Phil Factor on three "sanity checks" that any data scientist must perform in order to prevent businesses from interpreting data analysis errors, or fraudulent activity, as real trends.  Read more...

It is sensible to check the performance of different solutions to data analysis in 'lab' conditions. Measurement by instrumentation makes it easier to develop systems that are efficient.  Read more...

Phil Factor on why Data Science is about much more than just clever visualizations and big data.  Read more...

There is no better way of understanding new data processing, retrieval, analysis or visualising techniques than actually trying things out. In order to do this, it is best to use a server that acts as data science lab, with all the basic tools and sample data in place. Buck Woody discusses his system, and the configuration he chose.  Read more...