Data is now creating opportunities for business growth and profit like never before. In the last decade, the emergence of advanced data technologies and superior analytics tools has made it possible for business operators to reap numerous benefits from their data assets, yet for most they’ve only just scratched the surface of data’s potential. Data Science is allowing enterprise’s to successfully leverage that potential like never before.

Temperament has been a key issue in the 2016 presidential election between Hillary Clinton and Donald Trump, and an issue highlighted in the series of three debates that concluded this week. Quantifying “temperament” isn’t an easy task, but The Economist used the Microsoft Emotion API to chart the anger, contempt, sadness and surprised expressed in the faces of the candidates during key sequences of the debates.

Google's open source framework for machine learning and neural networks is fast and flexible, rich in models, and easy to run on CPUs or GPUs

What makes Google Google? Arguably it is machine intelligence, along with a vast sea of data to apply it to. While you may never have as much data to process as Google does, you can use the very same machine learning and neural network library as Google. That library, TensorFlow, was developed by the Google Brain team over the past several years and released to open source in November 2015.

How often do you get advice that is directly relevant to what you need right now? Probably never.

As a startup founder, I read a lot of articles about building and growing a business. A lot of these articles have great advice, but often try to reach a broad audience, and therefore end up glossing over the details that can really matter.

First off, let me state what I think is NOT the the problem: the fact that data scientists spend 80% of their time with data preparation. That is their JOB! If you are not good at data preparation, you are NOT a good data scientist. It is not a janitor problem as Steve Lohr provoked. The validity of any analysis is resting almost completely on the preparation. The algorithm you end up using is close to irrelevant.Complaining about data preparation is the same as being a farmer and complaining about having to do anything but harvesting and please have somebody else deal with the pesky watering, fertilizing, weeding, etc.

Finally released from private alpha, Google Cloud Machine Learning lets all comers build software powered by trained algorithms.

Google unveiled today machine learning-related additions to its cloud platform, both to enrich its cloud-based offerings and to offer expanded toolsets for businesses to develop their own machine learning-powered products.

The MOOC market (Massive Open Online Courses) has exploded. Last week Coursera landed another $20M in funding, bringing their total investment to $63M (even more than edX's original $60M funding by MIT and Harvard). Why all the investment? Because this market opportunity is massive and building these online courses is expensive.

Insight > Adaptation > Action

Critical thinking may be needed to turn insightful findings into actions. For example, a lack of parking on campus may be best solved by working with the city council to improve public transport options. Or, to give a positive example, if students say that they love the environment and the campus, the action could be to use this finding in the marketing material to attract students who care about this.