Data Science and Beyond

AUGUST 4, 2016

Data science can be defined as either the intersection or union of software engineering and statistics. In recent years, the field seems to be gravitating towards the broader unifying definition, where everyone who touches data in some way can call themselves a data scientist. Hence, while many people whose job title is Data Scientist do very useful work, the title itself has become fairly useless as an indication of what the title holder actually does.

Statistics 59

Statistics Data Science Software 59

In(tegrate) the Clouds

AUGUST 4, 2016

In my last post, I wrote about the new data integration requirements. In this post I wanted to share a few points made recently in a TDWI institute interview with SnapLogic founder and CEO Gaurav Dhillon when he was asked: What are some of the most interesting trends you’re seeing in the BI, analytics, and data warehousing space? There are three trends that I believe are fundamentally changing the world of data.

IoT 45

IoT Internet of Things Data Lake Data Warehouse 45

Nutanix

AUGUST 4, 2016

It’s that time of the year again! VMworld US 2016 is around the corner and there is nothing (other than our own.NEXT!) like the energy of over 20,000 people in Las Vegas talking technology. As always, Nutanix has a lot going on both on the show floor and off.

Technology 20

Technology 20

The Unofficial Google Data Science Blog

JULY 31, 2016

By JEAN STEINER Randomized A/B experiments are the gold standard for estimating causal effects. The analysis can be straightforward, especially when it's safe to assume that individual observations of an outcome measure are independent. However, this is not always the case. When observations are not independent, an analysis that assumes independence can lead us to believe that effects are significant when they actually aren't.

Experimentation 42

Experimentation Testing Measurement Metrics 42

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Experimentation

Darkhorse

AUGUST 5, 2016

This is the third part of a series of articles about effective analytics implementation. The first part “ The Five Faces of Analytics ” explores the roles you need on your team. The second “ Pluck the Low-hanging Fruit ” helps you discover and prioritize projects. An analytics project is a house of cards. Each project is really three: a change management project stacked on top of a software development project stacked on top of a research project.

Measurement 40

Measurement Metrics Modeling Visualization 40