Top 3 Trends in Data Science for 2020

James Mahoney

Everywhere you look the terms "data science" and "machine learning" appear. Whether it's on LinkedIn or other news sources, and they seem to be appearing more frequently every year. 2019 was a big year for data science. We saw AI become more accessible for both small and big businesses. As it became more accessible, it also has become more scrutinized. The ethical dimension of AI and Machine Learning became a big talking point in 2019. We also saw a huge uptick in IoT gadgets, in both wearables and household items. Looking at the future of 2020 these are the top three trends in data science for 2020:

Data Privacy and Security

While we have already seen an increase in data security (such as Europe's GDPR). While increased data security has been a hot topic in other parts of the world, it is only recently that we have seen the U.S. begin to crack down on it. As more (sensitive) data becomes available, hackings become increasingly worse for the community.

Take the Google Cloud hack, for example. Or this study that 60% of major US firms have been hacked. Hacking has become widespread and, more importantly, ubiquitous in daily news. As usual, this has led to citizens becoming more concerned about their data and who has access to that data. We predict that increased laws will come about for users to feel more secure about their data being in safe hands.

This is important because no matter what your company does, or where it is located, machine learning and data science will be a necessity in the near future. But it also shows that given this trend of expansion, finding data scientists to work in your field and location is not only becoming necessary, but also easier.

Hyper Increased Data Science in the Cloud

Somewhat related to why data privacy is becoming increasingly important, the actual data available has grown significantly. In 2016, roughly 44 Billion GB of data were produced each day, and it is predicted that the number will grow to 463 billion GB of data created per day in 2025. Given this huge increase in data, the computing power has to follow suit.

While a typical PC (or even a high-end one) works well for day-to-day use, it is not nearly powerful enough for industry-level data science. Hence the spike in cloud based servers, such as AWS (Amazon Web Services), which offers servers with up to 96 virtual CPU cores and up to 768GB of RAM. Much more than what you can get on the best personal computers. As the amount of data increases, so will the need for these high powered cloud services.

Humans Become More Important as Data Volumes Increase

Having data is one thing, but knowing what to do with it and what to get from it is another. As data increases, it's going to become more and more important to have people that can both do the data science itself, but also extrapolate from that data and present their findings. This is where behavioral analysis becomes key in interviews.

This is why AdaptiLab built our automated technical screen, to help companies regain their interviewing time and focus on the behavioral side of interviews. The live coding screen assesses candidates on the five core competencies of data science and machine learning (data processing, data analysis, feature engineering, model development, and general algorithms), so that companies who use the AdaptiLab product can quit worrying about their technical abilities and solely focus on their ability to present and think about various situations.

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