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Data science and analytics are progressing faster than ever before – and many predictions indicate that these fields will not slow down anytime soon. It makes a lot of sense, looking at their integration into the current business environment. But it goes beyond that. These fields are among the main driving forces in many important sectors right now, including ones where profit is not the main motivation.
There is a lot of space for implementing advanced analytical solutions in a wide range of industries, from healthcare to logistics. In many regards, we’re barely scratching the surface of what’s possible, and it will be exciting to see where the future takes us. Until then, let’s have a look at some of the key trends on the horizon right now.
Data science has been heavily involved in fraud – unfortunately, on both sides of it. On the one hand, we have malicious actors aided by technology that allows them to effortlessly spoof communication from other parties, including producing fake voice recordings and videos. While most have been focused on the entertainment implications of this technology, various sectors like finance have been facing significant issues as a result of these trends. Video is quickly becoming unreliable as a form of identity verification, and companies have been scrambling to find alternatives that don’t raise any red flags in privacy-conscious users’ minds.
On the other hand, advanced analytical systems are at the forefront of combating scammers right now. Many classic scams can be reliably identified almost completely automatically, relieving human operators of a huge portion of their work, and leaving them to focus on cases that actually require manual intervention.
In the beginning, while data science was still gaining momentum, the technological forefront of the field was a huge mess. Researchers were trying to use pretty much every language and tech stack under the sun to figure out what works and what doesn’t, and it was difficult for newcomers to orient themselves in a direction that didn’t face the risk of obsoletion. Now, it’s a different story. Several languages like R and Python have emerged as industry leaders, and we’re already seeing some full stacks stabilizing on the market and enjoying attention from companies at all levels.
And that’s a great change for those interested in getting involved in the field, because it provides them with much more security and confidence during their learning stage, which is arguably when people need that kind of support the most.
Data analytics used to be seen as something exclusive to companies that could afford the expensive specialists to handle those systems. Not anymore. Advanced analytical solutions are now increasingly being packaged into user-friendly ways, aimed at people with absolutely no experience in the field. That, in general, is not a new trend in the tech industry. Just look at application development – several decades ago, it required expensive, highly qualified specialists to just get some basic groundwork done. Today, those specialists are still needed, but in much more tightly defined positions. Scientists are still working on pushing programming paradigms further. But the rest of the work is handled by people with less experience, using technologies that have seen years of polishing to make them usable by the average person.
The same is already happening with data science and analytics. And it …….
Source: https://www.kdnuggets.com/2022/08/5-key-data-science-trends-analytics-trends.html