Data science has been a topic which has been recently going through a massive wave of regulations after GDPR. In May 2018, following the infamous Cambridge Analytica scandal, many were trying to understanding what would have happened to data science as a whole after GDPR and, long story short, there isn’t a final answer yet. With this being said, let’s analyse the future of data science now that it has (almost) being regulated.
What Did GDPR Do?
GDPR, when launched in May 2018, has restructured the way data was being passively gathered. In fact, following what happened with the Cambridge Analytica scandal, every form of digitalized data gathering must be declared and processed following simple data regulations, making, therefore, the entire “secret acquisition” impossible to do, or better, illegal to do.
GDPR has been the first step in regulating a digital field since the net neutrality law which has been launched by the FCC in 2018, proving once again how the law sector has been actively looking into digitalized fields.
How Did This Impact Data Science
Now that data can’t be simply gathered via cookies, the need for more advanced Python frameworks has become a necessity. In fact, in 2019, we’ve seen the rise of over 23 new data science-related frameworks, which were created with the sole purpose of delivering and building algorithms for data points and packages. This is quite important, as it states how quickly the data science development industry has been reacting to the GDPR update.
The Future Of Data Science
Is definitely related to pure analysis. In fact, data science as a whole will move towards a much more analytical (not automatic) approach, with split tests and case studies, instead of blindly relying on passively acquired data, given the fact that these packages will be gathered and processed with a smaller volume. This is definitely extremely important, as it confirms how important data science, whether if you’re an app developer or an Alexa developer, is.
Data science is moving fast, with new frameworks, software and approaching the entire cloud architecture in 2019. This is definitely an exciting time for the Python developer who wants to approach the world of data science in 2019 as, given the changes which are happening, everything is due to be rebuilt in a couple of years time.
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