In this episode, we tell you how to become data scientist and join an amazing community that – you like it or not – is changing the world with data analytics.
Should data scientists follow the old good practices of software engineering?
Data scientists make software after all.
The fact that they analyse data shouldn’t bring anything different from the pretty old skill that is software design and development.
This should apply in theory.
In practice… it’s another story.
It’s time to experiment with Data Science at home.
Since we are still dealing with our hosting service, consider the first episode purely experimental, even though the content might be of your interest, no matter what.
In this episode we speak about some of the most reliable predictions of data science as a discipline in 2016.
Detecting the interactions of genetic compounds like genes, SNPs, proteins, metabolites, etc. can potentially unravel the mechanisms behind complex traits and common genetic disorders. Several methods have been taken into consideration for the analysis of different types of genetic data, regression being one of the most widely adopted. Without any doubt, a common data type is represented by gene expression profiles, from which gene regulatory networks have been inferred with different approaches. In this work we review nine penalised regression methods applied to microarray data to infer the topology of the network of interactions. We evaluate each method with respect to the complexity of biological data. We analyse the limitations of each of them in order to suggest a number of precautions that should be considered to make their predictions more significant and reliable.
Read the full paper free of charge.
In the era of sustainable energy, power grids and electric cars, I think it would be appropriate to talk about sustainable gyms too. Sport and fitness are considered the most common leisure activity for the majority of people. This occurs even more often while we approach the Christmas holidays and New Year’s Eve, when our tables will be stuffed of cakes and calories.
So why not thinking about powering a gym,
by just doing a workout?
I envisioned a floor that would generate energy just by walking on it. The energy could be immediately used to power the display of the spinning bike our buddy is cycling on. Or just stored in a battery for later. Weight lifting is yet another source of energy. Every time the weights are “smashed” on the floor, they would generate an amount of watts. After all, that’s what people do when they go to the gym: they generate watts in order to burn that cake they really could not say no to.
I recently came across pavegen.com a company that makes a tile that, indeed generates electricity just by stepping over. Regardless of the fact that these guys do that since 2012, the problem of such a technology is that they need about 1500 euros to cover 1 square meter of surface.
The gym where my mate is used to go is about 400 square meters, meaning that the whole floor would cost 600 000 euros plus maintenance. Moreover, it would generate approximately 2kW at its best. Not so much for such a large gym and for the cost of the floor. This cost would be payed back in 3 years with a minimum of 1000 subscribers. Quite risky.
I am looking at this fact just as a technological gap, not an unfeasible idea. The day this technology will be more affordable there would be no reason to tile gyms, offices and even streets to produce energy just by walking on them.
This, of course, should not discourage us from doing workouts today.