Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. I recently gave a talk where I argued the importance of asking Why, touching on three different topics: stakeholder motives, cause-and-effect relationships, and finding a sense of purpose. A video of the talk is available below. Unfortunately, the videographer mostly focused on me pacing rather than on the screen, but you can check out the slides here (note that you need to use both the left/right and up/down arrows to see all the slides).

This article is written with much help by David Blei. It is extracted from a discussion paper on “Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing”. [link]

One of the most significant characteristics of the evolving digital age is the convergence of technologies. That includes information management (databases), data collection (big data), data storage (cloud), data applications (analytics), knowledge discovery (data science), algorithms (machine learning), transparency (open data), computation (distributed computing: e.g., Hadoop), sensors (internet of things: IoT), and API services (microservices, containerization). One more dimension in this pantheon of tech, which is the most important, is the human dimension. We see the human interaction with technology explicitly among the latest developments in digital marketing, behavioral analytics, user experience, customer experience, design thinking, cognitive computing, social analytics, and (last, but not least) citizen science.

What is this Isotonic Regression

Collaborative filtering is commonly used in recommender systems. The idea is if you have a large set of item-user preferences, you use collaborative filtering techniques to predict missing item-user preferences. For example, you have the purchase history of all users on an eCommerce website. You use collaborative filtering to recommend which products a user might purchase next. The key assumption here is people that agreed in the past (purchased the same products) will agree in the future.

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“The Fourth Industrial Revolution is being driven by a staggering range of new technologies that are blurring the boundaries between people, the internet and the physical world. It’s a convergence of the digital, physical and biological spheres.” –Fulvia Montresor, Director of the World Economic Forum, 2016

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