SLIDESHOW: 8 Steps to Success With Self-Service Analytics by David Weldon via @infomgmt

Self-service analytics are growing in popularity because the benefits are many and they can be easily deployed by non-technical users. Consider the following checklist for an effective and comprehensive self-service analytics strategy.

I think slide 10 is crucial - without data integrity there really is no point with providing any analytics.

SLIDESHOW: 7 Key Considerations When Choosing a Data Pipeline Service via @infomgmt

Picking a service that manages your data isn’t something to be taken lightly. You’ll want to research a few different services before choosing what is right for your company. Here is advice on how to look at different services to make sure you really understand the value each brings to the table.

I find slide 9 quite important even though the sideshow says it is a bonus.  In the past data has been put into a data warehouse or whatever is driving your reporting and just sits there forever.  All data has a shelf life and needs to be moved, updated or archived off in a timely manner else you run the risk of your reports and analyses being incorrect.

18 Free Exploratory Data Analysis Tools For People who don’t code so well by Manish Saraswat via @AnalyticsVidhya

Some of these tools are even better than programming (R, Python, SAS) tools.

Why write code if you don't have to/or don't have the ability?

INFOGRAPHIC: The bot platform ecosystem by Jon Bruner via @OReillyMedia

A look at the artificial intelligence and messaging platforms behind the fast-growing chatbot community

Wow I had no idea there were so many of them.

R Graphics Tutorial Series by Ankit Agarwal via @gladwinalytics

During one of the recent R workshops Ankit Agarwal conducted in his organisation, one of his colleagues asked for the reference material for "Graphics using R" and he thought of putting together this series of blogs which would help readers delve into the details of 'R Graphics' starting from the basics of Graphics.

This is a great collection of blogs and well worth a bookmark so you can refer back to them.

SLIDESHOW: 15 Top Open Source Artificial Intelligence Tools by Cynthia Harvey via @Datamation

Here's a list of 10 popular open source AI tools.

Great list and worth using it as a list of tools to try.

Becoming a Big Data Scientist: Skills You Need to Know and How to Learn Them by @ricknotdelgado via @Datafloq

To say that data scientists are in high demand would actually be sort of an understatement. With big data being utilised more and more within organisations, executives want men and women who know big data inside and out. The number of data scientist positions is on the rise and growing each year. This demand is reflected in the amount of money being paid to data scientists, with the median salary for computer and information research scientists hitting more than $110,000 in 2015, according to the Bureau of Labour Statistics. But it’s not enough to be considered a data scientist, you need to have the right skills to get noticed above your peers.

I would suggest online courses via Udemy, Coursera, etc and try to get into Kaggle to enter some competitions (you will have to pass tests to get in there).

Artificial intelligence and the future of design by Jon Bruner via @OReillyMedia

How algorithms will optimise everything.

I found this really interesting and can see the world as he describes it at the end of the article.

How to Choose the Right BI Tool for your Business by Mark Cunningham via @Data_Informed

Most companies understand the importance of being able to accurately consolidate, blend, and analyse their data to better understand what is happening in their business and to help decide what to do next. But many business teams have only scratched the surface of what is possible with data and data analytics.

He is so right - everyone just goes to a spreadsheet first.  Even when you have a BI tool there is still the temptation to do it in a spreadsheet and treat the tool like just something for visualisation.

Competitive Intelligence Implications from IoT Applications by Paul Santilli via @Data_Informed

The Internet of Things (IoT) is certainly all the rage these days. Every major technology firm or vertical industry icon claims to be shaking up the norm with the latest and greatest application in this space. With industry analysts predicting 50 billion connected devices and revenue to reach $1.7 trillion by 2020, it seems like just about every major vendor is trying to reposition their products to capitalise on the growing IoT market.

So many things I hadn't really realised that it could add competitive advantage.

What is Google Data Studio and how can you use it? by @sherrybonelli via @sengineland

Currently in beta, Google Data Studio allows you to create branded reports with data visualisations to share with your clients. Columnist Sherry Bonelli explains the benefits and how to try it out.

Wow - another useful tool from Google.I also found this which looks good:  Google Data Studio: 5 Reasons To Use It Today  via @digitalmktr

WEBINAR: What does IoT Edge Analytics look like? - 27 September 2016

Overview

Title: What does IoT Edge Analytics look like?

Date: Tuesday, September 27, 2016

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

What does IoT Edge Analytics look like?

This webinar will showcase the new Statistica features around Edge Scoring for IoT analytics and Native Distributed Analytics Architecture (NDAA). In this webinar a demonstration of deploying advanced analytics onto any edge device or IoT gateway and running analytic workflows at the edge of the network to transport predictive results. Discover NDAA that will enable you to build workflows and train and run models completely within your database engines without transporting data out of your Big Data systems.

You will learn how to:

• Deploy analytics onto the network edge.
• Reduce the need to transfer large data-streams over the network.
• Build and execute advanced analytics on Big Data systems.
• Carry out intensive computations with all data in every source system, including Hadoop clusters, database appliances and other high-performance platforms.

Speakers:
Jeremy Melville -- Senior Data Scientist -- Dell Statistica  
David Sweenor, Global Analytics Product Manager -- Dell Statistica 

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

WEBINAR: Whitelisting: The Art and Science of Business-Driven Information Security - 28 September 2016

  

Web Seminar  Whitelisting: The Art and Science of Business-Driven Information Security

September 28, 2016 | 2 PM ET/11 AM PT
Hosted by Information Management

Security would be easy if you could just shut things down. But the success of your business depends on enabling people to access the information they need, where and when they need it.

Effective security thus depends on rigorous enforcement of security policies that align with the constantly changing needs of highly mobile, information-hungry knowledge professionals.

Attend this webinar to learn how you can create, manage, and enforce smarter information security policies that:

• Minimize information risk
• Maximize business performance
• Simplify regulatory compliance
• Reduce IT workloads and costs

Plus you’ll have the unique opportunity to directly ask top security practitioners your own questions about risk and compliance in the new no-perimeter enterprise.

Featured Presenters:

Moderator:
Lenny Liebmann
Founding Partner
Morgan Armstrong

Speaker:
Patrick Gray
IT Security Consultant
Patrick Gray & Assoc.

Speaker:
Phil Alexander
Information Security Officer
University Medical Center

Register here

The Neural Network Zoo by Fjodor Van Veen via @asimovinstitute

With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first.

So Fjodor decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and unique, when I drew the node structures… their underlying relations started to make more sense.

This is really great and while I still struggle with this at least I know I can go back to this to try to understand more.

Beware of the gaps in Big Data by Edd Gent via @TheIET

As we entrust ever more of our lives to ‘big data’, how can we protect against the gaps and mistaken assumptions used to handle the information?

Great article pointing out a lot of things that can go wrong with relation to the loading of and use of Big Data (or any data really).

WEBINAR: IT Security in the Cloud - 27 September 2016

1. Tue, Sep 27, 2016 5:00 PM - 6:00 PM BST

Cloud computing has generated a lot of excitement due to its flexibility and potential cost savings; however, it also brings significant trials to IT security systems, processes, and policies. While cloud-based environments face many of the same security threats as traditional systems, the complexity, reliance on third-parties, and evolving control technologies and policies make IT security challenging. 

During this webinar, we will make a business case for using cloud computing and discuss important factors such as cloud security challenges; cloud provider management; encryption and key management; identity management and trust boundaries; governance, standards, and legal concerns; cloud storage; and information lifecycle management. 

This presentation is brought to you by the MS in Data Analytics and BS in Information Systems online degree programs at CUNY School of Professional Studies. 

PRESENTER: 
Joe Sabelja leads identity management at a major financial firm. He has over 20 years experience in data security and data management, working in roles on the business side and in IT strategy, at major financial firms in New York, Singapore, London, and Sydney. He currently works on strategy development for global access control systems. Joe has a B. Eng (Elec.) from Sydney University and an MBA from The Australian Graduate School of Management.

Register here

Goldman Sachs Isn't That Worried About Technology Destroying Your Job by Narae Kim via @infomgmt

The rise of automation, online tools, and big data echoes industrial revolutions of the past, with occupations and businesses following a "natural evolution" as technology advances, the bank argues.

Whilst I can agree what governments could do to help with employment, I can't necessarily see them actually doing it.

SLIDESHOW: What the Top 14 Relational Database Skills Pay via @infomgmt

By most accounts, the demand for professionals with data skills will remain strong heading into 2017. That translates into attractive salaries, especially for those with the most niche skills. According to the just-published “2016 Data Science Salary Survey” by O’Reilly Media, data pros with relational database skills can expect the following pay.

Interesting.

A Beginner’s Guide To Understanding Convolutional Neural Networks Parts 1 and 2 by Adit Deshpande via @kdnuggets

Interested in better understanding convolutional neural networks?

Here are Part 1 and Part 2 - both are 2 pages long

Wow - I read these both about 3 times each before I felt I understood it completely.

Data Sharing Critical to Success of Cancer Moonshot by Greg Slabodkin via @infomgmt

The goal of the Obama administration’s Cancer Moonshot, led by Vice President Joe Biden, is to make a decade’s worth of progress against cancer in five years.

If this works and makes great progress with Cancer it can be repeated for other dreadful illnesses.

WEBCAST: Six (Million) Degrees of Separation: The Practical Power of Network Analytics - 22 September 2016

The concept of “six degrees of separation” says that any person in the world is only six introductions away from every other person through their network of friends, family and acquaintances. Whether you believe this is true or not, the impact of studying and analyzing networks can be a powerful tool for solving many business challenges. Networks are everywhere. Simple or complex, they can consist of people, institutions, processes, equipment and more.

Network analytics examines the relationships between the objects in a network. This webcast will cover how the new network analytics functionality in Statistica 13.1 can be applied for fraud detection, supply chain optimization, recommendation engines, and identifying influencers and affinities.

What you will learn

Attendees will learn:

• Business use cases where network analytics can be applied
• The role of the graph database and how to structure and load data
• Visual exploration of relationships
• Clustering, scoring and anomaly detection

Speakers

Caroline Junkin is a data scientist for Statistica. She has a master's degree in information management and has been working in the field of business intelligence and advanced analytics for over 17 years. Her experience in data engineering, predictive analytics and application integration spans many industries, including healthcare, marketing and customer relationship management, finance, hospitality, and gaming.

Register here

Alert System Uses Predictive Analytics to Combat Sepsis by Greg Slabodkin via @infomgmt

Called Sentinel, the system from Newark-based collaboration and communications vendor Uniphy Health tracks more than 70 clinical features in real-time.

Sepsis is often missed so this is a great use of predictive analytics.

Hillary Clinton’s ‘Invisible Guiding Hand’ by Shane Goldmacher via @politico

Meet the little-known statistician behind the Democratic nominee's most important strategic decisions.

Wow - the guy needs to write some papers and start teaching all this stuff.

Cyber Security of the Connected Car in the Age of the Internet of Things by David Moss via @datafloq

As innovative and fun as these connected cars are to drive, they are not without flaws (mainly caused by poorly written software), which could compromise drivers' physical and personal safety. Automobile industry insiders are becoming increasingly aware of the security risks found with owning and driving a connected car today. The risks that come with a connected car being hacked can be enormous, hence the importance of cyber security

I can think of nothing worse than having your car hacked and something changing to cause an accident.  It feels like a sci-fi plot but could become reality.

The ultimate promise of artificial intelligence lies in sorting cucumbers by Dave Gershgorn via @qz

It can take months to learn how to properly sort spiky cucumbers. "When I saw Google's AlphaGo," explains Japanese farmer Makoto Koike, "that was the trigger for me to start developing the cucumber sorter with deep learning technology."

A great example of a practical use for AI.

Ending the Data Battle Between Business and IT by Heine Krog Iversen via @infogmgmt

Companies that can integrate self-service BI and still maintain governance, security, and data quality will empower business users to make decisions on-demand, relieving IT from these stakeholder pressures.

I think if you can crack this then both sides will be happy but even better the business will profit from it too.

WEBINAR: Big Data Webinar: Turning Sensor Data into Insight - 21 September 2016

Big Data Webinar: Turning Sensor Data into Insight

Wed., September 21, 2016 | 8:00 am PT/16:00 BST

Want to know how Pentaho can help turn mountains of sensor data into real business value?

Understanding when in-service equipment needs maintenance is a big problem with real business benefits at stake. To identify just the right time to intervene, organizations need the ability to perform advanced, real-time analytics on big data created from integrating massive amounts of sensor data with operational, customer and environmental data.

Join this webinar to learn how:

• Organizations can leverage big data to drive cost savings, new revenue opportunities and improved operational efficiencies
• Leading companies like Hitachi Rail Europe, Caterpillar Marine Asset Intelligence and Halliburton are succeeding with predictive maintenance
• The right tools can make a difference on your bottom line

Speaker: Jonathan Shafer, Sr. Customer Marketing Manager, Pentaho

Wael Elrifai, Director of Enterprise Solutions, Pentaho 

Register here

Marketing & Advertising: Stats and Data Analysis by Diana Beyer via @DataScienceCtrl

With marketing and advertising gaining more space on the internet, big data analytics are playing a prominent role in following the trends on the market and providing users with key statistics.

Data analysis and statistics traditionally play an important role in analyzing the success of companies and brands in the market.

I really enjoyed reading this - it gives a good idea of the scale and habits in these two areas.

Machine learning could help revolutionize cancer diagnosis by Divya Raghuram via @Biotechin.Asia

Data driven medicine used by IBM’s Watson and several other start-ups like InvitroCue are revolutionizing cancer diagnosis.

This is exciting in lots of ways.

WEBINAR: Data governance: Aspirin or vitamin? - 20 September 2016

Web Seminar  Data governance: Aspirin or vitamin?

September 20, 2016 | 2 PM ET/11 AM PT
Hosted by Information Management

Many organizations use data governance as an aspirin. It eases an immediate pain, like a looming regulatory deadline, and makes it temporarily go away. But organizations that use data governance as a vitamin – and practice data governance systemically - gain competitive advantage.

Join Collibra Co-founder and CTO Stan Christiaens as he shares how to make your organization truly healthy by giving the data governance vitamin to everyone. You’ll also hear how data governance has helped Collibra customers sustain data health.

Featured Presenters:

Moderator: Aaron Zornes Chief Research Officer The MDM Institute & Conference Chairman at MDM & Data Governance Summit	Speaker: Stan Christiaens Co-founder & CTO Collibra

Sponsor Content From:

Register here

New Research - We’re In the Middle of a Data Engineering Talent Shortage by @jakestein via @stitch_data

We’ve all become accustomed to hearing about the rising demand for data scientists, but according to the latest research, the real talent crisis lies in data engineering. This report explains where the gaps are and where things are expected to go.

This is very interesting and adds fuel to the facts that certain skills are essential.  There is too much focus on becoming a Data Scientist, but anyone who is technical is probably much better off as a Data Engineer.

Separating the Good from the Bad in the World of Big Data by Karen Peters via @infomgmt

As our world becomes more connected and the amount of data that is available increases, companies must make sure they are developing their own processes for collecting the best data.

Whilst this may read as cleaning data and only taking good data I would like to suggest a caution - you need integrity between your reporting and source so you cannot modify or delete data from your reporting or analytics as it will be incorrect. You have to develop a strategy to handle incorrect data as well as doing more to make sure the data is correct in the first place.   This also reminds me of the problems with reporting from a Data Warehouse and so in this aspect I don't believe that what we do with Big Data will be so different from this standpoint.

WEBINAR: Nielsen Marketing Cloud webinar: online machine learning & predictive personalisation - 20 September 2016

Tuesday, September 20, 2016
11:00 am PT | 2:00 pm ET

Learn how Nielsen Marketing Cloud leverages online machine learning and predictive personalization to drive its success

Advertising starts with knowing individual user profiles, but successful advertising requires knowing audiences. Nielsen Marketing Cloud has long been a pioneer in bringing actionable algorithms and data to bear on the world of online experiences and advertising.

A key component of Nielsen Marketing Cloud’s technical architecture is the Aerospike NoSQL database. Initially, it was used to store user profiles and campaign data; now, it also includes machine learning models, which allow adaptive audience intelligence.

Attend this live webinar and hear from Kevin Lyons, Senior Vice President of Data Science and Digital Technology, and Brent Keator, Vice President of Infrastructure – both from Nielsen Marketing Cloud - as well as Brian Bulkowski, CTO and Co-Founder at Aerospike, as they describe the front-edge architecture and technical choices that have led to Nielsen Marketing Cloud’s success. Topics will include:

• The online machine learning stack
• Predictive personalization
• Technical lessons that can be applied to digital transformation projects across many industries

Meet the speakers:

Kevin Lyons Senior VP Data Science, Digital Technology   Nielsen Marketing	Brent Keator  VP, Infrastructure  Nielsen Marketing Cloud	Brian Bulkowski          CTO and Co-Founder          Aerospike

Register here

SLIDESHOW: 10 Best Practices in Big Data – Granular Access Control via @infomgmt

Granular access control mechanisms are a tool that can be used to reduce data restriction without violating policies. According to the new report from the Cloud Security Alliance  - “100 Best Practices in Big Data Security and Privacy” - the following best practices should be followed while ensuring granular access control.

I particularly like slides 3 and 6

How the Bureau of Labor Statistics Analyzes Data by Brian McDonough via @infomgmt

In an era of big data, the bureau increasingly relies on the Internet, databases and analytics to produce the reports that government officials and business leaders use to gauge the health of the economy and labor force.

Really interesting and good to understand the process they follow.  Please note this is a 2 page article.

Moving from R to Python: The Libraries You Need to Know via @YhatHQ

Here are some libraries you need to know.

Great blog - I've used this a couple of times to help me to remember the right names for the right tool as I had started to confuse myself (not hard specially when writing code)

Everything Must Go: What Happens to Data After a Business Closes? by Richard Stiennon via @infomgmt

When closing the doors for good - whether it’s for strategic, economic or other reasons - auctioning off IP is just one direction that valuable company data can head in.

I guess I had never thought to stop and think about what has happened to my data when a store shuts down for whatever reason.  You trust it to be handed correctly but do you really know what has happened to it and how safe it is?

Data Mining Tip: How to Use High-cardinality Attributes in a Predictive Model by Julie Moeyersoms and David Martens via @kdnuggets

High-cardinality nominal attributes can pose an issue for inclusion in predictive models. There exist a few ways to accomplish this, however, which are put forward here.

This is useful and is a summarisation of what goes on in my head when doing this kind of model.

A Tutorial on the Expectation Maximisation (EM) Algorithm by Elena Sharova via @kdnuggets

This is a short tutorial on the Expectation Maximisation algorithm and how it can be used on estimating parameters for multi-variate data.

I like this tutorial and it is very useful to save and refer back to.

How Convolutional Neural Networks Work by Brandon Rohrer via @kdnuggets

Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.

This is very clear and easy to understand. It is a two page article.

Maybe Blockchain Really Does Have Magical Powers by Eaine Qu via @infomgmt

The cryptographic process of entering and verifying new information automatically reconciles all the copies. As a result, everyone always knows who owns what.

The whole process seems so obvious and maybe it was being done my others already, but as this article says, at least it is out in the open as a valid solution.

Visa Asks Banks to Participate in Blockchain Pilot for Transfers by Jenny Surane via @infomgmt

Visa Europe Collab, the network’s London-based innovation hub, is partnering with BTL Group to explore applications for blockchain technology in financial services.

The march of blockchain carries on.

MyStory: How I transitioned to Data Science after 6 years in Data warehousing? by Balaji SR via @analyticsvidha

Prior to getting initiated to Data Science,  I was working in data intensive Data-warehousing for more than 6 years. In 2013, I had an opportunity to work in a problem wherein we were required to build a model to predict the probability of a customer buying a product as part of transformation initiative. This has widened my horizon.

Inspiration and hints on how to progress into Data Science.

How to Become a (Type A) Data Scientist by Ajit Jaokar,via @kdnuggets

This post outlines the difference between a Type A and Type B data scientist, and prescribes a learning path on becoming a Type A.

I found this really interesting.

21 Must-Know Data Science Interview Questions and Answers via @kdnuggets

KDnuggets Editors bring you the answers to 20 Questions to Detect Fake Data Scientists, including what is regularisation, Data Scientists we admire, model validation, and more.

Something that needs to be read and made sure you have learnt. This is a three page article.

WEBINAR: How Not to Be Wrong About Visualization - 13 September 2016

Overview

Title: How Not to Be Wrong About Visualization

Date: Tuesday, September 13, 2016

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

How Not to Be Wrong About Visualization

Many of the things we take for granted in visualisation are based on nothing other than hearsay and maybe somebody’s aesthetic judgement. Even seemingly obvious things turn out to be wrong when we start to question them.

In this DSC webinar, Robert will walk you through a number of examples that show the limits of what we know about visualisation. As a particular example, he will talk about his recent research on pie charts. Yes, pie charts! How do we read pie charts? Look at any number of books, and they will tell you that we look at the central angle of a slice. That is important, because it means that removing the enter (like in a donut chart) will lead to less accuracy. But it's not true. And worse, this question hasn't been studied since 1926 — ninety years ago! It’s only one of the most used chart types out there, and yet there are lots of things we don’t know about it.

Attend this DSC webinar and find out what else you’re probably wrong about!

Speaker: Robert Kosara, Visual Analytics Researcher -- Tableau 

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

Register here

Yuval Noah Harari on big data, Google and the end of free will via @FT

It's not just external forces that are making data-based decisions that affect our lives. In this essay, the historian Yuval Noah Harari argues that Dataism is diminishing our ability to listen to ourselves and "free will" may be slipping away.

This was incredibly interesting and really makes you think.

SLIDESHOW: 5 Steps to Maximizing the Value of Your Big Data Lake via @infomgmt

Big data lakes have created a lot of change, a lot of angst and most importantly -- a lot of opportunity, according to Avi Kalderon, big data and analytics practice leader at NewVantage Partners. Here are five ways in which you can maximise the value of your data assets.

Interesting comments.

The iBrain is Here by @StevenLevy via @backchnnl

Apple isn't known for its work in AI but that doesn't mean it's sitting out this race. In this Backchannel article, Steven Levy offers an exclusive look at how artificial intelligence and machine learning work at Apple.

This is a great article and very interesting reading. There is always such focus on Google that it was nice to see it from a different company.

VMware New Cloud Plan: Sell Stuff for Rival Clouds by Dina Bass via @infomgmt

The company will announce Cloud Foundation, which combines software for storage, networking and virtualisation into one package, as well as the ability to use that product as a service hosted in IBM's cloud.

I like the sound of this strategy - they need to find their own place in what is a crowded market.

SLIDESHOW: 100 Best Practices in Big Data – Non-Relational Data Stores via @infomgmt

The Cloud Security Alliance has released its report on the 100 Best Practices in Big Data Security and Privacy. As the Alliance notes, “Non-relational data stores such as NoSQL databases typically have very few robust security aspects embedded in them. Solutions to NoSQL injection attacks are not yet completely mature...

I particularly like slide 8 (there are only 11 so don't worry about the 100 title)

Machine Learning Becomes Mainstream: How to Increase Your Competitive Advantage by @_NidhiC and @Ronald_vanLoon via @CIOWaterCooler

First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency. Then came fast data analytics – the application of big data analytics in real-time to help solve issues with customer relations, security, and other challenges before they became problems.

I love this article - it says so may things that are true.  It would be good to get your senior management reading this so they can understand the way things are changing.

Will Artificial Intelligence Defeat Cancer? by @AnnaDomanska_ via @IndLeaders

Cancer, the single most feared diagnosis imaginable, is being tackled by some of the biggest companies in the world using the most formidable weapon: à la artificial intelligence. In fact, the milestones we have hit in past two years in diagnosing rare forms of cancer using clinical data, has stumped the scientific community.

What a great use of AI.

Airline Flight Data Analysis – Part 1 – Data Preparation by Michael Kamprath via @DIYBigData

This is the start of a PySpark data analysis project concerning airline on-time performance. In this post, the usefulness of the Apache Parquet data format is explained as data is loaded and cleaned.

This is a great post and has code and a link to his github containing the code.

Introduction to Local Interpretable Model-Agnostic Explanations (LIME) by Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin via @OReillyMedia

Local interpretable model-agnostic explanations (LIME) is a technique to explain the predictions of any machine learning classifier. Here's an introduction to LIME and how it works.

I found this really interesting. I guess this is a similar to SMART for objectives.

Why You Should Level Up Your Data Wrangling Skills by @ricknotdelgado via @Datafloq

Big data analytics seems to be everywhere these days. Big data analytics has proven to be crucial to discovering new insights for businesses, but to actually get down to using that data is the big challenge many organisations are facing. It’s easy to simply say that companies need to gather data and analyse it, but in practice the process can be complex.​ That’s where data wrangling comes in and it could be crucial for your business.

Definitely gathering the data and making sure of the quality is a big thing that needs to be done, but it is also important to use it and use it correctly which needs certain skills.

How to Secure the Internet of Things (IoT) with Blockchain by @BanafaAhmed via @Datafloq

Blockchain, the “distributed ledger” technology that underpins bitcoin, has emerged as an object of intense interest in the tech industry and beyond. Blockchain technology offers a way of recording any digital interaction in a way that is designed to be secure, transparent, highly resistant to outages, auditable, and efficient. As such, it offers ample ways for new opportunities and to bring security of the Internet of Things to another level.

If you think about it blockchain could be used to record almost anything not just the traditional financial information of today.

The Internet of Things in “Smart City Rio 2016“ by @fmarotob via @Datafloq

The 2016 Rio Olympic Games have just finished, so it is time to look ahead. Many of you have followed the Rio 2016 Olympic Games by TV, Internet or the lucky ones who watched the Games in Rio​. Unfortunately, there were quite a few complaints of those attending the games. While the Rio 2016 Organising Committee applied the Internet of Things, all those sensors could not prevent a swimming pool turning green or empty stadiums. So, how will the next Olympic games look like and will we experience the first "Internet of Olympic Games"?

I can definitely see IOT becoming used more and more in the Olympic Games for measuring and monitoring things.

Machine learning algorithm cheat sheet for Microsoft Azure Machine Learning Studio by Brandon Rohrer

Here's a handy downloadable cheat sheet for choosing a machine learning algorithm for different predictive analytics needs.

Very useful.

Value of Database Skills a Mixed Bag In This Economy by David Weldon via @infomgmt

While reported gains are not large, the good news is that this is the 13th consecutive quarter that the overall market value has increased for the 405 IT certifications tracked by Foote Partners.

It seems clear that certifications are becoming essential.

Big Data, Big Growth, Big Promises by Jennifer Adams via @infomgmt

We expect non relational databases to be the fastest-growing sector within big data management solutions. We forecast that NoSQL will grow 25.0% and Hadoop will grow 32.9% annually over the forecast period.

Proof that relational databases are on the way out in they popularity stakes.

Do You Know Your Data Intelligence Quotient? by Isabelle Guis

More than ever before, we need to think about structured (machine-generated) data — considering content, content governance and an overall picture of what is being produced by employees within an organisation.

Some things to definitely consider and think about.