6 best practices for using data to set yearly targets by Kayla Matthews via @infomgmt

Raw, unfiltered data can be a goldmine for businesses looking to expand their knowledge of the average consumer. However, the data has to be legible first, and this practice takes work.

I agree with most of the points in this article. I would like to point out that making sure that the data you use is as accurate as possible is a complete MUST. You should only make business decisions on data that is accurate and can be relied upon.

I would implore you to think outside of the box. You might be surprised at the uses of some data and what it can tell you. Just make sure that you use good test data when you try these things out so you can really make sure you know what is happening.

Thinking Differently About A.I. by by Daily Wisdom via @Medium

AI has proven to be good at solving well-defined problems but not as good at creative problem solving. That may be because we aren’t asking the right questions.

This article is so very right - the question almost needs as much care as many of the other aspects of what you are trying to achieve. A great read and if you use Medium they are well worth a follow.

How the BBC Visual and Data Journalism team works with graphics in R by BBC Visual and Data Journalism via @Medium

This is an interesting look at how the BBC uses R’s ggplot2 package to create production-ready charts.

Who knew this was behind what we see on the screen. Interesting read.

Alibaba's 'City Brain' is slashing congestion in its hometown by Michelle Toh and Leonie Erasmus via @CNNI

Alibaba’s City Brain is easing congestion in its hometown.

AI definitely seems to be the way forward with traffic control systems and they can only get better over time which is really exciting.

Understand TensorFlow by mimicking its API from scratch by @elmd_ via @Medium

This great tutorial mimics TensorFlow’s API and implements the core building blocks from scratch, giving you an under-the-hood look at how TensorFlow’s deep learning libraries work.

I love this - it is very clear and easy to understand. You really need to bookmark this if you want to understand or learn about TensorFlow.

How Silicon Valley Puts the ‘Con’ in Consent by/via @nytopinion

If no one reads the terms and conditions, how can they continue to be the legal backbone of the internet?

A very interesting take on all the terms and conditions that we know we should read as much as they all assume you won't.

Top 10 Features to Look for in Automated Machine Learning by Colin Priest via @DataRobot

Following best practices when building machine learning models is a time-consuming yet important process. There are so many things to do ranging from: preparing the data, selecting and training algorithms, understanding how the algorithm is making decisions, all the way down to deploying models to production.

Great article by Colin that I think is worth a bookmark so that you can refer back to it.

Why the ranks of citizen data scientists will grow and thrive by Mike Flannagan via @infomgmt

The citizen data scientist is anyone who works with data and predictive analytics, but isn’t a data scientist or an expert in stats and analytics.

I agree with the principal but not his definition of citizen data scientist. I view them as someone who is not employed by the organisation and is not acting for them as a paid consultant of some sort. So for example someone who entered a competition in Kaggle or somewhere similar.

How Facebook Scales Machine Learning by Jamal Robinson via @Medium

Here’s a look at the software and hardware decisions Facebook made in scaling the company’s AI/ML infrastructure.

I love the level of detail in this article which gives a great insight into both Facebook but also what would be necessary to reproduce the kinds of results that they achieve.

WEBINAR: Ask Data: Simplifying Analytics with Natural Language - 14 February 2019

Ask Data: Simplifying Analytics with Natural Language Join us for this latest DSC Webinar on February 14th, 2019 What if you could directly ask questions of your data? Ask Data, Tableau’s new natural language capability, allows people to get insights by simply conversing with their data. In this latest Data Science Central webinar, members of Tableau’s Ask Data team will demonstrate how they are lowering the barrier to analytics and leveraging Natural Language Processing (NLP) as a tool for visual analysis. Looking to quickly make smarter, data-informed decisions and empower others to do the same? Watch this detailed overview of Ask Data’s capabilities to learn more. Speakers: Samantha Kwok, Senior Manager, Engineering -- Tableau Ruhaab Markas, Senior Product Manager -- Tableau Hosted by: Bill Vorhies, Editorial Director -- Data Science Central   Title:  Ask Data: Simplifying Analytics with Natural Language Date:  Thursday, February 14th, 2019 Time:  9:00 AM - 10:00 AM PST

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How to banish silos, consolidate data and avoid errors in the process by Fredrik Forslund via @infomgmt

Data silos tend to arise naturally in large businesses because each organisational unit has different goals, priorities and responsibilities, as well as different technical systems or platforms in place.

One of the keys to reducing silos is to have a strong data management team but you also need a strong team of data stewards too.  Something I have found is not only do you have silo's of data, but in those silos you have the same names data fields either in different formats or have a completely different name. You need to sort that out before you can think about getting rid of the silo.

Finland's grand AI experiment by Janosch Delcker via @POLITICOPro

Jaana Partanen is not your typical AI programming geek. Until a year ago, the 59-year-old Finnish dentist had never heard of machine learning. But now she is part of a government experiment—teach 1% of Finland's population (about 55,000 people) the basic concepts of AI and see what happens.

I love this and wonder if more countries should do this and not jut for AI - why no machine language too?

WEBINAR: Creating Business Applications With R & Python - 12 February 2019

Creating Business Applications With R & Python Join us for the latest DSC Webinar on February 12th, 2019 Across industries, data scientists are creating powerful models and analytics to solve urgent business problems. However, in far too many cases, these analytics never reach their intended business users. The result is wasted time and effort, as well as a failure to achieve the fundamental goal of transforming data and analytics into solutions. Please join this latest Data Science Central webinar to see how data science teams can stop this trend and start putting analytics into action. With FICO® Xpress Insight, it's easy to take any advanced analytic asset (such as an R or Python script) and turn it into a fully functioning application for business users. We'll demonstrate some key features, including: An environment that fosters collaboration between data scientists and business users during model creation A robust interface for rapidly deploying validated models into business user-friendly applications Enablement tools for business users to run models, perform simulations, compare scenarios and visualize outcomes Data scientists can finally stop seeing their efforts go to waste and start empowering business users with the predictive and prescriptive analytics capable of transforming businesses–join us to learn more! Speakers: Bill Doyle, VP of Decision Management Solutions -- FICO Libin Varghese, Principal Sales Consultant, Decision Management Solutions -- FICO Hosted by: Bill Vorhies, Editorial Director -- Data Science Central   Title:  Creating Business Applications With R & Python Date:  Tuesday, February 12th, 2019 Time:  9 AM - 10 AM PST

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How AI Has Revolutionised Online Learning by Mark Palmer via @Datafloq

Online learning has increased over the past couple of years and AI has become a driver of improvements.

There are many online learning platforms that can offer free or paid for but also casual or formal subjects too. Definitely the way to go.

Can crowdsourcing mitigate a dearth of data scientists for banks? by Penny Crosman via @infomgmt

Data scientists from NASA, Google and hundreds of other places are working for financial firms in their spare time. Is this a good idea?

I would add to this that Data Scientists and those studying to become one also enter competitions on the Kaggle platform which a) might win them a prize but b) also fixes a need set by the organisation who has submitted it.