What Data Science does, can do and will do for scientific discoveries

So far, we have proven that Data Science is not only for business, marketing and advertising, but can have practical uses for various types of fields in Science, ranging from Astronomy to Physics, and beyond. But the examples presented are just the beginning.

We have ascertained how Data Science can be useful in the pursuit of scientific discoveries, but now it is time to review the vanguard of the industry, where it is headed, what does the future holds and what might be some roadblocks to those ends.

Deep learning the future

The concept of deep learning is based of neural networks, which has been around for a long time (Roberts, 2014), but it has taken speed in the latest years, with corporations and universities investing in  multi-layer neural networks destined for the most varied of uses.

Without going much further, we can mention the much mentioned project Google Deepmind, which has developed things like human-level control deep reinforcement learning and many other projects (Google Deepmind, 2011).

Figure 1 – Example of how deep learning works for face recognition (Mayer, 2015)

But maybe the most impressive and well known feat achieved by the project is the development of a deep learning program that managed to learn the complex game of Go, and defeated the top Go player, Lee Sedol, in a 5-match competition (Gibney, 2016). This is an accomplishment that shows how much Deep Learning has advanced, since experts said that a computer would never beat a human player (Cho, 2016).

That’s why deep learning is being experimented on cell classification (Chen, et al., 2016), chemical mappings, x-ray scattering image classifications and many more (Brookhaven National Laboratory, 2015). Even major universities and research centers are investing in deep learning, like NERSC and Berkeley joining forces to test the capacity of the technology with health and medicine breakthroughs (Kincade, 2015).

Data Science as an aid of human knowledge broadening

With science advancing in giant leaps in several fields, and instruments getting more powerful and sophisticated, the amount of data to process is getting bigger and bigger. That is where data science comes into the scene.

The detection of gravitational waves is one of the biggest headlines in scientific discovery in the past months (Overbye, 2016), confirming a 100-year old Einstein theory. But the fact is that the Laser Interferometer Gravitational-Wave Observatory received a particular strong signal that managed to confirm the theory, a feat that proved difficult because of the difficulty of discerning signals from noise. That is how Data Science could help make this separation of signals from noise easier by finding underlying evidence by processing the outstanding amount of data produced by their equipment (Yuan, 2016). 

Figure 2 – Consistent signals detected in LIGO sites located 2000 miles apart (Circus Bazaar, 2016)

And it is worth mentioning how Data Science could help Astronomy. As telescopes get more complex and sensitive to light, the amount of data gathered is getting larger and unmanageable. That is the reason several projects are using Data mining to recognize celestial bodies, to try to keep up with the data production (Galaxy Zoo, 2016).

But it is not a paved road ahead

As sciences advances, so does the fear that humans will be replaced by robots. With predictions of computers with advanced neural networks replacing entry level lawyers (Kravets, 2015), and advances made with IBM Watson learning case histories of hospitals to learn what diagnoses and treatments to recommend (Cohn, 2013), there is a concern about how the advancements of Data Science are going to affect the rest of the population.

Figure 3 – Example of IBM Watson’s healthcare capabilities (Saxena, 2012)

Also, for data science to thrive, it needs data. And because scientific papers, research and publications are so difficult or expensive to get a hold of (The Cost of Knowledge, 2012), sometimes the raw data or sources necessary to discover something novel is somewhat of an utopia; with publishing companies charging enormous amounts to get a glimpse of their material (Elbakyan, 2015).

Data Science’s has yet no bounds

While there are still titanic challenges in the sciences that Data Science is yet to conquest, there are breakthroughs made by the day, trying to overcome shortcomings and achieve a better understanding in several fields of science (Prabhat, 2015).

So the future looks bright for Data Science, showing significant increase of demand of people expert in the field (Islam, 2015), a number of companies getting into the game and being a participant an active participant of scientific discoveries. It is to be seen how bright it can be (NeRSC, 2015).

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