Jim Harris

JANUARY 1, 2016

Once upon a time when asked on Twitter to identify a list of critical topics for data quality practitioners, my pithy (with only 140 characters in a tweet, pithy is as good as it gets) response was, and especially since I prefer emphasizing the need to take action, to propose six critical verbs: Investigate , Communicate , Collaborate , Remediate , Inebriate , and Reiterate.

Data Quality 61

Data Quality Data Governance Cost-Benefit ROI 61

Data Science and Beyond

JANUARY 23, 2016

Many modern data scientists don’t get to experience data collection in the offline world. Recently, I spent a month sailing down the northern Great Barrier Reef, collecting data for the Reef Life Survey project. In addition to being a great diving experience, the trip helped me obtain general insights on data collection and machine learning, which are shared in this article.

Data Collection 52

Data Collection Machine Learning 52

DataRobot Blog

JANUARY 16, 2016

Having a hero / heroine helps you navigate through the difficult times. You look up to them and then think that the problems you thought were difficult are actually trivial in nature. If people can solve and deliver at a much larger scale, you can too! Read more. The post 24 Ultimate Data Scientists To Follow in the World Today appeared first on DataRobot AI Cloud.

52

52

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. Because individual observations have so little information, statistical significance remains important to assess.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Speaker: John Mansour

If you're looking to advance your career in product management, there are more options than just climbing the management ladder. Join our upcoming webinar to learn about highly rewarding career paths that don't involve management responsibilities. We'll cover both career tracks and provide tips on how to position yourself for success in the one that's right for you.

Management

MLWhiz

JANUARY 9, 2016

It has been a long time since I wrote anything on my blog. So thought about giving everyone a treat this time. Or so I think it is. Recently I was thinking about a way to deploy all these machine learning models I create in python. I searched through the web but couldn’t find anything nice and easy. Then I fell upon this book by Sebastian Rashcka and I knew that it was what I was looking for.

Machine Learning 40

Machine Learning Modeling IT 40

Nutanix

JANUARY 21, 2016

There’s no question that enterprise application usability has for the most part been left behind.

Enterprise 20

Enterprise 20

Nutanix

JANUARY 21, 2016

There’s no question that enterprise application usability has for the most part been left behind.

Enterprise 20

Enterprise 20

Nutanix

JANUARY 19, 2016

There’s no question that enterprise application usability has for the most part been left behind.

Enterprise 20

Enterprise 20

Nutanix

JANUARY 19, 2016

There’s no question that enterprise application usability has for the most part been left behind.

Enterprise 20

Enterprise 20

Nutanix

JANUARY 6, 2016

There’s no question that enterprise application usability has for the most part been left behind.

Enterprise 20

Enterprise 20

Advertisement

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.

Big Data