If you have come across this article, you already know what data science is and how it can be used. That’s great! Now, you are probably wondering why there is so much fuss about data science. If you want to know why you should become a data scientist, the facts speak for themselves!
According to LinkedIn’s 2017 U.S. Emerging Jobs Report, the number of data scientists has grown over 650% since 2012. Yet there are still too few people exploiting the opportunities in this field. Why has it grown so fast?
Companies need to use data to run and grow their everyday business. The fundamental goal of data science is to help companies make quicker and better decisions, which can take them to the top of their market, or at least – especially in the toughest red oceans – be a matter of long-term survival. The number of companies prepared to use big data is increasing. As Dresner Advisory Services laid out in their Big Data Analytics Market Study, forty percent of non-users expect to adopt big data in the next two years.
What is more, you can apply machine learning on smaller data sets, such as ones from a local company’s social media or shopping gift card history. This provides even more opportunities and increases the demand for data scientists. Job growth in the next decade is expected to exceed growth from the past ten years, creating 11.5M jobs by 2026, according to the U.S. Bureau of Labor Statistics. Companies are building up their data science teams to embrace data analytics and will make it integral to their success. Why are these analytics so important? Is it worth working for one of these companies? You will find the answer in the next two chapters.
Data science changes how decisions are made and companies are adapting a data-driven approach on a huge scale. Data-driven decisions made with advanced data analytics benefit all manner of company, from global behemoths to medium-sized companies down to local businesses looking to get ahead. Lack of data is rarely an issue – mountains of it are collected every single second, and we are beginning to understand the potential and influence it can have. Data sets in the right hands can help predict and shape the future.
The problem is getting data sets to mingle. It is the data scientist’s role to transform organisations from reactive environments with static and aged data, to automated ones that continuously learn in real time. Forecasts are simple – data is a valuable resource and investing in it will definitely pay off.
Tractica forecasts that worldwide revenue from deployments of AI software, hardware, and services will increase from $14.9 billion in 2017 to $23.6 billion in 2018, a year-over-year increase of 58%.
Do we need more data scientists?
Now, knowing that data science is in huge demand, you are probably wondering who is going to do all the work. Do we have enough data scientists? Maybe the market is already flush with experts. Nothing could be further from the truth – data scientists are few and far between, and highly sought after. IBM predicts demand for data scientists will soar 28% by 2020. Machine learning and data science are generating more jobs than there are experts to fill them, which is why these two fields are the fastest growing tech employment areas today.
Why should you become a data scientist?
Let’s start from the bottom of Maslow’s pyramid of human needs, which you secure with money. According to Glassdoor, in 2016 data science was the single highest paid profession. If data is money, as they say, then this should come as no surprise. The combination of skills necessary to do data science the right way is not common. The good news, however, is that if you want to become a data scientist and are willing to develop yourself, you are very likely to succeed. A background in mathematics, statistics or physics is a good foundation to build upon. You don’t necessarily need to have finished a data science program. We write a lot about learning methods on our blog, which you’ll see if you read our next post. Sign up for our newsletter if you would like to be updated.
Make the world easier
Besides its financial and economic aspects, data science is simply a fascinating discipline, one which affects many areas of our everyday lives and makes the world a better place. We already use it in many fields, such as quick and easy customer service, intelligent navigation, recommendations and voice-to-text. You can even improve the resolution of an image with deep learning.
We don’t have space enough to chronicle as of the ways that data science is improving people’s lives. It is indispensable to the banking sector as it is used to detect fraud by analyzing the behavior of financial institutions in real time. Elsewhere, robots will be used to help the elderly and the disabled gain mobility and independence. Data science makes these breakthroughs accessible to individuals, solves social problems and modernizes business. Most importantly, you can take part in the revolution data science is bringing about.
Things that matter
Among the many reasons you would want to become a data scientist is that you can make a positive contribution to society. Data science can give you some pretty super superpowers. One of them is reshaping industries like healthcare. The amount of data produced about patients and illnesses rises by the second, opening new opportunities for better structured and more informed healthcare. The challenge is to carefully analyze the data in order to be able to recognize problems quickly and accurately – like deepsense.ai did in diagnosing diabetic retinopathy with deep learning.
Did you know that deep learning can help predict dangerous seismic events and keep miners safe? Underground mining is fraught with threats including fires, methane outbreaks or seismic tremors and bumps. An automatic system for predicting and alerting against such dangers is of utmost importance – and also a great challenge for data scientists. Our deepsense.ai team created a machine learning model for the Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines, which was the winning solution, and one we take great pride in.
Another superpower is saving rare species. When you think of rescuing endangered animals, you see remote jungles and scientists chasing them. This is a stereotype that has changed a lot in recent years. Complex predictive models and algorithms can create insights that help scientists analyze threats to wildlife and create a solution that can save animals – all from the relative comfort of a desk. In fact, it was at our very desktops that we created the Facebook for whales, and It works with 87% accuracy!
Psst… There’s just one more thing. Data science can simply be fun. Can deep learning play Atari games? Yes! Or perhaps you want to make art even if you aren’t an artist. Data scientists can do it. The only limitation is your imagination!
In the next post you will find inspiration on how to become a data scientist and what kinds of data science training are available on the market, along with their advantages and drawbacks. Stay tuned!