The A-Z of Data Science for IoT

Over the course of the next two weeks, Metiora’s blog is going to focus heavily on the topic of Data Science for IoT, as we post a series of articles that explain the A to Z of data analytics and everything in-between.

The subtopics we’re going to delve into are the following:

So, let’s begin with the “Internet of Things (IoT) and its impact on Data Science”.

Over the last two decades, more than six billion devices have been connected to the Internet, from Smart refrigerators, to Smart alarm clocks and even Smart hairbrushes. These devices collectively generate more than 2.5 quintillion bytes of data daily, which is enough to fill 57.5 billion 32 GB iPads per day, according to Gartner. The large volumes of data generated from these IoT devices needs to be analysed to extract knowledge and valuable information in order to help organisations make smarter decisions. This is where IoT intersects with data science processes.

Data Science is a multidisciplinary field that involves extracting knowledge and valuable insights from structured or unstructured data. The mathematical concepts for data science are all applicable to IoT datasets, but programming for IoT datasets involves using tools like Python and libraries, such as Pandas, Numpy and distributions like Sci-kit. The major difference between traditional data science and IoT analytics is that the latter focuses on cognitive computing, real-time processing, deep learning, edge computing and in-memory processing. 

Due to the huge amounts of data being generated, and wasted, on a daily basis, it’s safe to say that the big data of today will soon become the small data of tomorrow. In other words, the impact of IoT on Data Science is just as vast as the data at hand and the need for Data Scientists trained within the IoT field is bigger than ever before.