40% of data science tasks will be automated by 2020 via @helpnetsecurity

More than 40 percent of data science tasks will be automated by 2020, resulting in increased productivity and broader usage of data and analytics by citizen data scientists, according to Gartner, Inc.

Sounds interesting.

The Most Popular Language For Machine Learning and Data Science Is … by Jean Francois Puget via @kdnuggets

When it comes to choosing programming language for Data Analytics projects or job prospects, people have different opinions depending on their career backgrounds and domains they worked in. Here is the analysis of data from indeed.com with respect to choice of programming language for machine learning and data science.

Interesting.  There were definitely languages there that I didn't know.

Tableau Prepares for Future with Data Preparation and Collaboration by David Menninger via @infomgmt

Tableau is adding self-service data preparation through an effort called Project Maestro. Data preparation will be a stand-alone application that enables users to access and prepare data prior to visualisation.

This sounds exciting and I can't wait to see this and how it works out.

Open Source, Vendor Lock-In Are Top of Mind for Execs This Year via David Weldon via @infomgmt

The use of open source software has increased dramatically in the past decade, and this year could be the one in which we see real maturity in the market.

I think open source is rising rapidly and that vendors are increasingly under pressure to provide more and more extra in order to "justify" their price against open source software/operating systems/etc.

Cloud Computing Sees Huge Growth Rates Across All Segments by Bob Violino via @infomgmt

Infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) had the highest growth (53%), followed by hosted private cloud infrastructure services (35%) and enterprise software-as-a-service (SaaS), at 34%.

Seems that it's growing well but maybe it could grow more and faster?

WEBINAR: Turning the IoT into opportunities - 31 January 2017

Web Seminar  Turning the IoT into opportunities

January 31, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Most organizations are now touched by the Internet of Things, and some to a great degree. The IoT represents a golden opportunity for some organizations to obtain more and better data to improve business processes, reduce operational costs, and even improve customer service.

Among the topics to be discussed:

• Which industries and organizations are leading the way at benefitting from the IoT?
• What are the impacts on data management from IoT systems and devices?
• What tools or technologies can help you best turn IoT straw into revenue gold?

Featured Presenters:

Moderator:  Lenny Liebmann Founding Partner Morgan Armstrong

Sponsored By:



Register here

WEBINAR: Improving Advanced Data Prep and Analytics in Spreadsheets - 7 February 2017

Overview

Title: Improving Advanced Data Prep and Analytics in Spreadsheets

Date: Tuesday, February 07, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Improving Advanced Data Prep and Analytics in Spreadsheets

Spreadsheets are still heavily relied upon for advanced data preparation and analysis in many organizations as it is often the most accessible tool available. But, accessibility doesn’t always mean the right tool, especially when it comes to handling multiple spreadsheets, working with larger datasets, or trying to share Excel workbooks. Join us for this DSC webinar as we discuss how self-service data analytics can help you work more efficiently and effectively when working with data in Excel, improve data preparation processes and deliver transparency into your analysis.

Join us for this latest Data Science Central Webinar to learn:

• Prominent use cases where organizations still rely on spreadsheets
• Impacts that spreadsheets have on repeatability of data and analytical tasks    
• Top 3 issues organizations face when considering alternative tools and approaches  
• How self-service can deliver a more robust and seamless process to improve data preparation   

Speakers:
Gene Rinas, Sr. Solutions Engineer -- Alteryx 
Lisa Aguilar, Sr. Product Manager -- Alteryx 

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

  

Register here

Organisational Complexity Greatest Threat to Cybersecurity, Study Finds by David Weldon via @infomgmt

A new global survey by the Ponemon Institute on IT security infrastructure finds that 83 percent of organisations believe they are most at risk for cyberattack because of organisational complexities.

I lot of important areas that need to be handled in order for data to be secure.

WEBINAR: A Natural Language Processing (NLP) Approach to Data Exploration - 31 January 2017

Overview

Title: A Natural Language Processing (NLP) Approach to Data Exploration

Date: Tuesday, January 31, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

A Natural Language Processing (NLP) Approach to Data Exploration

What if you could directly ask questions of your data and the software could respond with a selection, filter, or new visualization? In this DSC webinar, the Tableau Research team explains natural language queries and how they are (already) helping you visualize your data. This presentation includes an introduction to natural language processing (NLP), examples of how NLP is already used in Tableau's geocoding and map search, and a demonstration of some research prototypes that you might see in the not too distant future. Come participate in the discussion and tell us what things you would say to or ask your visualization!

Speakers:
Vidya Setlur, --  Senior Research Scientist -- Tableau 
Melanie Tory,  --  Senior Research Scientist -- Tableau 

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

  

Register here

Genomic Data Sharing Requires Standardization of Lab, Clinical Info by Greg Slabodkin via @infomgmt

To address a number of challenges, the group calls for the standardisation of laboratory and clinical information to enable data compatibility as well as interoperability between systems.

Like always standards are always needed for the format of the the data, the structure of the messages that shre the data, and how the data is treated.

World’s largest hedge fund to replace managers with artificial intelligence by Olivia Solon via @guardian

Bridgewater Associates, one of the world's largest hedge fund companies, assigned a team of engineers to a project to automate decision making to save time and eliminate human emotional volatility.

Interesting.  I can see that it would be unemotional - BUT it relies on the lack of emotion on the design of the solution and should probably have something to be a safety net.

Pharma adopts data-science culture in move toward AI by Marc Iskowitzvia @MMMnews

"We're hiring engineers, quantitative pharmacologists, economists, mathematicians, and machine-learning experts. It's with diversity in mind—diversity not only in skill set but also in experience," says Sandy Allerheiligen, Merck VP of predictive and economic modelling. It's "technical possibility meets business opportunity," says Hilary Mason.

Interesting to focus on an area I am not familiar with to see how they are focusing.

What Is Fog Computing? And Why It Matters In Our IoT And Big Data World by Vincent Stokes via @datafloq

Fog computing is a disruptive technology that adds another level of complexity to Cloud computing, but also offers greater efficiency and lower costs.

There are some great links to other articles from this one to help build up the picture of what it is a why it is needed.

How a machine learns prejudice by Jesse Emspak via @sciam

Artificial intelligence picks up bias from human creators—not from hard, cold logic.

This is interesting and shows how key the data you use to train from is.

Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017 by Matthew Mayo via @kdnuggets

As 2016 comes to a close and we prepare for a new year, check out the final instalment in our "Main Developments in 2016 and Key Trends in 2017" series, where experts weigh in with their opinions.

Some exciting developments and trends in this article - can't wait to see how it all works out.

5 Machine Learning Projects You Can No Longer Overlook by Matthew Mayo via @kdnuggets

There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.

I love this article and it has great diagrams and examples - if nothing else you can use it to point you in certain directions as part of your learning or playing with Machine Learning.

SLIDESHOW: 10 Top Ways IT Pros Are Boosting Their Careers by David Tsai @informgmt

What do IT pros hope to accomplish in their careers in 2017? To find out, job site Spiceworks polled more than 1,000 of members of the Spiceworks Community for their IT career resolutions.

I would suggest if you work in IT then you are already committed to continuous learning - lets face it nothing stands still.

WEBINAR: Optimal Data Analytics Architecture - 24 January 2017

Overview

Title: Optimal Data Analytics Architecture

Date: Tuesday, January 24, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Optimal Data Analytics Architecture

An explosion of Big Data is rapidly changing the IT landscape. While Big Data generates vast opportunities for new sources of revenue, customer insights and operational efficiencies, it also creates new challenges for existing data infrastructure. To keep up with this explosion and capitalise on new data-driven business opportunities, enterprises must select a data analytics architecture that fits the specific needs of their business and the structure of their data. 

Join us for our latest Data Science Central Webinar with guest speakers Mike Gualtieri, VP and Principal Analyst at Forrester, and Steve Sarsfield, product evangelist and spokesperson for HPE Vertica, while they discuss a newly commissioned study on enterprises that select the correct mix of analytical engines and the best database for each job.  They will review:

• How a diversity of analytical engines can drive greater ROI
• Which databases have distinct capabilities and “sweet spots”
• Why no one database can do it all 

Speakers:

Mike Gualtieri --VP Principal Analyst -- Forrester  
Steve Sarsfield -- Product Evangelist and Spokesperson -- HPE Vertica   

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

Six Secrets to Landing a Job in Data Science by Sham Mustafa via @Data_Informed

Here’s how you can stand out in a pool of highly qualified applicants.

Interesting and well worth reading.

SLIDESHOW: 6 Trends to Expect for Big Data In 2017 by David Weldon via @infomgmt

The focus on big data in 2017 will be on the value of that data, according to John Schroeder, executive chairman and founder of MapR Technologies, Inc. Schroeder offers his predictions on the 6 trends in big data we can expect.

Interesting.

SLIDESHOW: Mastering the Five Stages of Analytics Maturity by Venkat Viswanathan via @infomgmt

Many organisations struggle with how to best use analytics to drive business decisions. The key is to understand the five stages of analytics maturity and how the organisation matches to each.

I love that this includes tips on how to get to the next level. Look at this and try and work out where you organisation is in this hierarchy.

Business Intelligence – From Chaos to Value by Eric Haahr via @infomgmt

BI environments draw on a strong legacy of version control, change management and a well-defined path from development to production that is missing or at least very rudimentary in BI environments.

I can agree with him - there needs to be some discipline when using BI - it is not a tool for a free for all.

The world’s best Go player says he still has “one last move” to defeat Google’s AlphaGo AI by @pingroma via @qz

Over the past few days, Google’s Deepmind machine-learning team secretively put its AlphaGo artificial intelligence system onto two Chinese online board-game platforms to test its skill in fast-paced games against several of the world’s best Go players. It won every game it played. Go has become the province of AI, and DeepMind further proves that GANs are an extremely promising approach.

Great progress by the Google Team.

Four Ways Big Data Will Make You Happy in 2017 by @VanRijmenam via @Datafloq

Let’s start the year on a positive note! We are in the middle of a data revolution, where data sources will be connected with each other and as such provide valuable insights. These insights can be used by organisations to reduce costs or increase their revenue, but it can also help in making consumers happier, something that in these times of uncertainty can be very welcome.

Interesting thoughts - maybe it will start you thinking about some possibilities after the rest over the New Year?

CEOs Reveal Cyber Naiveté as Incidents Rise and Losses Mount by David Weldon via @infomgmt

While CEOs remain confident that their cyber strategies are well equipped to handle the risks facing their company networks, there is a disconnect between their perception and reality.

This is definitely an area that needs thought, care and concentration.

Kickstart My Year: 7 trends in Insights & Data for 2017 by Ron Tolido via @infomgmt

It’s a motley crew of trends here, as you'll see, with major themes such as automation, enterprise-scalability, cloudification and (surprise) the rise of Machine Intelligence making it to the top of the pile

Interesting list :-)

The Great AI Awakening by Gideon Lewis-Kraus via @nytimes

In "The Great AI Awakening," Gideon Lewis-Kraus describes how Google used artificial intelligence to transform Google Translate and the decision to reorganise Google around AI.

If you missed it, check it out. It's a great read.

Microsoft dataset aims to help researchers create tools to answer questions as well as people via @MSMarcoAI

Microsoft has released a set of 100,000 questions and answers that artificial intelligence researchers can use in their quest to create systems that can read and answer questions as well as a human.

There is a link in the article that you can use if you have an interest and want to download the dataset.

Practical Deep Learning For Coders by Jeremy Howard via @fastdotai

Free, online course that's designed to take anyone with some coding experience to the point they can apply deep learning to create state of the art models in computer vision, natural language, and recommendation systems.

Something you really must add to your things to do this year.

Scientists are frantically copying U.S. climate data, fearing it might vanish under Trump by @brady_dennis via @washingtonpost

Alarmed that decades of crucial climate measurements could vanish under a hostile Trump administration, scientists have begun a feverish attempt to copy government data onto independent servers in hopes of safeguarding it from any political interference.

This is so sad - I hope that everything works out ok.

IBM and The World of Watson by David Menninger via @infomgmt

The core of Watson is a set of cognitive computing capabilities. These cognitive capabilities have been packaged into a set of cloud-based services for creating cognitive applications.

Nice to read this and have a little clarity and confirmation as to what exactly Watson is and provides.

4 Reasons Your Machine Learning Model is Wrong (and How to Fix It) by Bilal Mahmood via @kdnuggets

This post presents some common scenarios where a seemingly good machine learning model may still be wrong, along with a discussion of how how to evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall.

Incredibly useful to remind yourself of all the things you know but have forgotten in your frustration to fix it.

First Deep Learning for coders MOOC launched by @JeremyPHoward by Gregory Piatetsky via @kdnuggets

Leading Data Scientist and entrepreneur Jeremy Howard launches a free Deep Learning course that shows end-to-end how to get state of the art results, including a top place in a Kaggle competition.

Definitely worth signing up for if that kind of thing interests you.

Data Engineers vs. Data Scientists: The Difference According to LinkedIn Data by @jakestein via @stitch_data

In 2012, Harvard Business Review named data scientist the "sexiest job of the 21st century." In 2017, it is expected for demand to continue for data scientists, but the talent gap will be framed in terms of data engineers (more than data scientists).

I found this really interesting. From my own point of view I'm in the middle of the two roles with certain skills missing which means I can use this as a indicator of what skills I need to gain in order to go in either direction - something that you can do too.

Machine Learning Crash Courseby By Daniel Geng and Shannon Shih via ML@B @BerkeleyMl

A visual and easy to follow 2 part course in Machine Learning from Berkley.

Part 1 - Introduction, Regression/Classification, Cost Functions, and Gradient Descent

Part 2 - Perceptrons, Logistic Regression, and SVMs

These are brilliant and very useful if you want to understand the basics without spending large amounts of time.

I would advise following them too by clicking on the 3 horizontal bars at the LH top of the screen.