I know is very interesting for us when we discover new things and not so much when we ran into the same old places everyday. Even so, discovering the magnitude of all the small thing that we encounter daily might come as a shock for many, and because we were curious, we started investigating and then came up with this infographic. We know that more than 250 million cars sounds a lot, and it actually means that there is 78% of a car for every citizen (America’s population:323846000), but check out this other cool stuff that we found!
If you want more, you can visit TheWebMiner GEO.
Data science can be a art, a art of identifying patterns and decisions before of even being taken, all this, with impressive accuracy. For our blog’s comeback I thought I should cover more the literary part of this science-art-craft and talk about some of the ground principles exposed in some of the finest books about data science.
In today’s article I will focus on a very well sturctured paper of Trevor Hastie, Professor of Mathematical Sciences at Stanford Univesity. His book, co-writed with Robert Tibshirani and Jerome Friedman is called The Elements of Statistical Learning: Data Mining, Inference, and Prediction and tries, if not, manages to give a detailed explanation to the challenge of understanding of how data led to development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. This paper mainly observes the important fields and ideas in a common virtual framework.
The approach being mainly is statistical, the emphasis falls on concepts rather than on mathematics. Many examples are given, with a easy-to-understand use of color graphics. It is a valuable resource for statisticians and everyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (better known as prediction) to unsupervised learning. Various topics are covered including neural networks, support vector machines, classification trees and boosting – the first comprehensive treatment of this topic in any book of this kind.
All in all I can certainly say that the presentation is not keened on mathematical aspects, and it does not provide a deep analysis of why a specific method works. Instead, it gives you some intuition about what a method is trying to do. And this is the reason why i can say that I like this book so much. Without going into mathematical details of complicated algorithms, it summarizes all necessary (and really important) things one needs to know. Sometimes you understand it after doing a lot of research in this subject and coming back to the book. Nevertheless, the authors are great statisticians and certainly know what they are talking about!
There comes a time in each of our lives when we wonder ourselves either from curiosity or from perspective what is going to be the next big thing, and because this is a blog dedicated to science we are gonna restrict to this area.
Of course we can’t know what is going to be the technology of tomorrow but we are going to tell you what is not going to be: Facebook! According to Princeton’s engineers facebook it’s very likely to reach to an end in the next few years. They used for the research an epidemiological model, very similar to Gaussian bell but more complex in the way of describing the transmission of communicable disease through individuals. According to the model chosen, called SIR the total number of population equals the sum of Susceptible plus Infected plus Recovered persons. They chose this pattern because is relevant for phenomena with relative short life span, and after that they applied in the case of MySpace and they noticed that it fit almost perfectly.
We can easily see in this graph that the decline of facebook has already begun but it’s not as near as expected. Actually we can be sure that we will not exterminate it from our lives sooner than 2018 but also, internet can be a very unpredictable place and no one can exactly determine how it’s going to end.
Also we advise you not to take for granted this study because, as we found out, it was conducted by researchers based in the school’s department of mechanical and aerospace engineering. Not saying that they are not professionals but nevertheless not experts in such social studies.