Incoming Resources
- Mining the social web, data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more, Matthew A. Russell, Mikhail Klassen
- Collective intelligence in action, Satnam Alag
- Data mining, practical machine learning tools and techniques /Ian H. Witten ; Eibe Frank ; Mark A. Hall ; Christopher J. Pal
- The data journalism handbook, edited by Jonathan Gray, Liliana Bounegru, and Lucy Chambers
- Mining the Web, discovering knowledge from hypertext data, Soumen Chakrabarti
- Data analysis with open source tools, Philipp K. Janert
- Data mining, concepts and techniques, Jiawei Han, Micheline Kamber
- Data mining, concepts and techniques, Jiawei Han and Micheline Kamber
- Mining the social web, Matthew A. Russell
- Data mining, the textbook, Charu C. Aggarwal
- Data smart, using data science to transform information into insight, John W. Foreman
- Information quality, the potential of data and analytics to generate knowledge, Ron S. Kenett, Galit Shmueli
- Advanced analytics with Spark, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills
- Data mining techniques, for marketing, sales, and customer relationship management, Gordon S. Linoff and Michael J. A. Berry
- Mining the social web, by Matthew A. Russell
- Making sense of data II, a practical guide to data visualization, advanced data mining methods, and applications, Glenn J. Myatt, Wayne P. Johnson
- Making sense of data I, a practical guide to exploratory data analysis and data mining, Glenn J. Myatt, Wayne P. Johnson
- Big data at work, dispelling the myths, uncovering the opportunities, Thomas H. Davenport
- Handling class imbalance using swarm intelligence techniques, hybrid data and algorithmic level solutions, Haya Alhakbani
- Curation, the power of selection in a world of excess, Michael Bhaskar
- If then, how one data company invented the future, Jill Lepore
- 30-second data science, the 50 key principles and innovations in the field of data-gathering, each explained in half a minute, editor: Liberty Vittert ; contributors: Maryam Ahmed [and thirteen others] ; illustrator: Steve Rawlings
- Our bodies, our data, how companies make billions selling our medical records, Adam Tanner
- Confident data skills, master the fundamentals of working with data and supercharge your career, Kirill Eremenko
- Data mining with R, learning with case studies, Luis Torgo
- Analyzing social media networks with NodeXL, insights from a connected world, Derek L. Hansen, Ben Schneiderman, Marc A. Smith
- Doing data science, Cathy O'Neil and Rachel Schutt
- Machine Learning with Python Cookbook, practical solutions from preprocessing to deep learning, Kyle Gallatin & Chris Albon
- Child data citizen, how tech companies are profiling us from before birth, Veronica Barassi
- Data mining, practical machine learning tools and techniques, Ian Witten [and three others]
- Introduction to data mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar
- Web data mining, exploring hyperlinks, contents, and usage data, Bing Liu
- Mining social networks and security informatics, [edited by] Tansel Ozyer, Zeki Erdem, Jon Rokne and Suheil Khoury
- Graph analysis and visualization, discovering business opportunity in linked data, Richard Brath, David Jonker
- Predictive analytics, data mining and big data, myths, misconceptions and methods, Steven Finlay
- Data mining, a tutorial-based primer, Richard J. Roiger and Michael W. Geatz
- Music emotion recognition, Yi-Hsuan Yang, Homer H. Chen
- Handbook of statistical analysis and data mining applications, Robert Nisbet, John Elder, Gary Miner
- Data mining, introductory and advanced topics, Margaret H. Dunham
- Rapid Miner, data mining use cases and business analytics applications, edited by Markus Hofmann and Ralf Klinkenberg
- Post, mine, repeat, social media data mining becomes ordinary, Helen Kennedy
- Data mining, practical machine learning tools and techniques, Ian H. Witten, Eibe Frank
- Data mining techniques, for marketing, sales, and customer relationship management, Michael J.A. Berry and Gordon S. Linoff
- Doing data science, Rachel Schutt and Cathy O'Neil
- Predictive analytics and data mining, concepts and practice with RapidMiner, Vijay Kotu, Bala Deshpande
- Practicing trustworthy machine learning, consistent, transparent, and fair AI pipelines, Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar
- Data mining, concepts and techniques, Jiawei Han, Micheline Kamber, Jian Pei
- Music data analysis, foundations and applications, edited by Claus Weihs, Dietmar Jannach, Igor Vatolkin, Guenter Rudolph
- Data analysis using SQL and Excel, Gordon S. Linoff
- The elements of statistical learning, data mining, inference, and prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman