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What’s Changing in Data Science?: The Top Data Science Trends of 2019

Big data is a big deal: the worldwide market for data analytics will grow to over $40 billion by 2023. Analyzing data is becoming more and more important to businesses both big and small. And that trend doesn’t seem to be slowing down anytime soon.

This year, we’ve seen a transformation of the accessibility of futuristic trends. These include machine learning, artificial intelligence, and augmented analytics. Now, even small businesses can use some of this technology and compete with the big guys better than ever.

There’s also a growing trend for novel forms of analytics. Curious about the top data science trends of the year? Read on to find out what’s growing — and what you should be implementing to stay ahead of the curve!

IoT Networks and Analytics

With the rise of Amazon’s Alexa and Google Home technologies, this trend is clear. Internet of Things (IoT) has become commonplace in homes around the world.

As these networks grow, an increasing amount of data collection is possible. That means we need better ways to collect, analyze, and process this data. So the industry is pushing to create better solutions.

By 2020, experts predict 20.4 billion IoT devices will be in use. And every single one of these billion devices produces tons of data.

These new solutions will have to sort through and make sense of all this information. And that’s no easy feat.

Amazon has already started with their AWS IoT Analytics solution. Others will soon follow. There’s big money in creating solutions for IoT analytics. And this likely won’t change anytime soon.

Machine Learning

Machine learning is finally taking off. Right now, Python as the clear leader for its implementation. And with affordable processing power, storage, and GPU optimization options, it’s becoming accessible.

Machine learning will allow data analytics to become fully automated. Machines can train using deep data sets. They can then recognize the hidden patterns present in data. They may be able to soon outpace human data analytics.

So, does that mean your job in data analysis isn’t safe? Of course not.

Once machine learning becomes commonplace, it simply means that you’ll be free from simple tasks. Instead, you’ll be able to focus on the creative and productive implementation.

Want to learn how to use this technology? District Data Labs has a great Machine Learning with Python training course to teach you all the basics. You can learn more about it on their website.

AI Accessibility

Artificial intelligence used to sound like the technology of the distant future. But now it’s becoming accessible to even small companies.

AI can complete tasks quicker and with better precision than humans. This helps reduce errors and speeds up workflow.

But don’t fret if you don’t know how to implement AI yourself. There are plenty of machines that come with AI applications which have made its implementation easier than ever.

And AI is going to make small businesses better able to compete with bigger companies. AI can help small business owners reduce costs and improve ROI so that they can scale up with ease. 

Using AI, small businesses will also be able to analyze data across several marketing channels. And this would be almost impossible to do in budget with a low-budget marketing team.

Augmented and Embedded Analytics 

Together, these data science trends will help analytics take on a more proactive role. By 2023, the augmented analytics market is expected to grow 30.6% to $18.4 billion. Together, they’ll offer faster insights by combining machine learning and language processing.

Together, they’re lowering the prohibitive cost of entering into the business analytics industry. Soon, smaller businesses will be able to perform these types of analyses by automating data prep, analysis, and building. Additionally, they’ll help create better interactions between suppliers, customers, and partners.

Predictive and Proactive Data Analytics

Up until now, data analytics has looked to the past to better inform future business decisions. But now, data analytics will be able to help predict what might happen in the future.

The advantages of this are clear. Predicting changes will save time and money and help businesses achieve their goals. It will also provide a competitive edge to businesses still doing things the old way.

Counteract scheduling problems, staffing issues, and overtime concerns before they happen. Anticipate what your customers will want to buy this upcoming season. Shift towards a more proactive data analysis model today.

New Cybersecurity Challenges

Protecting cybersecurity has always been a challenge for data analytics. But with the growth the IoT, there’s a whole new class of data security challenges.

The IoT means that an increasing amount of data gets collected by many types of devices. So that means that information confidentiality will have to be improved across them all.

With the rise of machine learning, new methods are in development for analyzing cybersecurity threats. Soon, machine learning and AI may be able to integrate security data to automate security.

These new methods can analyze past threats. They can then use this information to help prevent future data breaches and hacking.

More (and Better) Chatbots

Anyone who’s visited recent websites can attest to the rise in chatbots. And it makes a lot of sense. After all, with better AI, chatbots can handle customer queries in an instant.

No longer will we have to wait on the phone for an hour to speak to a customer service representative. And this will save businesses money because they won’t need to hire as many individuals on staff.

Using big data, these bots can process data and provide personalized, relevant answers. And with more conversations, these bots can constantly evolve to become more relevant and craft better answers.

Edge Computing

The cloud was the next big thing only a few years ago, but now edge computing is already outpacing the cloud. Unfortunately, there can be a lag in collecting data that comes from the cloud. And with the IoT growing in popularity, the vast amounts of data that need to be transferred is going to make this a real problem.

So with this huge amount of information, data processing is going to have to happen closer to the information source. Because less info flows in and out of the network with edge computing, it costs less and performs faster. 

It can help solve problems about latency, bandwidth, and connectivity. And it does all this while speeding up data analysis to provide quicker reaction times.

Data Protection Regulations

Only last year was the first data put into place. The General Data Protection Regulation (GDPR) in the EU is changing the way we go about data analytics. There are limits on what information is able to be collected and how personal data can be managed.

If companies practice poor personal data management, they’ll receive huge fines. The EU has also made it clear that any fully automated decisions must be properly explained to all affected individuals. 

This is only the first step in data protection regulations. The U.S. Congress is already talking about implementing a similar rule.

It seems clear that accessing data is going to come with restrictions in the coming years. The question that remains is how data scientists will access this information while keeping in legal boundaries. And we’re going to have to question how we can manage all of these laws from individual countries while we work with a global internet.

Continued Job Security

Just because AI and Machine are growing doesn’t mean that data scientists will need to worry about their jobs. In fact, data analysis skills continue to be in high demand.

LinkedIn’s annual report found that the top three hard skills companies needed were all data science specialties: cloud computing, statistical analysis, and data mining. And data skills have been at the top of this list for the last 5 years.

Data science helps create better software and products. They can predict user requirements to help build smarter, better solutions that the customers actually want.

Data engineering will also be highly sought after. These individuals take the insights of data scientists and apply them to businesses. Together, data scientists and data engineers will work together to create better AI, real-time analytics, and augmented analytics.

Now You Know the Top Data Science Trends of the Year!

Now you know the top data science trends of the year. Don’t fall behind! Sign up for a data science course in one of these topics today so that you can stay current in your education.

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