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Data science in 2020 is a dynamic field. As more organizations discover the benefits that serious data collection and analytics offer, and the field becomes both more accessible and more advanced, there’s never been a stronger demand for experts who know how to accumulate and understand high-quality data. Much of the excitement is driven by three key trends; read on to discover what they are.

Democratization of AI-Augmented Analytics

As artificial intelligence tools become easier to use and more accurate, a greater number of organizations are leveraging it to discover data patterns, organize information, and improve their forecasting and predictive capabilities. While many of the recent developments in AI have come from supervised learning, which attempts to find a specific pattern in a dataset, machine learning capabilities have also improved in unsupervised settings.

Also known as strong AI, unsupervised models have the more complex mission of discerning patterns in data that humans don’t know for certain to exist. An ML model that applies unsupervised methods to pattern recognition assignments can also integrate with its supervised learning counterparts to examine these newly discovered patterns. As machine learning models improve their teamwork, the advancements for organizations capable of leveraging their abilities will be profound.

Integration of IoT and IIoT Data

From Wi-Fi-enabled lighting and thermostats to voice-activated smart speakers like the Amazon Echo and Google Mini, consumers have embraced the timesaving features of the Internet of Things. The relatively new omnipresence of IoT devices has provided companies and data analysts with a proliferation of new inputs that offer a distinct perspective on their customers’ behavior and needs. Sensors and voice recognition add a new dimension to the browsing data generated by phones, tablets, and computers. Companies poised to collect and manage the vast troves of new data available to them will be able to cultivate a lasting competitive advantage.

While the popular image of networked devices revolves around domestic use cases, internet integration is also so promising in logistical and industrial markets that some developers have spun off applications specifically for this segment, christened the Industrial Internet of Things, or IIoT. Because of the existing availability of SCADA systems and HMI monitoring, as well as networking protocols like MQTT, UDP, and even MODBUS, opportunities for collecting and analyzing data for performance optimization are plentiful. For instance, manufacturing facilities across a variety of industries can apply IIoT tools to monitor equipment and send alerts to operators to initiate repairs or shutdowns when values like temperature or pressure fall outside predetermined limits.

Consolidation of Programming Languages

Although it seems as if there are at least as many programming languages as human languages, one has increasingly emerged as a favorite among data scientists: Python. While this isn’t to say that other languages, especially mainstays like Scala, Java, and R, don’t still have a place in the world of data science, this newer language has become a fast favorite, especially among engineers and programmers with fewer than five years of experience in the field.

Why? For one thing, Python’s popularity is a self-perpetuating cycle. Because it’s readable, and thus easier for new programmers to learn, the Python community is exceptionally active, so it offers excellent community support as well as a wide array of open-source libraries that work to expand the language’s capabilities for data processing, all of which are free. Its simplicity and readability also mean that programmers can write less code to achieve the same goals when working with Python. Finally, it’s also one of the most flexible programming languages; it appears in applications from data science and machine learning to browser-based applications. Python also offers data visualization and analytics tools that many programmers find indispensable.

Among the numerous popular segments in computer science, data science may be the one most in-demand right now. With an increased need for accurate predictive models that respond quickly to fluid situations, the high-speed, high-tech world of modern data analysis has compelling applications in every field.