The maths is more important in data science

Much of what was written about data science in 2010 focused on the marvels of modern technology that allow for the analysis of massive stores of data. As these technologies become ubiquitous, more concern will be on the methods of analysis and presentation of findings.

I’m sure that when it comes to data science it is the maths (the understanding of how to look for outcomes and the ability to interpret the results) rather than the technology (what programming languages or software stack you are using) that really counts.  The value is in the maths – there a a lot fewer people know the maths theory than know the coding practice.

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