Incoming Resources
- XML data mining, models, methods, and applications, Andrea Tagarelli [editor]
- Developing analytic talent, becoming a data scientist, Vincent Granville, Ph.D
- Introduction to machine learning with Python, a guide for data scientists, Andreas C. Müller and Sarah Guido
- Data mining for dummies, Meta S. Brown
- Outside insight, navigating a world drowning in data, Jorn Lyseggen
- Predictive analytics for dummies, Anasse Bari, Mohamed Chaouchi, and Tommy Jung
- Predictive analytics for marketers, using data mining for business advantage, Barry Leventhal
- Data science for dummies, by Lillian Pierson ; foreword by Jake Porway, Founder and Executive Director of DataKind
- Python for data analysis, data wrangling with Pandas, NumPy, and IPython, Wes McKinney
- Data science from scratch, first principles with Python, Joel Grus
- Applied Predictive Analytics, Principles and Techniques for the Professional Data Analyst
- Text mining with R, a tidy approach, Julia Silge and David Robinson
- Data literacy, achieving higher productivity for citizens, knowledge workers, and organizations, Peter Aiken, Todd Harbour
- Agile data science, Russell Jurney
- Data science for business, [what you need to know about data mining and data-analytic thinking], Foster Provost & Tom Fawcett
- Social media data mining and analytics, Gabor Szabo, Gungor Polatkan, Oscar Boykin, Antonios Chalkiopoulos
- Web metrics for library and information professionals, David Stuart
- The eye test, a case for human creativity in the age of analytics, Chris Jones
- Click, what millions of people are doing online and why it matters, Bill Tancer
- The AI delusion, Gary Smith
- Data analysis, Michael Milton
- Our bodies, our data, how companies make billions selling our medical records, Adam Tanner
- Big data, understanding how data powers big business, Bill Schmarzo
- Python data science handbook, essential tools for working with data, Jake VanderPlas
- Data analysis using SQL and Excel, Gordon S. Linoff
- If then, how the Simulmatics Corporation invented the future, Jill Lepore
- Predictive analytics using Oracle data miner, develop & use data mining models in Oracle Data Miner, SQL & PL/SQL, Brendan Tierney
- Python for data analysis, data wrangling with Pandas, NumPy, and IPython, Wes McKinney
- The metric society, on the quantification of the social, Steffen Mau ; translated by Sharon Howe
- Mining the social web, Matthew A. Russell
- Confident data skills, master the fundamentals of working with data and supercharge your career, Kirill Eremenko
- Big data governance, an emerging imperative, Sunil Soares, [editor]
- Introduction to machine learning with Python, a guide for data scientists, Andreas C. Müller and Sarah Guido
- Pandas for everyone, Python data analysis, Daniel Y. Chen
- All you can pay, how companies use our data to empty our wallets, Anna Bernasek and D.T. Mongan
- Data mining for dummies, by Meta S. Brown
- Introducing data science, big data, machine learning, and more, using Python tools, Davy Cielen, Arno D. B. Meysman, Mohamed Ali
- Master competitive analytics with Oracle Endeca information discovery, Helen Sun and William Smith
- Python for data science, by John Paul Mueller and Luca Massaron
- Predictive analytics for dummies, by Anasse Bari, Ph.D., Mohamed Chaouchi, and Tommy Jung
- Ask, measure, learn, using social media analytics to understand and influence customer behavior, Lutz Finger, Soumitra Dutta
- Data science using Python and R, Chantal D. Larose, Daniel T. Larose
- Unstructured data analytics, how to improve customer acquisition, customer retention, and fraud detection and prevention, Jean Paul Isson ; foreword by Paul Zikopoulos
- Mining the social web, data mining, Facebook, Twitter, LindedIn, Google+, GitHub, and more, Matthew A. Russell
- Data smart, using data science to transform information into insight, John W. Foreman