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James Kobielus

James Kobielus is IBM Senior Program Director, Product Marketing, Big Data Analytics solutions. He is an industry veteran, a popular speaker and social media participant, and a thought leader in big data, Hadoop, enterprise data warehousing, advanced analytics, business intelligence, data management, and next best action technologies.

ensembles-to-boost-machine-learning-effectiveness

Ensembles to Boost Machine Learning Effectiveness

Employing ensemble-based crowdsourcing can confidently identify best-fit models

monetizing-a-crowdsourced-data-scientist-existence

Monetizing a Crowdsourced Data Scientist Existence

Can project-oriented crowdsourcing initiatives put food on data scientists’ tables?

explore-new-frontiers-in-business-analytics

Explore New Frontiers in Business Analytics

Converging basic and advanced analytics paves the way for keen insight and business innovation

the-power-of-behavioral-fingerprinting

The Power of Behavioral Fingerprinting

A big data application of online behavior detection is well suited for antifraud protection

the-ground-truth-in-agile-machine-learning

The Ground Truth in Agile Machine Learning

Distilling knowledge effortlessly from big data calls for collaborative human and algorithm engagement

in-data-science-take-nothing-on-faith

In Data Science, Take Nothing on Faith

Adopting a rigid regimen is required for vetting reproducible computational findings

ibm-data-magazine-its-value-and-its-vision

IBM Data Magazine: Its Value and Its Vision

A new course is set for an online publication with a rich tradition of serving the data management community

hidden-biases-that-may-cloud-cognitive-computing

Hidden Biases That May Cloud Cognitive Computing

Consign scrutiny of unconscious biases that data scientists bring to their analytics and algorithms

saving-the-planet

Saving the Planet

Planetary resource surveillance using geospatial analytics offers an innovative method for busting the bad guys

cloud-has-a-blu-lining-and-blu-has-a-cool-hub

Cloud Has a BLU Lining, and BLU Has a Cool Hub

IBM extends the reach of next-generation BLU Acceleration to the cloud and beyond

when-and-when-not-to-have-faith-in-statistical-models-part2

When and When Not to Have Faith in Statistical Models: Part 2

In addition to empirical validity and beauty, authority and efficiency contribute to accurate statistical models

when-and-when-not-to-have-faith-in-statistical-models

When and When Not to Have Faith in Statistical Models: Part 1

Criteria for high-quality, efficient statistical models begin with truth and beauty