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Data Science Weekly Newsletter
December 29, 2016
Damn, We Wish We’d Written These 11 Stories
Every year, Bloomberg News publishes a “Jealousy List” — the stories Bloomberg staffers wish they had published. It’s a delightful and endearing concept, and one that makes us incredibly — what’s that word? — envious. So, here are the stories other folks published in 2016 that made the FiveThirtyEight staff super duper jelly...
Deep Learning Enables You to Hide Screen when Your Boss is Approaching
I feel awkward when my boss is creeping behind. Of course, I can switch the screen in a hurry, but such behavior is suspicious, and sometimes I don’t notice him. So, in order to switch the screen without being suspected, I create a system that automatically recognizes that he is approaching to me and hides the screen. Specifically, Keras is used to implement neural network for learning his face, a web camera is used to recognize that he is approaching, and switching the screen...
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Why you should master R (even if it might eventually become obsolete)
Although R is very popular today (and increasing in popularity over the last few years) another language might become more popular for data science. We don’t know. What I do know, is that if you want to learn data science today, you need to select a tool and master the basics. With that in mind, I want to clarify a few points to make sure that you understand exactly what you need to do as you get started learning (and mastering) data science...
Can Convolutional Neural Networks Crack Sudoku Puzzles?
Sudoku is a popular number puzzle that requires you to fill blanks in a 9X9 grid with digits so that each column, each row, and each of the nine 3×3 subgrids contains all of the digits from 1 to 9. There have been various approaches to that, including computational ones. In this pilot project, we show that convolutional neural networks have the potential to crack Sukoku puzzles without any other rule-based post-processing...
Taste Testing Beer Brewed With Artificial Intelligence
With a thrilling blend of customer feedback, digital interfaces, algorithms, and nuanced brewing, a British company has concocted four beers using artificial intelligence, and they are now available in the United Kingdom. The company, IntelligentX, created a survey system that uses Facebook Messenger chat bots to gather feedback from consumers on their taste preferences for beer. The data is fed into an algorithm to develop a beer recipe that is passed on to actual humans who brew, bottle, and share the beers...
Towards a new kind of [football] analytics
I have been involved in football analytics for four years and doing it for a living since 2014. It has been a wonderful adventure, but there is no denying that the public side of the field has stalled. But this is not really a “crisis of analytics” piece or an indictment of the community. Instead, I want to point out one critical barrier to further advancement and plot a course around it. In short, I want to argue for a more theoretical, concept-driven approach to football analysis, which is in my opinion overdue...
Principal Data Scientist - Comcast - Philadelphia, PA
Are you passionate about the future of data analytics and desire to help shape it? If so, Comcast is looking for a Data Scientist with deep quantitative and statistical skills to join a Data Science team which consists of world class business minds and scientists tasked with driving transformational change through evidence-based decision making.
Ideal candidate for the role would possess advanced predictive machine learning skills, have expert level background in Spark, Scala, Python, R or SAS with strong SQL skills. Excellent written, verbal and presentation skills are needed along with top-tier communication and listening skills. Preference given for candidates with experience in digital analytics - site optimization, marketing-mix, digital channel attribution, end-to-end digital campaign experience. Experience collecting and integrating of social, mobile and site qualitative and quantitative data to highlight important themes in consumer engagement, customer journeys across desktop/mobile site experiences and mobile app...
Training & Resources
From Python to NumPy
I think there is room for a different approach concentrating on the migration from Python to NumPy through vectorization. There are a lot of techniques that you don't find in books and such techniques are mostly learned through experience. The goal of this book is to explain some of these techniques and to provide an opportunity for making this experience in the process...
Improved generator objectives for GANs
We present a framework to understand GAN training as alternating density ratio estimation and approximate divergence minimization. This provides an interpretation for the mismatched GAN generator and discriminator objectives often used in practice, and explains the problem of poor sample diversity...
How Bayesian inference works
Although it is sometimes described with reverence, Bayesian inference isn’t magic or mystical. And even though the math under the hood can get dense, the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer...