19 top paying Internet-of-Things jobs by Bob Violino via @infomgmt

The Internet of Things remains one of the hottest trends in technology. Here's how the demand is translating to salaries for experienced professionals.

Good if you want to know what skills and roles you need to aim towards in your own career.

How to Spot a Machine Learning Opportunity, Even If You Aren’t a Data Scientist by Kathryn Hume via @HarvardBiz

Having an intuition for how machine learning algorithms work - even in the most general sense - is becoming an important business skill.

Interesting and definitely worth a read.

Graph Databases Help Companies Unlock Connections Within Their Data by Tom Smith via @DZone

Once you become familiar with graph databases, you’ll expand your view of how to ingest and analyse unstructured data at speeds you cannot imagine.

This is in the form of an interview with Jim Webber, Chief Scientist at Neo4j. Really interesting.

The Python and R Graph Gallery by/via @R_Graph_Gallery

This could be handy for your next Python or R data viz project: hundreds of charts along with the reproducible R and Python code.

Something to bookmark and keep for the next time you need to find a a great chart for your data.

WEBINAR: Predictive Forecasting with Time Series Analysis - 7 November 2017

Overview

Title: Predictive Forecasting with Time Series Analysis

Date: Tuesday, November 07, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Predictive Forecasting with Time Series Analysis

The ability to accurately predict what is likely to happen at a point in the future, and build plans and strategies based on that knowledge, is essential to an organization’s success. But what happens when a forecast is inaccurate? What is the impact on a business, its customers or its partners? For businesses, the ability to catch even a tiny glimpse of what the future may hold can lead to happy customers, improved efficiency and productivity, and highly successful business decisions.

In this Data Science Central webinar learn how time series analysis better enables departments across your organization with actionable, more accurate insights related to the timing of equipment failure, customer offers, and the impact of effects like seasonality.

Speakers:

Murali Prakash,  IBM Product Manager  -- IBM SPSS
Mikhail Lakirovich, IBM Offering Manager  -- IBM SPSS
Douglas Stauber, IBM Offering Manager -- IBM SPSS

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

 

Register here

The Seven Deadly Sins of AI Predictions by Rodney Brooks via @techreview

"Mistaken extrapolations, limited imagination, and other common mistakes that distract us from thinking more productively about the future."

Great list and I agree with them 100%

The top MDM and data governance consultancies by David Weldon via @infomgmt

The Master Data Management Institute offers its picks for the top global and regional systems integrators, and advice on how organisations can best work with them.

Great lists of consultancies and integrators for MDM.

Introducing R-Brain: A New Data Science Platform by @idigdata via @kdnuggets

R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker, which supports not only R, but also Python, SQL, has integrated intellisense, debugging, packaging, and publishing capabilities.

Great article and it sounds like a great platform. I'm hoping to have a go with it next week if I can find the time to play.

Beyond Hadoop by James Ovendon via @iegroup

A company once synonymous with big data is on its way out, but what comes next?

Interesting.  So people are starting to use alternate to Hadoop or using it for other reasons.

AI & Machine Learning : The Most Used Fundamental Terminology of It via @08DevEsh

Suddenly, artificial intelligence (AI) is everywhere. For decades, the dream of creating machines that can think and learn like humans seemed like it would be perpetually out of reach, but now artificial intelligence is embedded in the phones we carry everywhere, the websites we use every day and, in some cases, even in the appliances we use around our homes.

Great list and explanation of terms.

3 ways machine learning is revolutionising IoT by Majeed Ahmad via @networkworld

One of the greatest boons that machine learning and its algorithms have delivered to the IoT is how easily it integrates into the IoT’s platforms.

Interesting article that makes some really good points.

Even This Data Guru Is Creeped Out By What Anonymous Location Data Reveals About Us by DJ Pangburn via @FastCompany

Using code and the web, a data scientist follows two unnamed people and learns just how much our anonymous location data can say about who we are.

Scary. There is some advice on settings at the end that you should read.

How to win Kaggle competition based on NLP task, if you are not an NLP expert by Artem Farazei via @indatalabs

Here is how he got one of the best results in a Kaggle challenge remarkable for a number of interesting findings and controversies among the participants.

This is great for a number of reasons. I like the way it tries to teach you another way of ooking at the problem and yet when you read this it seems so obvious.

WEBINAR: Data Literacy – Closing the Data Skills Gap - 26 October 2017

Overview

Title: Data Literacy – Closing the Data Skills Gap

Date: Thursday, October 26, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Data Literacy – Bridging the Gap

With the volume and velocity of data available in the world today, most industries and companies have a desire to use that data better.  Unfortunately, as data has grown at incredible speeds, there has followed a real and growing data literacy skills gap. This skills gap can lead to major issues within organizations, which is why understanding what data literacy is and how to alleviate this gap is so important. 

Join us for this latest Data Science Central webinar to learn more about the growing data literacy skills gap, what exactly data literacy is, and how your organization can be better prepared in the current data revolution.

Speaker: Jordan Morrow, Program Manager of Data Literacy -- Qlik 

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

  

Register here

Keras Cheat Sheet: Deep Learning in Python by Karlijn Willems via @DataCamp

Keras is a Python deep learning library for Theano and TensorFlow. The package is easy to use and powerful, as it provides users with a high-level neural networks API to develop and evaluate deep learning models. With this library, you’ll be making neural network models in no time!

This is great - well worth signing up with DataCamp and getting some tools and courses that are incredibly useful.

WEBINAR: How data archiving supports GDPR readiness - 26 October 2017

Web Seminar  How data archiving supports GDPR readiness

Oct. 26, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

The EU’s General Data Protection Regulation (GDPR) will take effect in May 2018. Whether your organization is located inside or outside Europe, it will be impacted if it holds data on even a single European resident. One element of GDPR preparedness should include your data archiving strategy, or how you handle personal data on either production or legacy systems that is no longer changing. Just because static information might not be actively used, it still is subject to GDPR.

This webinar will address how GDPR impacts organizations in North America and will specifically explore the large volume of static data that is residing on both production systems and outdated, redundant, or otherwise obsolete applications that are common in many organizations. Participants will hear from GDPR experts from OpenText and data archiving specialists with Flatirons Jouve.

You will learn:

• How the GDPR impacts U.S.-based companies
• Foundations of an enterprise information management strategy essential for GDPR
• How data archiving supports GDPR readiness
• Specific capabilities of archiving platforms that address privacy by design, consent, data portability, the right to erasure, and other GDPR requirements

This webinar is essential for anyone responsible for compliance, data strategy or management, enterprise information management, information governance or architecture, or application strategy or management.

Bryant Bell Director Product Marketing OpenText (Speaker)	Gino Vicari Principal Value Consultant OpenText (Speaker)	Bill Young, Ph.D. Consultant Flatirons Jouve (Speaker)	Jim Ericson  Consultant, Editor Emeritus Information Management (Moderator)

Sponsor Content From:

Register here

23 more insurers sign on with blockchain consortium by Nathan Golia via @infomgmt

Aon, Chubb, Willis Re and 20 more carriers have joined B3i, a working group looking to develop blockchain-based insurance solutions.

This is great news and means that blockchain is definitely here to stay in the insurance world.

Five big data misbeliefs that could cost organisations millions by Marc Wilczek via @infomgmt

Information is the engine spurring digital growth. Yet, false truths are causing risks and preventing companies from capitalising on their data-driven strategies.

I most definitely agree with numbers 1 and 3!!

WEBINAR: The Fast Path to Success with AI - 26 October 2017

Across industries, AI is transforming business. The organisations that harness the power of their data will overwhelm competitors who do not. Unfortunately, while business leaders know AI is essential to long-term success, they lack the familiarity with these kinds of solutions to successfully execute. What’s more, the data scientists needed to build AI solutions are in short supply and high demand.

The good news is that advanced AI technologies like automated machine learning bring AI into reach for everyone. With these tools and technologies, most companies will make progress without hiring data scientists and without expensive training. These advanced tools make it possible for almost anyone to build predictive models without writing a single line of code or having deep knowledge of the algorithms.

In this webinar, Greg Michaelson, PhD, and Head of DataRobot Labs, will review the practical first steps an organisation takes toward becoming an AI-driven enterprise and remaining competitive in the coming years

You will discover how to:

• Train users across your organisation - including business leaders - to spot AI opportunities
• Systematically identify the opportunities best suited for accelerating your company’s AI efforts and offering the highest ROI
• Quickly experience AI success with small, successful projects comprised of the people and data you have today
• Get AI implementation right the first time

Event details

Thursday, October 26

1:00 pm ET/10:00 am PT

45 minutes with Q&A

Speaker:

Greg Michaelson, PhD

Head of DataRobot Labs

Register here

How can Blockchain Revolutionise Mobile App Security? by @junedghanchi via @Datafloq

Blockchain can increase mobile app security in various domain, ranging from data security to data transactions.

I agree that blockchain needs to be used in other areas than the financial services side of things.  However we do need to look at alternatives too in order to have a more balanced environment.

3 Ways Blockchain Will Transform the Internet of Things by @VanRijmenam via @Datafloq

There is no denying the power of the Internet of Things (IoT). IoT devices are already in 60 percent of U.S. homes using a broadband connection, and an estimated 200 million vehicles will be connected to the internet by 2020, standing to transform entire industries for a good reason.

Great article by Mark and I completely agree that strategies need to be combined and cross referenced as they often need to be handling the same area at the same time.

VIDEO: Build smart applications with your new super power: Cloud AI by @Ppoutonnet via @oreillymedia

In this video Philippe Poutonnet discusses how you can harness the power of machine learning, whether you have a machine learning team of your own or you just want to use machine learning as a service.

I found this really interesting and a great way to have ML for all.

WEBINAR: Moving from BI to Automated Machine Learning - 18 October 2017

Wednesday, October 18, 2017 1:00 pm Eastern / 10:00 am Pacific 35 minutes with Q&A

Machine Learning has become a competitive differentiator in a big data world. Vast amounts of data are already overwhelming existing BI tools and analytics processes. When faced with hundreds of variables, a human's ability to efficiently identify new insights or detect changing patterns manually has also been exceeded. 

To address these challenges, BI and analytics professionals are adopting user-friendly, automated machine learning solutions.

In this webinar hosted by DataRobot, recognised analytics industry expert Jen Underwood will discuss how BI and analytics professionals can get started with automated machine learning. She will cover:

• An introduction to machine learning and popular machine learning use cases

• CRISP-DM Methodology and common algorithms (regression, clustering, classifiers, and more)

• How to get started with Automated Machine Learning 

Guest Speaker: Jen Underwood Founder, Impact Analytix

Register here

A history of AI in 10 landmarks by @lukedormehl via @DigitalTrends

Ten moments to remember in the development of AI.

Interesting to look back and think about where it has come from and how that progression happened.

How analytics and machine learning can aid transplant decisions by Dimitris Bertsimas and Nikolaos Trichakis via @infomgmt

A new tool looks at 10 years of data and millions of prior decisions to estimate a patient’s waiting time in the context of a current active organ offer.

Great idea and a good use of these techniques and the data that has been collected.

Selling data to feed hedge fund computers is one of the hottest areas of finance right now by @johndetrixhe via @qz

Kumesh Aroomoogan has come a long way from his days at Wall Street bank Citigroup, where his job was to stay on top of breaking news. He remembers getting yelled at by a trader because he stopped watching news feeds to go the bathroom and missed a market-moving headline. Now, he’s the co-founder and CEO of Accern, a company that automates his old job.

Interesting article - seems it's not only money but data that makes people wealthy.

Definition of a Quant Fund here.

80% of data scientists will have deep learning in their toolkits by 2018, predicts Gartner by Divina Paredes via @cio_nz

Deep learning, a variation of machine learning (ML), represents the major driver toward artificial intelligence(AI), reports Gartner.

Good well thought out article based on the Gartner report. Deep learning is definitely a step above machine learning so I can understand the usefulness.

Why AI and advanced analytics benefits still elude most organisations by Tony Fisher via @infomgmt

Enterprises are too often drowning in an over-abundance of data types and sources - many of which contradict each other.

I think he makes some good points - how can you work out what you have and what you can do from it?  How can you work out what you can do with it?  If you don't have data management you can't know what you have so that you can think about what you can do with it.  It's clear to me that you have to have clear benefits and understand your data in order to do any kind of project and actually achieve those benefits.

Data Security for Data Scientists by @therriaultphd via @Medium

Ten practical tips for protecting your data (and more importantly, everyone else’s!)

These tips are great and you should study them quite closely. I know in the past I've probably broken some of them although at that time I don't know if anyone was so conscious of those things.

Analytics maturity models and the control environment by Angel Serrano via @infomgmt

It is important to maintain information quality and a management framework to ensure that the data used for the analysis is fit for the purpose.

This was interesting and reminds me of all the other maturity models in existence like CMM and these in the list on Wikipedia.

AI is changing the skills needed of tomorrow's data scientists by Ashish Thusoo via @infomgmt

It’s critical that today's students understand how analytics is evolving and how artificial intelligence can help solve real world problems.

This is a great summary of the kinds of skills that are becoming necessary. Worth reading and thinking hard about the kinds of skills you might need so you can fill any gaps that you have.

Manchester Mayor to work with Microsoft to boost digital skills in youngsters via @MicrosoftUK

Manchester Mayor Andy Burnham wants to work with Microsoft to boost digital skills in youngsters.

A great use of new technology and something that could produce good results for us all. Well done Microsoft.

New Theory Cracks Open the Black Box of Deep Learning by Natalie Wolchover via @QuantaMagazine

Naftali Tishby is a researcher with an important new idea about how deep learning might work. He proposes that the most important part of learning is actually forgetting and his ideas about "information bottlenecks" are getting a lot of attention around the web this week.

Wow.  This makes so much sense when you think about it.

How AI apps for banks are changing the face of the financial sector by Rahul Sharma via @TechGenix

Artificial Intelligence is already changing the face of banking on a global scale. Long before chatbots popped up as interesting business-use cases, long before mobile banking applications offered military-grade secure transactions, and much before focused analytics tools for banking made themselves known.

I really like this and it makes you stop and think about all the ways you are already encountering AI from banks already.  Certainly I already use a robo-adviser for investments because I am a customer of Nutmeg.

6 data modeling best practices for better business intelligence by Kayla Matthews via @infomgmt

Although specific circumstances vary with each attempt, here are six tips to follow that should improve outcomes and save time.

I definitely agree with some of these observations.  Time, Status and history of values are all very useful at times. However be guided by the requirement - just be sensible in your design so you could extend it easily - you need to keep the future in your mind so check the roadmap of projects coming up in case they impact even slightly.

You will find this link useful - slowly changing dimensions

Sentiment analysis on Trump's tweets using Python by @FerroRodolfo via @ThePracticalDev

This tutorial shows how to use Twitter's API to access a user's Twitter history and perform basic sentiment analysis using Python's textblob package. Includes lots of code snippets and it's trivial to swap in any Twitter user you might be interested in.

This is just genius - you really need to read/understand and try this so that you can then use the same techniques/methods on other data.