Why Data Scientists Should Consider Adding ‘IoT Expert’ to Their List of Skills
The work of a data scientist is never done — nor is the pursuit of personal growth and knowledge. Technology advances and evolves at alarming speeds, which means anyone working in the industry must remain informed if they want to be competitive. In fact, this means it can be beneficial to get involved with neighboring industries or sectors, even if they have little to no relation to your current career.
For data scientists, it may be time to seriously consider adding the title “IoT Expert” to your growing list of traits and abilities. Why would you be concerned with learning this relatively new form of data science? More importantly, how can it help your career?
Let’s start with the obvious — there’s a huge demand for IoT experts in the current landscape, and also in the far future. This can be due to a skill shortage, one that’s not only felt by IoT, but nearly all data and technology careers evenly.
Traditional Data Science Versus the Internet of Things
By compacting the comparison between these two data technologies down to its simplest form, it comes down to time. Traditional data science, for example, relates to more common processes that facilitate data collection and organization. There is no expiry or looming schedule to be concerned with, for the most part.
While you need to analyze and understand data as soon as possible, IoT is in real-time, simple to use and seemingly endless. The real-time factor is one of the significant elements that sets the two technologies apart.
Both require high signal rates and processing times, but IoT exclusively requires aggregated insights and decisions on the spot. To expand or draw out the time it takes for data processing, you must decrease or mitigate the overall value of the data at the core of the technology.
This also factors into edge analytics, which requires systems to be efficient at optimizing insights and decision information and detecting performance or predictive misbehaviors.
Of course, there are also the matters of security and privacy, varying devices and tools employed, general training and much more.
That’s not to say data scientists cannot be skilled at IoT or jump between the two technologies. It just means that out-of-the-gate, you’ll need an additional series of skills and knowledge to begin working with IoT technologies and platforms.
How Data Scientists Can Become Skilled IoT Developers and Analysts
Overall, the skill sets are remarkably similar. However, data scientists tend to be familiar with big data and related analytics systems. By nature, traditional big data platforms vary in the scope, source and frequency of relevant content.
Often — but not always — this means data scientists and developers will focus on a single area or coding language that meets their preferences. IoT, on the other hand, requires a much broader proficiency with programming languages and related technologies. This is because no single platform or device uses a central or core platform. Languages used to develop and maintain various devices will change based on the platform, company and implementation.
Next-generation developers must be multi-lingual in tools, technologies and languages. So, if you have already invested a lot of time exploring other platforms, types of technologies and even computer programming languages, you already have a head up on the competition. If you don’t, that’s the first thing you should focus on before moving forward.
According to CIO magazine, some of the most prominent skills necessary to be an IoT developer include:
- Machine learning and AI
- js experience
- Consumer and enterprise-level data security and privacy
- Cloud computing and management
- Edge analytics, and edge computing basics
- Blockchain for future proofing
You Need More Than Technical Skills to Succeed
While the above list does include primarily technical and technology-based skills, those aren’t the only aspects and traits you’ll need to succeed in the industry. IoT also requires a good grasp of problem-solving, curiosity and a desire for personal growth, self-actualization and refined time management and productivity skills.
If you’re working with a team of like-minded professionals, you’ll also need to be able to network, collaborate and share experiences with others. Of course, the ability to take criticism, direct guidance and communicate are also crucial in such an environment.
For now, just ensure you have the core or foundational skills before moving to a new data technology. That calls for broadening your horizons outside traditional data science, to become both more effective and reliable.
About the author: Nathan Sykes is a contributor at Simple Programmer, KDNugget, Information-Management and editor of Finding an Outlet. To stay up to date with his latest articles, follow Nathan on Twitter @nathansykestech.