graphics, machine learning, artificial intelligence, probability and statistics, and more. Publication dates: 1908 - current
IEEE Xplore
Technical literature in computer science, electrical engineering, and electronics. A particularly useful resource when creating or researching computer programming to control hardware devices, such as robots and other forms of electronics. Coverage: 1864 - current
MathSciNet (American Mathematical Society)
Core database for mathematical and statistical research, with citations to academic articles covering most leading journals in the field. Developed by a key association in the field, the American Mathematical Society, and includes tools to find most cited articles, authors, or books in a search, as well as find where individual articles have been cited by other articles. Publication dates: 1864 - current

Handbook of Data Structures and Applications
This book provides a comprehensive survey of data structures of various types and offers background information for readers with no formal training in computer science. In this latest edition, chapters have been added on Bloom Filters, Binary Decision Diagrams, Data Structures for Cheminformatics, and Data Structures for Big Data Stores (2018). Type in your UML email to get access.
Python Data Science Handbook : essential tools for working with data
A must-have reference for scientific computing in Python. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Helps you build efficient and clean Python implementations of the most important and established machine learning algorithms (2022). Type in your UML email to get access.
Practical Statistics for Data Scientists
Statistical methods are a key part of data science. The second edition of this practical guide - now including examples in Python as well as R - explains how to apply various statistical methods to data science.

Web Resources

Kaggle
Kaggle is an online community platform for data scientists and machine learning enthusiasts. Kaggle allows users to collaborate with other users, find and publish datasets, use GPU integrated notebooks, and compete with other data scientists to solve data science challenges. It’s a crowd-sourced platform to attract, nurture, train and challenge data scientists from all around the world to solve data science, machine learning and predictive analytics problems. All of the resources on Kaggle are free.
GeeksforGeeks
A Computer Science portal for geeks. Their mission is to empower data scientists and technology enthusiasts worldwide to excel. Contains well written and well explained computer science and data science articles, tutorials, quizzes and practice interview questions. GeeksforGeeks offers courses on topics like data science, data visualization, data analysis, deep learning, machine learning and more.