Data science has for long been one of the highest on demand jobs in the tech industry. While COVID has adversely affected a lot of industrial and service jobs, those in data and AI have been booming.
Ipsos Mori’s report on the UK labour market found that more than 110,500 jobs in data and AI were posted in the last year alone. This was a 16% increase from 2019 and double the figures in 2014, making 2021 the best year to be a prospective employee in AI and data.
Moreover, the trend seems to be only on the rise as up to two thirds of UK firms expect the demand for AI and data related skills to increase in the next 12 months.
Now while all these numbers seem impressive, they are no surprise to anyone in the tech sector. Data is after all the “oil of the 21st century.” Everything in the modern world ‘runs’ on data. From the route you take to work, that ad you get on Instagram or even the match you get on a dating app. Everything is connected. What’s even more astonishing is the amount of data we have accumulated in the last decade.
Consider this, a groundbreaking report in 2013 found that 90% of the world’s data had been collected, just in the previous two years. Let that really sink in! In the two years preceding 2013 the world had collected 9x the amount of data as the previous 92000 years of human existence.
And this has just been exacerbated in the years following 2013. The total amount of data as of 2020 is estimated to be around 2.7 zettabytes. And if you are wondering what a zettabyte is it’s the equivalent of the whole Pacific Ocean full of data, if 1 byte was a single drop of water. Furthermore, this number is predicted to grow to around 44 zettabytes this year.
The questions that remain are: what to do with all these data? And, how to manage it all?
That’s where data scientists come in.
What do Data Scientists Really Do?
Explaining what data scientists do is a massive task even for a data scientist. It involves a variety of disciplines such as statistics, data engineering, math and advanced computing to be able to sift through incalculable amounts of data.
It generally, according to built in, involves a process of:
Uses of Data Science
As mentioned earlier, data is central to the digital economy. It powers almost every sector of the global economic machine. Here are some of the major ways data is used in the 21st century digital economics.
With the number of devices used to capture patient data having multiplied in recent years, there is now more data about diseases and infections than ever before. Doctors, physicians and even surgeons are now better placed to make better decisions before prescribing a treatment. With the use of advanced data analytics and other modern tools, they are able to make more accurate diagnosis and thus ascribe better treatment to their patients.
Autonomous driving is the future of the automotive industry. Self-driving EVs are safer, cheaper to operate, cleaner and dare I say cooler. Data science is at the center of this revolution. It is through the accumulation, timely communication and processing that self-driving cars are able to make sense of their environment.
Ever wonder how Spotify recommends/plays just the right song to capture the mood or even how Netflix seems to recommend just the right film for you? Well it all boils down to data, specifically yours. These sites make use of advanced of algorithms to assess your tastes and recommend just the right content to keep you hooked.
The financial sector makes heavy use of data. In fact, it might be argued that they are of one of the first to employ data, analytics and statistics in wide scale. In the stock exchange and banking.
The stock exchange makes extensive use of data to track stock prices, sales etc.
In banking, JP Morgan’s Contract Intelligence (COiN) is a good example of use of data analytics. It ‘uses Natural Language Processing (NLP) to process and extract vital data from about 12,000 commercial credit agreements a year.’
The internet continues to evolve and with it the risks involved continue to change. Hackers are getting smarter and have better resources. What is needed is a system that will stay on top of all these changes so as to be able to identify and neutralize threats before they do any significant harm.
Kaspersky for instance, utilizes machine learning and data science to detect over 360,000 new samples on a daily basis.