2015 came, and by now is almost gone and we can see that we’ve been mostly deceived by popular expectations from the media industry like hover boards, flying cars or laser guns.
It’s obvious that we all wish for such cool gadgets and we are eager to use them, but are we actually? in this matter data science has a word and establishes itself as an expression of people’s hidden wishes by underlining not what they say or what they wish but actually what people do in order to fulfill a goal. By now we determined that people love to read or watch SF but don’t actually want to experiment dangerous technologies that can be unstable and as much as The Jetsons inspired security things are not quite so, and from a darwinian point of view it’s the most normal thing to do.
Tinder App is nothing new for anybody since most of us slowly accepted it in our lives but it also brings some displeasure. For instance, this guy thought that it can automate the process in the way of an app that decides if you’d like a person and start a conversation.
For those of you who aren’t convinced yet that Big Data infiltrates more and more in our daily lives i have one more proof that data science is the future of all sciences and has the power of interconnecting all branches of life. it’s not a fast process, replacing conventional methods of taking decisions with ones more analytically but certainly the small steps we are taking are headed in that direction.
Although the improvement was of only 12% Los Angeles took a giant leap last week when they announced that after a effort of more than 30 years of local authorities they managed to synchronize and adapt to traffic conditions all the red light signals in the 4 millions residents city, making this an experiment we will all have something too learn in the near future.
Research engineers in state DOT explained that synchronizing traffic light is only a small step in decongesting the traffic and this is why they are more focused on studying the habit of the drivers. Since the ’80s they anonymously gathered data, first through a network of cables and sensors placed into the roads, called loop wires and more recently, as the technology evolved, they used toll bots to calculate average speed of cars or wireless sensors to detect number of people in the car, by the number of phones inside and their behaviors.
The next step, traffic researchers say will be to find a way to communicate with the car itself for gathering of data and ultimately to provide real time feedback for congested streets, alternative routes or energy management tips. Last but not least, improving public transportation must be a priority in every form it can be found, and also implementing new ways like modular self driving cars, linked together for a common road portion, all for better management of transportation.
I don’t know how much have you heard about Perspective Analytics because it is not as popular as Descriptive and Predictive Analytics but sure it has the power of changing how we treat Big Data.
By taking a blunt look at this situation we can say that Perspective Analytics is the new term to name the step from analytics to knowledge in the data to knowledge pyramid. Predictive analytics is the next step up in data reduction. It utilizes a variety of statistical, modeling, data mining, and machine learning techniques to study recent and historical data, thereby allowing analysts to make predictions about the future. As we know, big data imposes a huge amount of information the majority of which is useless, hence the necessity for this new service.
The purpose of analytics is not to tell you what is going to happen in the future but, because of its probabilistic nature, to inform you of what MIGHT happen, based on a a predictive model with two additional components: actionable data and a feedback system that tracks the outcome produced by the action taken.
This type new step/ type of analytics was first introduced in 2013 after the Descriptive Analytics was defined as the simplest class of analytics, one that allows you to condense big data into smaller, more useful nuggets of information, after which next step in reducing information is by applying a Predictive algorithm.
IBM’s vision is that descriptive analytics allows an understanding of what has happened, while advanced analytics, consisting of both predictive and prescriptive analytics, is where there is real impact on the decisions made by businesses every day
A problem well known in the data science world is the mismatch between people who have the data and people who know how to use it. On the other hand data scientists complain about the difficulties of the scrapping process and more exact, the difficulties of obtaining the data. For this mismatch Kaggle was created, trying to mediate a connection between data and analysts.
The platform was born on this principles and creates a competition between users which must update solutions to diverse data sets and so to win points, and, in the end, money.
On the other side, the uploader of data gets a number of possible solutions of analysis to his data sets, from which he can choose the most appropriate to his interests.
A very interesting case study, and a powerful demonstration in favor of Kaggle capabilities is the collaboration that the platform has, with NASA and Royal Astronomical Society, in which the challenge was to find an algorithm for measuring the distortions in images of galaxies in order for scientists to prove the existence of dark matter. It seems that within a week from the start of the project, the accuracy of the algorithms provided by NASA, and obtained in studies started back in 1934 and continued to that time was reached. More than this , within three months from the start of the project, an algorithm was provided by a user, that was more than 300% more accurate than any of the previous versions. The whole case study can be found here.
essentially, the fun thing about Kaggle is that the winners of the competitions are folks around the world with a knack for problem solving, and not always degrees in mathematics. And degrees don’t matter on Kaggle; all that matters is result.