Silicon Valley siphons our data like oil. But the deepest drilling has just begun

The Guardian

Customers in the UK will soon find out. Recent reports suggest that three of the country's largest supermarket chains are rolling out surge-pricing in select stores. This means that prices will rise and fall over the course of the day in response to demand. Buying lunch at lunchtime will be like ordering an Uber at rush hour. This may sound pretty drastic, but far more radical changes are on the horizon.


Silicon Valley siphons our data like oil. But the deepest drilling has just begun

#artificialintelligence

Customers in the UK will soon find out. Recent reports suggest that three of the country's largest supermarket chains are rolling out surge pricing in select stores. This means that prices will rise and fall over the course of the day in response to demand. Buying lunch at lunchtime will be like ordering an Uber at rush hour. This may sound pretty drastic, but far more radical changes are on the horizon.


From Data Analysis to Machine Learning

#artificialintelligence

This article was originally posted here, by Mubashir Qasim. In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization.


Data Cleansing and Exploration for the New Era with Optimus

@machinelearnbot

Data scientists, data analysts, business analyst, owners of a data driven company, what do they have in common? They all need to be sure that the data that they'll be consuming is at its optimal stage. Right now with the emergence of Big Data, Machine Learning, Deep Learning and Artificial Intelligence (The New Era as I call it) almost every company or entrepreneur wants to create a solution that uses data to predict or analyze. Until now there was no solution to the common problem for all data driven projects for the New Era - Data cleansing and exploration. With Optimus we are launching an easy to use, easy to deploy to production, and open source framework to clean and analyze data in a parallel fashion using state of the art technologies.


What Overstock.com learns about its customers from decades of data

ZDNet

In the competitive retail industry, personalization strategies have become table stakes. But for a brand to really connect with a customer, it first has to know that customer. Data science and machine learning are making it easier for brands to get useful insight into their customers based on their behavior. Director of Data and Audience, talked to ZDNet about the different insights it can gain from its customer data -- what decades-old information about a customer can tell you versus the latest updates to their shopping cart. Overstock.com is marketing roughly five-plus million products on a global scale, Robison noted.