A curated list of data tools to help you make better product decisions

Python is one of the most popular open source (free) languages for working with the large and complicated datasets needed for Big Data. It has become very popular in recent years because it is both flexible and relatively easy to learn.


Like Python, R is hugely popular and supported by a large and helpful community. Where Python excels in simplicity and ease of use, R stands out for its raw number crunching power.


Although SQL is not designed for the task of handling messy, unstructured datasets of the type which Big Data often involves, there is still a need for structured, quantified data analytics in many organizations.


As the name suggests MATLAB is designed for working with matrixes which makes it very good for statistical modelling and algorithm creation. It isn’t open source so doesn’t have the volume of free community-driven support but this is alleviated somewhat by its widespread use in academia.


The SAS language is the programming language behind the SAS (Statistical Analysis System) analytics platform, which has been used for statistical modelling since the 1960s and is still popular today after many years of updates and refinements.


Julia is a relative newcomer, having existed only for a few years, however it is quickly gaining popularity with data scientists praising both its flexibility and ease of use.