Over the past ten years, the Internet of Things (IoT) has established itself as one of the most important technologies of the 21st century. Comprised of ever-expanding networks of sensors and machines, the rise of IoT is increasing productivity, powering the creation of new products and services, and enabling us to gather more data about our world than ever before.
Crucial to the growth of IoT is the field of data science. The innovation of IoT provides yet more proof of data science’s indispensable role within the modern tech sector, and has increased the profession’s already top-tier employability even further. The IoT industry already has a data science talent shortage, and as the industry continues to expand, its hunger for data science talent will continue to expand too.
What Makes Data Science Valuable to IoT?
IoT is fundamentally concerned with computers and machines using networks to “talk” to each other, a process which occurs entirely through the exchange of data. Therefore, if data is the fuel that IoT runs on, data science algorithms turn that fuel into something useful.
Daniel Christie is Head of Engineering at James Cook University and believes data science serves a crucial value creation function for IoT systems. “Data science takes and utilises the data collected through IoT systems and technology, and transforms that through analysis and visualisation into something that can create value for an organisation or business. The data science component allows the value to be extracted and understood from the IoT technology use and deployment.”
For example, virtual assistants such as Amazon’s Alexa use machine learning to power their speech recognition functions. The sophisticated hardware and networking setups that Alexa devices rely upon would be of little value if data scientists weren’t around to enable their core voice-command capabilities.
Voice recognition is just one of many IoT-centric abilities that data science enables. IoT devices leverage a wide variety of data science capabilities to power the key functions they need to operate.
What Specific Data Science Skills Do IoT Data Scientists Need?
Due to the IoT industry’s focus on creating devices that directly interact with the physical world, the industry’s data science needs are somewhat particular. Here are some of the key areas that IoT data scientists need to know about.
Key Big Data Skill: Real-Time Streaming Analytics
One of the benefits of IoT is the massive amount of data it generates – a projected 79.4 zetabytes annually by 2025. In order to work in this context, IoT data scientists must be skilled at manipulating and processing big data, especially large scale streaming data.
Real-time streaming analytics are necessary to power devices that need to quickly devise answers to complex user requests, or which need to provide users with real-time visibility into something that the devices are monitoring (such as a factory floor). Because many IoT services receive data from multiple sensors simultaneously, it is crucial to be able to aggregate multiple data streams together so they can be analysed as a single whole.
For a more technical explanation of how IoT streaming analytics work, see here.
Key Machine Learning Skill: Reinforcement Learning
Machine learning skills are crucial to the function of many IoT services. A knowledge of deep learning, which powers things like voice recognition systems, is particularly important.
One of the most important types of machine learning for IoT data scientists to understand is reinforcement learning. Reinforcement learning frameworks enable deep learning neural networks to learn from their mistakes by ‘punishing’ them for errors and ‘rewarding’ them for successes. IoT services that enable machines to operate autonomously, such as those used by self-driving vehicles, often rely on reinforcement learning.
Reinforcement learning is often compared to a game in which the data scientist sets the rules of the game and the algorithm plays it. Through countless simulations, the algorithm learns which behaviours win the game and which behaviours lose it, until it eventually becomes skilled enough to operate with consistent success once deployed into the real-world.
These technical skills are necessary for data scientists to be able to recognise and solve complex problems to make IoT systems work efficiently. During the design phase, Christie notes that data scientists must be able “to understand [the] question/problem that you are trying to answer with the data collected through the design and deployment of an IoT system/solution.” The IoT system must then “effectively analyse, interrogate and communicate to present real solutions to the question/problem.”
What Specific IoT Knowledge Do IoT Data Scientists Need?
In order to work effectively as part of an IoT team, it’s important to have a functional understanding of the entire IoT design and deployment process.
According to Christie, a strong knowledge of IoT technologies is crucial for data scientists. In order to understand “the art of the possible”, data scientists must know how to “design and implement IoT systems/solutions to collect the required data to understand [and] answer the problem”. This knowledge also ensures that data scientists can ascertain the limitations of a given IoT system’s data collection process, allowing them to make “appropriate considerations” to ensure that the data analytics process is feasible in various real-world settings.
Key IoT technologies that data scientists should gain an understanding of include:
CAD (Computer-Aided Design and Drafting)
CAD software is a key part of the process for designing and engineering IoT devices. Knowledge of CAD is necessary for data scientists to understand the basic structure of the devices they’re working on and the IoT development process from a physical design perspective.
IoT Computing Hardware
Most IoT devices need to be compact and rely on compact computing hardware to operate. Two common hardware frameworks used by IoT developers are Raspberry Pi and Arduino, both of which allow for the creation of feature-complete computers on a single chipboard. Learning about IoT hardware frameworks allows data scientists to understand how far they can push their project’s on-device analytics capabilities.
Data scientists can gain a basic understanding of these technologies through self-learning – both Arduino and Raspberry Pi are open-source frameworks, and Raspberry Pi was initially invented as a computer science learning tool.
The majority of IoT products use cloud computing as part of their service. By utilising cloud processing, low-power IoT devices can execute complex tasks that would otherwise be beyond their means to carry out. The cloud also provides IoT services with a central repository to analyse data from, and push updates to, all of its deployed devices. IoT devices that rely on big data processing are especially reliant on cloud services.
While it isn’t necessary for IoT data scientists to have an understanding of cloud engineering, it is extremely advisable to have an understanding of what cloud-based services are available and how they can be used. A wide array of cloud providers offer data storage, management, transformation and analytics services. A data scientist must have a strong knowledge of these services in order to know what tools they can leverage to power their IoT projects.
Important cloud services to know about include:
● Cloud services that are solely dedicated to IoT Analytics, such as ThingSpeak.
● Major public cloud providers, such as Google Cloud Platform, Amazon Web Services, Microsoft Azure and IBM Cloud. Many IoT companies host their platform on these providers and make use of the various services that they offer, which include database storage, data warehousing, ETL, big data processing and analytics.
All of these providers also offer dedicated IoT service management platforms that can connect with a variety of devices and protocols.
Whereas cloud computing refers to computing that takes place outside of an IoT device’s network, edge computing refers to instances where data is processed inside the network the device is connected to. Edge processing can occur on the device itself, or on a dedicated IoT server that acts as a gateway between devices and the internet.
Edge processing tasks often rely on machine learning and other data science functions to operate. For instance, many edge sensors installed on industrial equipment use machine learning algorithms to predict when the equipment will need preventative maintenance.
Edge computing was developed to improve IoT services, and is an increasingly important part of the industry. The ability to process data directly on a device means that devices can respond to user requests faster, which is extremely important for autonomous machines that need to be able to rapidly analyse and make decisions about their environment in order to operate safely and effectively.
What Does Australia’s IoT Ecosystem Look Like?
The Australian economy has a strong engagement with IoT, both from a usage and an innovation perspective. The Australian government is a champion of the Industry 4.0 paradigm, which envisions an IoT-driven fourth industrial revolution, and industries throughout Australia are already seeing positive results from adopting IoT technology.
What Are Australian Companies Doing With IoT?
Australia’s engagement with IoT goes far beyond simply purchasing technology made elsewhere. Numerous Australian firms are IoT innovators, showing that the country has many opportunities for those who want a career in the industry. Furthermore, Australia’s IoT presence isn’t limited to a specific sector of the economy, and successful IoT innovation is being driven by established corporate giants and cutting-edge startups alike.
Australia’s mining giant puts a lot of money into Research and Development, and that includes the field of IoT. The company is working to use IoT to automate much of its workflow, including heavy-duty trucking, train car loading and drilling. Autonomous drills are already in-use in the company’s Western Australian iron mines, where they have increased productivity and reduced maintenance costs.
A construction-focused robotics firm, FBR’s premier product is a bricklaying robot called Hadrian. By relying on a sophisticated laser-guided sensor network, Hadrian can autonomously lay 200 bricks per-hour – ten times more than a human can. Formerly a startup, FBR is now publicly traded on the Australian Securities Exchange.
FactoryOne is a perfect representation of the convergence of IoT and data science. The company offers a suite of sensors that provide factories with real-time, machine-specific monitoring of their energy consumption. This data is sent to the cloud where it can be monitored by clients in real-time, via a self-service analytics dashboard that can automatically identify inefficiencies in the factory’s energy usage.
Want more information on the Australian IoT scene? Everything IoT provides information on both IoT startups and established companies, as well as news about networking events and career opportunities.
JCU Can Prepare You for a Career as an IoT Data Scientist
Almost half of all IoT firms have difficulty finding the data science talent they need to succeed. For anyone looking to become a tech industry professional, a data science career in IoT stands out as a high-demand career that is firmly positioned on the cutting edge.
JCU’s online Master of Data Science program provides all of the skills needed to begin a professional data science career, and its new IoT career track covers everything necessary to succeed in the industry. Data science and IoT are two of the most important technologies of the 21st century, so a master’s degree that specialises in both is a key to success in Australia and globally.
Whether you’re looking to upgrade to a high-demand career or have long been interested in this emerging field, JCU’s online Graduate Diploma of Data Science (IoT) offers a flexible and affordable way to achieve your goals. For more information on what the program can offer you, contact an admissions representative on 1300 535 919.