I want to write now about a phenomena that is a part of our lives with or without our consent. I’m talking about Apple’s market strategy and how its intrusive policy of becoming one of the most exquisite brands have affected our perception on quality or innovation.
There’s no point in denying the direct correlation between Apple total revenue and the influence it has on the market of electronics and only by looking at the graph of sales we can say so.
Further more, although the statistics that we found aren’t quite recent we can add that last week Apple inc. has sold 10 millions devices of its latest product, making it double than the original estimations. It would have been an even larger number but because of the major traffic that apple.com has had, the servers were down for a number of hours.
Now, in terms of data and what we are interested in, regarding the fact that all expectations were exceeded last week we can say that the only thing that is still bringing the money to Apple is the iPhone division. The sales of iPad are decreasing and so are the ones of Macs and iPods but even so the stocks listed are increasing their values. This can be explained only by following the history of the company from the day they were first listed back in the 90’s until now and observe that customer satisfaction and brand recognition as quality are one of the highest ever recorded.
In the end i’d like to say that even if Apple took its blows first with the competitive market of Android and Windows phones, later with the death of Steve Jobs and most recently with the flaws in the iCloud system that allowed hackers to break in and publish nude pictures of dozens of celebrities, marketing data reveals that the company is still in the top preferences of gadgets enthusiasts all over the world with a consolidated position over Samsung that will not decline too soon.
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!