5 Key Data Science Trends & Analytics Trends

Let’s have a look at some of the key tech trends on the horizon right now.

5 Key Data Science Trends & Analytics Trends
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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.


1. Fraud – Aiding and Fighting Against It


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.


2. Tech Stacks Are Getting More Streamlined


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.


3. Lower Technological Barrier of Entry


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 will likely continue to happen over the next few years, possibly even through the whole decade. Which is great news for everyone involved – companies will find it easier to get access to in-depth analytics, while specialists will enjoy the freedom of working on more challenging projects instead of constantly being tasked with menial work.


4. A Growing Demand for Competent Specialists


And that leads us to another important point. As these changes are taking over, the job market has started to shift in some predictable ways. While companies don’t need advanced specialists to handle their analytics anymore (at least on a basic level), demand for the most qualified professionals in that field has been climbing steadily. That’s because companies with deep pockets want to be at the front of new developments in the field, and that still requires significant investments and a competent workforce.

Those who’re thinking of orienting themselves in this direction certainly have a lot of potential ahead of them. And there’s no indication that things will move in a different direction anytime soon.


5. More Attention on Cleaning Up and Maintaining Data Sets


The last decade saw an explosion in gathering data and storing it for future analysis. One of the benefits of advanced data analytics is that it can work just as well on historic data, which has prompted some borderline hoarding behavior in some of the biggest companies on the market – the ones that can afford the large data centers needed to store all of that information.

But recently, a new trend has started to emerge. Companies have started to realize that a lot of the data they’ve been piling up for later analysis could actually end up largely useless, at least in its current state. Data collection practices weren’t exactly diligent and streamlined in the beginning, meaning that many companies are now in possession of huge sets that need a lot of sanitization work. Unfortunately, that’s still something that requires manual labor to a huge extent – and that’s where a lot of the attention is going to fall over the next decade.

In general, data science is moving towards a much more streamlined situation, one where everyone has a specific place in the industry and where typical requirements for a project are known in advance. That doesn’t mean that there will be any less opportunity for competent specialists to thrive though – quite the contrary. Now is the best time for those who want to be involved in that field as closely as possible.

If you’re interested in diving deeper into data science, Springboard’s Data Science Career Track can offer you an in-depth understanding of key concepts in the topic, and also has specializations you can choose from to deepen your knowledge of a particular domain of your choosing. Consider checking the offering out today if you’re looking to move to the next level in data science!

Riley Predum has professionally worked in several areas of data such as product and data analytics, and in the realm of data science and data/analytics engineering. He has a passion for writing and teaching and enjoys contributing learning materials to online communities focused on both learning in general as well as professional growth. Riley writes coding tutorials on his Medium blog.