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University Library, University of Illinois at Urbana-Champaign

Engineering & Innovation


Applied Predictive Modeling

By Max Kuhn & Kjell Johnson
ISBN: 9781461468493
Publication Date: 2013-05-17

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Techniques are explained intuitively and with an emphasis on problem-solving with real data across a wide variety of applications. Readers should have knowledge of basic statistical ideas, and a mathematical background is needed for understanding advanced topics.

Data Mining

By Charu C. Aggarwal
ISBN: 9783319141428
Publication Date: 2015-04-13

This text explores different aspects of data mining and their applications. It introduces advanced data types such as text, time series, discrete sequences, spatial data, and social networks. The language used is accessible for all, including those with limited mathematical background. Suitable for both introductory and advanced data mining courses.

Data Mining

By Ian H. Witten, Eibe Frank, Mark A. Hall, & Christopher J. Palestro
ISBN: 9780128043578
Publication Date: 2016-10-01

This text offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This new edition includes chapters on probabilistic methods and deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato.

Data Science and Big Data : An Environment of Computational Intelligence

By Witold Pedrycz & Shyi-Ming Chen
ISBN: 9783319534749
Publication Date: 2017

This text presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business. Of interest to researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.

An Introduction to Statistical Learning

By Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani
ISBN: 9781461471387
Publication Date: 2013-06-24

This text provides an accessible overview of the field of statistical learning, including modeling and prediction techniques and their relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Each chapter contains a tutorial on implementing the analyses and methods presented in R.

Strategies in Biomedical Data Science

By Jay Etching
ISBN: 9781119255970
Publication Date: 2016-12-27

This text provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. It explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. Real-world examples are featured alongside coverage of data sources, problems, and potential mitigations.

Data Science Learning Tools

Data Analysis Tools & Tutorials

  • Atlas.ti
    A tutorial to get started with the computer assisted qualitative data analysis software (CAQDAS) program ATLAS.ti.

  • Cytoscape
    Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data. Download it here. Cytoscape has apps available for various kinds of problem domains, including bioinformatics, social network analysis, and semantic web.

  • RStudio
    RStudio develops free and open tools for R and enterprise-ready professional products for teams to scale and share work.

  • SAS
    This guide provides a general overview of the SAS statistical software suite.

  • SPSS Tutorial
    This guide provides a general walkthrough of SPSS's basic features.

Coding & Development Tools

Data Resources from NIH and NLM

Clinical Data Research Networks

General Data Repositories

Data and Statistics Guides