### Models

When we talk about big data analytics, the usual topic of discussion would normally revolve around Hadoop, Pig, MapReduce, NoSQL - platforms basically. Granted - those technologies are the enablers of big data, for without which - one can't store big data in their data warehouse.

For now though, I'd like to focus on the math.

The models to be precise. For without which, one can't derive any usable use cases anyhow with all those data that you have (you might be able to get those low hanging fruits, but over the years - you will have to put on your math hat as well).

Below is a link for a course in Coursera which I find to be perfect for beginners like myself who's rather new with statistics and the different models that can be used to solve different questions. The reason I'm mentioning this course in particular is because I like the pace at which it is going and how the presenter is able to articulate complex ideas in simple words.

For now though, I'd like to focus on the math.

The models to be precise. For without which, one can't derive any usable use cases anyhow with all those data that you have (you might be able to get those low hanging fruits, but over the years - you will have to put on your math hat as well).

Below is a link for a course in Coursera which I find to be perfect for beginners like myself who's rather new with statistics and the different models that can be used to solve different questions. The reason I'm mentioning this course in particular is because I like the pace at which it is going and how the presenter is able to articulate complex ideas in simple words.

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