A Dead Simple Tool To Find Out What Facebook Knows About You by Katharine Schwab via @FastCoDesign

Facebook builds complex profiles of each of its users so it can offer data points to advertisers for targeting ads. Some data points are obvious, like your age and interests, but many may surprise you. This free tool reveals the unsettling amount of information Facebook tries to deduce about you.

Wow - just wow.

Machine-learning model predicts remission, relapse in cancer patients by Greg Slabodkin via @infomgmt

Researchers were able to teach a standard 64-bit computer workstation running Windows to predict remission with 100 percent accuracy, while relapse was correctly predicted in 90 percent of relevant cases.

This is great news and points to the future.

Automatically Segmenting Data With Clustering by @bilalmahmood via @kdnuggets

In this post, the author walks through one such algorithm called K-Means Clustering, how to measure its efficacy, and how to choose the sets of segments you generate.

Useful and worth reading even if you already know this just to make sure you are clear on it.

Making Python Speak SQL with pandasql by/via via @YhatHQ

Want to wrangle Pandas data like you would SQL using Python? This post serves as an introduction to pandasql, and details how to get it up and running inside of Rodeo.

This is a great post and includes lots of code and examples - one for you to bookmark and sign up for his updates while you are there!!

WEBINAR: How to Identify and Track Customers Across Platforms with a Universal ID - 28 February 2017

Webinar Event Details

Date: Tuesday, February 28, 2017
Time: Noon ET/ 9:00 am PT
Duration: 60 minutes (including Q&A)

What You'll Learn In an economy where customer attention is fleeting, expectations are constantly changing, and competitors are just a click or a tap away, understanding customer behaviour is critical. Before you can dive into any kind of advanced customer behaviour analysis, however, you must ensure that you are counting each customer once and only once ― a difficult task, especially with customer touchpoints ranging across a variety of devices and website domains. So how do you accurately identify your customers? In this webinar, Erin Franz, data analyst from Looker, and Julie-Jennifer Nguyen, Product Marketing Manager at Segment, show you how to determine if you are correctly counting your customers and how to create a universal user ID for customers across touchpoints. You will learn: • Which signals indicate that you are miscalculating your customers • Why creating a universal ID is the backbone of customer analysis • Best practices for identifying customers, including how to tie identities across anonymous and logged-in sessions, account for changed email addresses, and plan for cross-platform interactions • How to derive a user table of universal IDs with SQL and LookML results.

Presenters Erin Franz is a data analyst technology lead at Looker. She focuses on partner technical integration and enablement. Prior to Looker, Erin worked in analytics at Accenture where she helped build out big data solutions for enterprise customers. Julie-Jennifer Nguyen is a Product Marketing Manager at Segment where she helps advocate for the customer, support the product development lifecycle, and enable the sales team. Before Segment, she was in charge of CX Strategy and Analytics at Warby Parker.

Register here

Using Machine Learning to predict parking difficulty via @googleresearch

Google released a feature for Google Maps for Android in 25 US cities that predicts parking difficulty close to your destination so you can plan accordingly. To build it, Google had to overcome obstacles like the lack of real-time information about parking spots, high variability (day, time, work day, events, etc.), difficult to graph parking structures, and illegal parking. Google used a combination of crowdsourcing and machine learning to address those issues. Here's how they did it.

Posted by James Cook, Yechen Li, Software Engineers and Ravi Kumar, Research Scientist

Interesting feature that could be very useful in London or any other busy city. Lets hope they are able to roll it out.

How Big Data and AI Help Us Tackle The World’s Biggest Problems in 2017 and Beyond by @BernardMarr via @Data_Informed

Can computers solve all our problems? Well, when combined with the creative power of humans, the answer is… maybe.

I find all of these examples to be very exciting and can't wait to see how they develop long term (as initial progress is not always an indicator of long term success).