Big Data & Digital Marketing

When a successful Data Science project is well presented, it usually looks very simple. It reminds me of some of the proofs in Calculus or Physics that I studied when an undergrad.

In fact, they just *look* simple, and they do so because someone has done an incredibly hard and difficult job before hand.

In Data Science projects, the hidden job is usually related to data: looking for data, cleaning the data, joining the data, realising you are missing some data and iterate.

There is a lot of content in this (long) article published by the Harvard Magazine.

Here are my main 3 takeaways:

1. "The Big Data revolution lies in improved statistical and computational methods, not in the exponential growth of storage or even computational capacity" by Gary King

2. Big Data isn't everything: "We had petabytes of data and yet we were building models that were fundamentally flawed, because we didn't have insights about what was happening" by Nathan Eagle

3. "No matter how much data exists, researchers still need to ask the right questions to create a hypothesis, design a test, and use the data to determine whether the hypothesis is true." by Nathan Eagle