WEBINAR: Breaking Down Business Barriers with Enterprise Data Architecture - 5 August 2015

Breaking Down Business Barriers with Enterprise Data Architecture Wednesday, August 5, 2015 11:00am Pacific / 1:00pm Central / 2:00pm Eastern As a data professional, you know how important it is to have good data models and to ensure that your extended team has access to them. Many companies are suffering from silos that keep valuable data from being used effectively because business users may not have the right access or understanding of the data they are using to make decisions. When corporate data has business context with centralized communication and collaboration, the overall integrity and visibility of the data can be improved. To create a business-driven data architecture, you need an enterprise data environment that enables both business stakeholders and IT users to access and collaborate on key models and metadata, at the right levels for their needs. Embarcadero offers the ER/Studio Enterprise Team Edition with the Team Server web portal to help you break down the barriers between business and IT users in your organization. Join this webinar to see how the ER/Studio Team Server works with the Enterprise Team Edition to extend the value of data in your organization, with capabilities including: Business glossaries to specify the terms and definitions for metadata Discussion and activity streams to track requests and actions for tasks Permission structures to give users and groups the right level of access About the Presenter: Josh Buckner is the ER/Studio Team Server Solutions Expert for Embarcadero. He helps customers understand the benefits of Team Server and works with them to implement it effectively in their organizations. Register here

Python at Scale for Data Science Via @cloudera

Nice overview in their blog of a new data analysis framework called "Ibis" that has the goal of making big data as easy to work with as small data. It’s exactly the same Python you know and love but at scale.

We are data: the future of machine intelligence via @ftmag by @dougcoupland

Thought-provoking article about freedom and control in a world driven by metadata. The internet is going to do to us whatever it is going to do - and it’s far too late to stop it.

The Big 'Big Data' Question: Hadoop or Spark? by @BernardMarr via @DataScienceCtrl

A great guest blog by Bernard Marr where he talks about Hadoop vs Spark as a big data framework

I agree completely that they can both work together to give a more complete and rounded solution.

Can Police Use Data Science to Prevent Deadly Encounters? via @sciam

As part of Obama's Police Data Initiative, researchers are studying predictive analytics to identify officers whose unprofessional behaviour could cause problems in the communities they serve. There are a lot of interesting issues around this.

An executive’s guide to machine learning via McKinsey

This McKinsey Report provides a great overview of machine learning for smart people that aren't necessarily machine learning experts. This is really an opportunities and strategies report for the C-Suite, which provides insights into how well machine learning is understood and appreciated by decision-makers.

A Tutorial on Loops in R – Usage and Alternatives via @DataCamp

Great tutorial on using loops in R from DataCamp's blog.

I love the examples.  All I would say is that you need to be careful when using loops and be sure it is the right kind of loop for what you want to do.  Also check that it really does what you think it will do.  Do a dry run of the loop and it's behaviour using either a piece of paper or a computerised method of making notes just to be sure you know how the loop and the commands around and within it will behave.  5 minutes checking now could save a lot longer later on.  Have fun!