Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Survey: Machine Learning Projects Still Routinely Fail to Deploy
 Originally published in KDnuggets. Eric Siegel highlights the chronic...
Three Best Practices for Unilever’s Global Analytics Initiatives
    This article from Morgan Vawter, Global Vice...
Getting Machine Learning Projects from Idea to Execution
 Originally published in Harvard Business Review Machine learning might...
Eric Siegel on Bloomberg Businessweek
  Listen to Eric Siegel, former Columbia University Professor,...

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“Data Scientist” catches “Statistician”, surpasses “Data Miner”

 The rapidly rising term “Data Scientist” caught up with “Statistician” and surpassed “Data Miner” on Google Trends. However, Statistics remains a lot more popular than “Data Science”, which begs the question: What do Data Scientists do? Clearly, it is not Data Science. By Gregory Piatetsky, Dec 22, 2013. comments A recent blog on Flowing Data:

Searching for data scientists? They come in sets of 3!

 Data scientists have been called the most sought-after IT professionals of all for 2014. But that mission is distorted on two fronts, according to one current data scientist. Dr. Michael Wu, chief scientist at Lithium Technologies (aka,...

Perfect Information Doesn’t Equal Perfect Predictions

 Many organizations attempt to achieve “data nirvana” by having 100% complete information for any given business decision. In the customer analytics space, this is sometimes referred to as a “360 degree view of the customer.” However, we...

Why not now? The barriers to adopting true predictive analytics.

 While reading Jared Diamond’s excellent book on the rise and subsequent global dominance of Eurasian societies Guns, Germs and Steel, I was stopped in my tracks by his chapter on the evolution of technology entitled Necessity’s Mother. Diamond...

2014 Big Data Predictions from IDC and IIA

 Both IDC and The International Institute of Analytics (IIA) discussed their big data and analytics predictions for 2014 in separate webcasts last week. Here is my summary of their predictions plus a few nuggets from other sources. IDC predicts...

Hiring 1 Data Science unicorn is hard enough, a team is impossible. To scale means to specialise.

 The Data Scientists need a large set of skills, including business know-how, modelling and mathematics, plus programming. They are as hard to find as unicorns, or superheroes. I know this talent shortage first hand. Is the solution...

Python Displacing R As The Programming Language For Data Science

  R remains popular with the PhDs of data science, but as data moves mainstream, Python is taking over. While R has traditionally been the programming language of choice for data scientists, it is quickly ceding ground...

Understanding Your Business With Descriptive, Predictive And Prescriptive Analytics

 Companies have long been involved in the analysis of how a company performed over time. As the history of big data shows, already for many years we try to understand how the organisations or the world around...

“The hungry statistician” – or why we never can get enough data

 As the “Year of Statistics” comes to a close, I write this blog in support of the many statisticians who carefully fulfil their analysis tasks day by day, and to defend what may appear to be demanding behavior when...

Analytics 3.0 — the old guard masters how to build data products

 Asked to name a big data company, many of us would say Google or Facebook or eBay. But for old-school giants such as General Electric Co. and Macy’s Inc., big data is fast becoming as central to...

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