WEBINAR: Transforming 3rd Party Data Into Actionable Insights - 28 November 2018

The rise of third party or external data has given data scientists and organisations additional building blocks to discover breakthrough insights. But many data scientists struggle to understand what third party data is relevant and struggle further to efficiently access and transform that data. In today’s Data Science Central webinar, we’ll explore innovative techniques to simplify third party data access and transformation. You will learn: Techniques for assessing third party data quality and relevance Strategies for accessing third party data Information about the third party data landscape as it applies to business outcomes Speakers: Mark Hookey, CEO -- DemystData Richard Scioli, General Manager, Platform -- DemystData Hosted by: Bill Vorhies, Editorial Director -- Data Science Central Title:  Transforming 3rd Party Data Into Actionable Insights Date:  Wednesday, November 28th, 2018 Time:  09:00 AM - 10:00 AM PST Space is limited so please register early: Reserve your Webinar seat now After registering you will receive a confirmation email containing information about joining the Webinar.

Managing risk in machine learning by Ben Lorica via @OReillyMedia

Machine learning models are becoming mission critical. Ben Lorica reveals data from a recent survey on ML adoption and discusses some important considerations for managing risk in machine learning.

This is really clear and easy to understand. A good place to start and it will give  you something to think about. Maybe it will give you something to consider in your own processes?

Simpson’s Paradox: How to Prove Opposite Arguments with the Same Data by @koehrsen_will via @Medium

Here's an explanation of Simpson's paradox and some interesting aspects of this statistical phenomenon, such as correlation reversal.

I love this - it's definitely worth a bookmark and some applause on Medium for an insightful and well written explanation of this important principle.

WEBINAR: Scaling Big Data Pipelines in Apache Spark, No Coding Required - 15 November 2018

Various companies across multiple industries collect and house vast amounts of data. However, most face the same challenge: the ability to process big data and quickly find insight within its framework. Introducing KnowledgeSTUDIO with Apache Spark, the ultimate solution for both data scientists and data analysts. The graphical user interface with Big Data capabilities allows organizations to build pipelines seamlessly. Topic: Scaling Big Data Pipelines in Apache Spark, No Coding Required Date: Thursday, November 15, 2018 Time: 2 p.m. ET/11 a.m. PT Datawatch Speakers: Dr. Steve Walker, Sr. Data Scientist and Mike Rowley, Product Director   Join us and learn how users of KnowledgeSTUDIO for Apache Spark, a wizard-driven productivity tool for building Spark workflows, have overcome these challenges. Learn how data science teams can:  Utilise interactive workflows with an automated design canvas for building, displaying, refreshing, and reusing analytic models   Automatically generate code that can be customised and incorporated into production scripts   Include manually written code within the graphical workflow   Leverage advanced modelling with open source packages such as Spark ML, Spark SQL   Avoid overhead costs of parallelisation when datasets are very small   Build, explore data segments, and discover relationships using patented Decision Tree technology REGISTER NOW

The Future of Cybersecurity: How to Protect Your Business from Great Data Risks by/via @Datafloq

A data breach can have severe consequences for your business (and your career). And a recent OTA report concluded that 93% of data breaches were entirely avoidable. Taking these steps to avoid a data breach can save you a lot of headaches down the road.

Good list of steps to make sure you are aware of and doing something about - definitely something to use as a light level list to take forward and expand from.

3 best practices for improving and maintaining data quality by Maxim Lukichev via @infomgmt

Organisations are increasingly relying on insights generated by data analysis, and they realise that insights are only as good as the data they come from.

Maxim makes some very good points in here.  I think any data analysis with bad data is at best worthless and at worst destructive for your business as you will be making key decisions based on something which is not correct. It is important that you validate your data to make sure it is trustworthy and have a network of data stewards in your business to ensure that data is correct and processes and in some cases systems are updated to make sure that quality is improved and assured going forward.

How to build your own AlphaZero AI using Python and Keras by David Foster via @Medium

This tutorial shows you how to build a replica of the AlphaZero methodology to play the game Connect 4—and how to adapt the code for other games.

This looks really good and is worth following and trying.