WEBINAR: Data Prep For Data Ops: How To Select & Deploy - 12 December 2018

Data Science Central Webinar Series Event

Data Prep For Data Ops: How To Select & Deploy Join us for the latest DSC Webinar on December 12th, 2018 In recent years, a new term in data has cropped up more frequently: DataOps. As an adaptation of the software development methodology DevOps, DataOps refers to the tools, methodology and organisational structures that businesses must adopt to improve the velocity, quality and reliability of analytics. Widely recognised as the biggest bottleneck in the analytics process, data preparation is a critical element of building a successful DataOps practice by providing speed, agility and trust in data. Join guest speaker, Forrester Senior Analyst Cinny Little, for this latest Data Science Central webinar focusing on how to successfully select and deploy a data preparation solution for DataOps. The presentation will include insights on data preparation found in the Forrester Wave™: Data Preparation Solutions, Q4 2018. During this webinar you will learn: Where does data preparation fit within DataOps What are the key technical & business differentiators of data preparation solutions How to align the right technologies, people and processes Speakers: Will Davis, Sr. Director of Product Marketing -- Trifacta Cinny Little, Senior Analyst -- Forrester Hosted by: Bill Vorhies, Editorial Director -- Data Science Central   Title:  Data Prep For Data Ops: How To Select & Deploy Date:  Wednesday, December 12th, 2018 Time:  9 AM - 10 AM PDT   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.

The Big Data Game Board™ by William Schmarzo via @kdnuggets

Move aside “Monopoly,” “Risk,” and “Snail Race!” Time to teach the youth of the world of an important, career-advancing game: how to leverage data and analytics to change your life! Introducing the “Big Data Game Board™”!

This is great and well worth your investment in time to read and bookmark. I think this could provide you with a clear roadmap on what you need to do in your own organisation. 

WEBINAR: Connected Intelligence Solutions with AI and ML - 11 December 2018

Data Science Central Webinar Series Event

Connected Intelligence Solutions with AI and ML Join us for this latest DSC Webinar on December 11th, 2018 TIBCO Connected Intelligence solutions for energy and utility companies provide powerful capabilities. Our platform connects data, systems, processes, and people—and it delivers predictive analytics, AI, and data visualisations for all aspects of asset management, customer information, distribution, forecasting, production, and supply chain.  The TIBCO platform can help you reduce costs and downtime, increase output, and improve customer retention. It lets you embed machine learning into sensors, processes, and equipment for modernised grids and smarter oil fields.  Speaker: Michael O'Connell, Chief Analytics Officer -- TIBCO Software, Inc. Hosted by: Bill Vorhies, Editorial Director -- Data Science Central Title:  Connected Intelligence Solutions with AI and ML Date:  Tuesday, December 11th, 2018 Time:  9:00 AM - 10:00 AM PST Space is limited so please register early: Reserve your Webinar seat now

Tips for protecting your data when losing an employee by Jason Park via @infomgmt

Most employers would be surprised to learn that departing internal employees can pose a much bigger threat to their business’s data security than external hackers.

These are really good guidelines. Some organisations take away access as soon as an employee tenders their resignation or at least limits it - however I would sound a small caution there - if someone is that keen to take a copy of data they will do that BEFORE they resign - so you have to have good auditing and great control over data transfers/data sticks in your office.

Understanding the new ePrivacy Regulation and how it differs from GDPR by Christian Auty via @infomgmt

The ePR is expected to address electronic communications, including text messages, email, chat applications and IoT devices. Think of the ePR as the traffic cop for data as it travels between controllers and processors governed by GDPR.

This is an insightful article by Christian that I think is a good high level analysis of the differences between the two.

WEBINAR: AI Models And Active Learning - 4 December 2018

Data Science Central Webinar Series Event

AI Models And Active Learning Join us for this latest DSC Webinar on December 4th, 2018 The increased availability of computer resources and the prevalence of high-quality training data combined with smart learning schemas, have resulted in a rise in successful AI deployments. However, many organisations simply have too much data, posing a challenge for data scientists: unless at least some of that data is labelled, it's essentially useless for any ML approach that relies on supervised or semi-supervised learning. So, which data needs to be labelled? How much of a dataset needs to be labelled for an ML application to be viable? How can we solve the problem of having more data than we can reasonably analyse?  One promising answer is active learning. Active learning is unique in that it can both solve this data labelling crisis and train models to be more accurate with less data overall. Join us for this latest Data Science Central webinar where we’ll cover: The pros and cons of active learning as an approach The three major categories of active learning How your active learner should decide which rows need labelling How to obtain those labels How to tell if active learning is appropriate for your ML project Speaker: Jennifer Prendki, VP of Machine Learning -- Figure Eight Hosted by: Bill Vorhies, Editorial Director -- Data Science Central   Title:  AI Models And Active Learning Date:  Tuesday, December 4th, 2018 Time:  9:00 AM - 10:00 AM PST   Space is limited so please register early: Reserve your Webinar seat now

Comparing the performance of machine learning models and algorithms using statistical tests and nested cross-validation by/via @rasbt

Sebastian Raschka compares the performance of machine learning models and algorithms using statistical tests and nested cross-validation.

This blog is great and very much worth a bookmark.  Go and look through the entire series of articles - this is useful bot both those new to data science and those who are experienced too.