Tabular: a schema-driven probabilistic programming language
Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
| Hi-index | 0.00 |
Analytics enables businesses to increase the efficiency of their activities and ultimately increase their profitability. As a result, it is one of the fastest growing segments of the database industry. There are two usages of the word analytics. The first refers to a set of algorithms and technologies, inspired by data mining, computational statistics, and machine learning, for supporting statistical inference and prediction. The second is equally important: analytical thinking. Analytical thinking is a structured approach to reasoning and decision making based on facts and data. Most of the recent work in the database community has focused on the first, the algorithmic and systems problems. The people behind these advances comprise a new generation of data scientists who have either the mathematical skills to develop advanced statistical models, or the computer skills to develop or implement scalable systems for processing large, complex datasets. The second aspect of analytics -- supporting the analytical thinker -- although equally important and challenging, has received much less attention. In this talk, I will describe recent advances in in making both forms of analytics accessible to a broader range of people, who I call data enthusiasts. A data enthusiast is an educated person who believes that data can be used to answer a question or solve a problem. These people are not mathematicians or programmers, and only know a bit of statistics. I'll review recent work on building easy-to-use, yet powerful, visual interfaces for working with data; and the analytical database technology needed to support these interfaces.