Perhaps your first question is: WTF ??? Data what ???

Who should care about this kind of geek-grade bullshit ?

Well, the simple thruth is: if you ever wondered why Amazons knows what you like or why Google knows the best web pages relating to your search terms, you SHOULD have a look into the marvels of analysing data with the power of algorithms and applied statistics.

So if you don’t belong to those faint of heart that rather have themselves subjected to rules that they don’t understand, you should have a look at the underlying principles and tools that drive big parts of the digitial revolution – for this revolution has created amounts of data never seen before which just wait to be exploited in great new, unexpected and sometimes also frightening ways.

And data analytics / data science is just that: a set of tools to derive insight from all this data.

And as plunging into this subject may  seem daunting to anyone who didn’t voluntarily attend computer or statistic classes in high school (…I think I mentioned some of the stuff is geek grade..), I wanted to share the link to the two best resources that allowed me, as humble human, to get a first insight and understanding into the subject.

The first is a very straight-forward, plain English overview of the major algorithms used in this field. It already allows to gain a good idea on which kind of problems can be tacked with these tools…. and how:

http://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/

And the second resource is a book that goes much more in depth. A bit of math understanding doesn’t hurt, neither. But besides a very good overview of the math and algorithms (going from zero to more detail without forcing you to work half-time on your math degree), it also dives into the understanding of the underlying concepts:

Data Mining for Busines (Fawcett & Provost)

Plus these concepts are explained with the best visuals I have seen so far in the field. And as the saying goes: a picture says more than thousand words…. which incidently paves the way to an adjacent field of data analysis which is just as interesting and pretty complementary to data analysis: data visualization – but that should be the subject of a seperate post….

 

 

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