MoDAT: Designing the Market of DATa – Workshop Report
An overview of MoDAT workshop on "Designing the Market of DATa" - key research ideas such as recommending expertise, chance discovery, "data jackets", privacy risks, and more.
on May 28, 2014 in Dallas-TX, ICDM, IEEE, Marketplace, Workshop, Yukio Ohsawa
Exclusive Interview: Richard Socher, founder of etcML, Easy Text Classification
An exclusive interview with Richard Socher, co-founder of etcML, a new and free tool for helping users with creating classifiers for text using machine learning.
on Mar 31, 2014 in etcML, Machine Learning, Richard Socher, Startups, Text Classification
Nate Silver FiveThirtyEight tackles Climate Change, fails on Data Science
Nate Silver FiveThirtyEight forays into climate science and drowns in criticism for bad data analysis. We examine a basic data science question: Can we tell if natural disasters are related to climate?
on Mar 28, 2014 in Climate Change, FiveThirtyEight, Munich Re, Nate Silver, Natural Disasters
Is Data Scientist the right career path for you? Candid advice
Candid advice from an industry veteran reveals the true picture behind the much-talked-about Data Scientist "glamour" and helps people have the right expectations for a Data Science career.
on Mar 28, 2014 in Advice, Career, Data Science, Data Scientist, Hadoop, Paco Nathan, Recommendation, Visualization
Fractal Analytics Interview Highlights
Fractal Analytics CEO on starting the company, competing with the best, managing attrition, attributes he looks for when hiring, 4 different analytics career tracks, strategic bets, and advice for starting data scientists.
on Mar 27, 2014 in Advice, Career, Fractal Analytics, Hiring, Interview
White House-MIT Big Data Privacy Workshop – Top Researcher Reports
Leading database researcher Michael Brodie gives a summary of an important White House-MIT Big Data Privacy workshop and discusses privacy, government, technical solutions, Edward Snowden, SXSW, and technical challenges associated with big data and privacy.
on Mar 27, 2014 in Big Data Privacy, CSAIL, Michael Brodie, Michael Stonebraker, MIT, Snowden, SXSW, White House
Identity Fraud and Analytics – An Overview
With the consumers being increasingly concerned about identity theft, leading financial institutions are leveraging analytics to detect Identity Fraud as it happens.
on Mar 26, 2014 in Boosting, Decision Trees, FCRA, Identity Fraud, Identity Theft, Logistic Regression, Machine Learning
KDnuggets Exclusive: Interview with Anjul Bhambhri, VP of Big Data Products at IBM
KDnuggets talks with Anjul Bhambhri, IBM’s Vice President of Big Data Products about Big Data Trends, developing the Big Data capabilities in-house vs. outsourcing, five crucial steps to adopting a success big data strategy and advice for beginners.
on Mar 25, 2014 in Advice, Big Data Strategy, Challenges, Data Science, IBM, In-house, Interview, Outsourcing, Watson
Exclusive: Interview with Daniel Tunkelang, Head of Query Understanding at LinkedIn
Daniel Tunkelang, Head of Query Understanding at LinkedIn talks about search quality, IR, query understanding, and advice for data science enthusiasts. Don't miss: 4 steps to get your LinkedIn profile show up on top of search results.
on Mar 19, 2014 in Daniel Tunkelang, Information Retrieval, LinkedIn, Ranking, Search Quality
How Deep Learning Analytics Mimic the Mind
There has been a lot of buzz surrounding the potential impact deep learning will have in the field of analytics. This post looks at the origins of deep learning.
on Mar 19, 2014 in Deep Learning, DeepMind, FICO, Fraud, Neural Networks, Shallow Learning
KDnuggets Exclusive: Interview with Geoffrey Moore: Crossing the Chasm and Big Data
KDnuggets talks with a noted author Geoffrey Moore about his "Crossing the Chasm" book, his vision for Big Data analytics, when Big Data will cross the chasm, and advice for entrepreneurs.
on Mar 14, 2014 in Adoption, Business Strategy, Crossing the Chasm, Geoffrey Moore, Interview, Life Cycle, Strata
Evolution of Fraud Analytics – An Inside Story
The amazing analytic innovations in payment fraud prevention can be grouped into three major categories: large data-set modeling, sparse data-set modeling, and false-positive reductions - a view from the inside.
on Mar 14, 2014 in False positive, FICO, Fraud analytics, Fraud Prevention, Neural Networks, Sparse data
KDnuggets Exclusive: Part 2 of the interview with Paco Nathan
We discuss about Paco's upcoming book "Just Enough Math", problems with current university curriculum around Math for Data Science and Big Data trends.
on Mar 10, 2014 in Apache, Big Data Player, BioCoder, Hadoop, Interview, Mesos, Mesosphere, Paco Nathan, Trends
KDnuggets Exclusive: Interview with Paco Nathan, Chief Scientist at Mesosphere
KDnuggets talks with Paco Nathan, computer scientist, OSS developer, author, and advisor about Apache Mesos, Cascading, his books and Big Data trends.
on Mar 10, 2014 in Apache Mesos, Big Data Player, Cascading, Hadoop, Interview, Mesosphere, Monoids, Paco Nathan
KDnuggets Exclusive: Part 2 of the interview with Quentin Clark, CVP, Microsoft Data Platform Group
We discuss Microsoft decision to embrace Hadoop as the standard, collaboration with Hortonworks, and advice to newbies in Data Science.
on Mar 6, 2014 in Advice, Azure HDInsight, Data Platform, Hadoop, Hortonworks, Microsoft, Strata 2014
KDnuggets Exclusive: Interview with Quentin Clark, CVP, Microsoft Data Platform Group
KDnuggets talks with Quentin Clark, Corporate Vice President, Microsoft Data Platform Group. In the interview, we discuss Power BI for Office 365, Big Data trends and Microsoft’s strategic decisions.
on Mar 6, 2014 in Accessibility, Data Platform, Interview, Microsoft, Office 365, Power BI, Quentin Clark, Strata 2014
Why Predictive Analytics Marketplaces are not taking off, and how to fix it
Three main hurdles holding back Predictive Analytics Marketplaces are a highly fragmented data mining tools market, limited support for customization, and lack of commitment. We examine how to overcome them.
on Mar 1, 2014 in How to fix, Hurdles, Marketplace, Predictive Analytics, Snap Analytx
The Do’s and Don’ts of Data Mining
Leading data mining and analytics experts give their favorite do's and don'ts, from "Do plan for data to be messy" to "Do not underestimate the power of a simpler-to-understand solution".
on Mar 1, 2014 in
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