Data are becoming the new raw material of business
The Economist


Here’s How to Survive the Rise of A.I. – Become a Data Facilitator

Front office jobs at investment banks are increasingly being taken over by intelligent machines. Many current front office employees are worried about being displaced by artificial intelligence, and their fears are not unfounded. Huy Nguyen Trieu, former head of macro structuring at Citigroup, has a positive message for traders who risk being replaced by automation: become a data facilitator.

 

Huy Nguyen Trieu left Citi in 2016. After 13 years in financial engineering at SocGen and RBS, before becoming an Managing Director and head of macro structuring at Citi, he has shifted his focus to acting as a thought leader in the fintech space. Currently a fintech fellow at London’s Imperial College and mentor at fintech accelerator, Level 39, Nguyen Trieu is both fintech guru and entrepreneur. And his current focus is centered on the issue of long term employability in investment banks.

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Data Science in 30 Minutes: Why Big Data Needs Thick Data with Tricia Wang


This FREE webinar took place on June 26th, 2018. Sign up below for the free video!

Tricia Wang, co-founder of SuddenCompass joined The Data Incubator for the June 2018 episode of our free online webinar series, Data Science in 30 minutes: Why Big Data Needs Thick Data.

Why do so many companies make bad decisions, even with access to unprecedented amounts of data? Tricia has the answer: companies are implementing “big data” without what she calls the secret, missing ingredient, “thick data” – precious, unquantifiable insights from actual people – to make the right business decisions and thrive in the unknown. Tricia shared stories and lessons from how her company, Sudden Compass, advises and teaches organizations to unlock insights from big data and turn their big data projects from optimizing the bottom-line to driving growth.
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GPU Cloud Computing Services Compared: AWS, Google Cloud, IBM Nimbix/Power AI, and Crestle

This technical article was written for The Data Incubator by Tim Pollio, a Fellow of our 2017 Fall cohort in Washington, DC who joined The Data Incubator team as one of our resident Data Scientist instructors.

At The Data Incubator, a data science training and placement company, we’re excited about the potential for neural networks and deep learning to transform AI and Big Data. Of course, to practically run deep learning, normal CPUs won’t suffice — you’ll need GPUs. GPUs can dramatically increase the speed of deep learning algorithms, so it’s no surprise that they’re becoming increasingly popular and accessible. Amazon, Google, and IBM all offer GPU enabled options with their cloud computing services, and newer companies like Crestle provide additional options.

We tried four different services — Amazon Web Services, Google Cloud Platform, Nimbix/PowerAI, and Crestle — to find the options with the best performance, price, and convenience. Each service was tested using the same task: 1000 training steps on a tensorflow neural network designed for text prediction. The code for this benchmark can be found here.

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Ranking Popular Deep Learning Libraries for Data Science

Gold Blog
At The Data Incubator, we pride ourselves on having the most up to date data science curriculum available. Much of our curriculum is based on feedback from corporate and government partners about the technologies they are using and learning. In addition to their feedback we wanted to develop a data-driven approach for determining what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. Here are the results.
 

The Rankings

Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average = score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average. See below for methods.


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MIT’s $75,000 Big Data finishing school (and its many rivals)

New courses target the need for managers and techies to talk to each other as data proliferate

For most students, a top degree in a field such as computer science or maths ought to be a passport to a career perfectly in tune with the relentless digitisation of work.

For the 30 graduates taking up a new one-year course at MIT’s Sloan School of Management in September, it will be only the prelude to a spell in a Big Data finishing school.

This first cohort of students will pay $75,000 in tuition fees for their Master of Business Analytics degree, with classes ranging from “Data mining: Finding the Data and Models that Create Value” to “Applied Probability”.

They will be calculating that the qualification will sprinkle their CVs with extra stardust, attracting elite employers that are trying to find meaning in the increasing volumes of data that businesses are generating. Continue reading