What is hardcore data science – in practice?


For example, for personalized recommendations, we have been working with learning to rank methods that learn individual rankings over item sets. Figure 1: Typical data science workflow, starting with raw data that is turned into features and fed into learning algorithms, resulting in a model that is applied on future data. This means that this pipeline is iterated and improved many times, trying out different features, different forms of preprocessing, different learning methods, or maybe even going back to the source and trying to add more data sources. Probably the main difference between production systems and data science systems is that production systems are real-time systems that are continuously running.

Robot CEO: Your next boss could run on code


At the time it was predicted that it might take another hundred years until computers would beat top human players at the boardgame Go. But a few days ago, Google's AlphaGo beat the world's champion Go player in a five-game series. Business books and management consultants commonly list six functions that a CEO is responsible for: determine the strategic direction, allocate resources, build the culture, oversee and deliver the company's performance, be the face of the company, and juggle with everyday compromises. Human managerial decisions will switch focus to the "why" rather than the "how" as data-driven decisions slowly creep from scheduling to resource allocation to performance measurement and reporting, and finally to daily management tasks.