In recent years, the data science sector has become one of the fastest-growing job areas, with demand far outstripping supply. As more and more companies come to realize the tremendous value that can be gained from taking an in-depth view of their operations by studying data, so the demand for data scientists will continue to increase globally.
A role that can be applied to almost all sectors and companies
As well as the relative lack of data scientists around the world, another contributing factor to the high value of data science is the fact that the principles can be applied to almost any type of firm. From farming to technology, manufacturing to service companies, pretty much all companies can benefit from taking an overhead view of their processes and studying the data they produce.
As the demand for data scientists continues to grow, and more companies come to realize the worth of studying their data and the transformative effect it can have on profits, a job as a data scientist will continue to be one of the hottest tickets in town – but just what is a data scientist and what do they do?
What is a data scientist?
In simple, layman’s terms, a data scientist is much like an analyst that uses their knowledge of technology and science to mine information and improves efficiencies from the information generated by companies and individuals. As the boundaries between the real and virtual worlds continue to blur, we are producing more data than ever – data that can be interpreted for all manner of uses and outcomes.
In the realm of business, this data (or, another way of putting it, this digital information) can give an otherwise unimaginable depth of reporting to internal processes. The digitization of business has already been well underway for many years, but it’s only been relatively recently that firms have come to realize that by studying this internal data, they can drive greater efficiency and eliminate waste. Because pretty much all companies these days now inherently rely on the web, computers, and tech, the knowledge, and skills of a data scientist can be applied across huge swathes of business and industry.
How to train as a data scientist
Although it could be theoretically possible to train yourself as a data scientist, for the majority of people, the fast-track route and most successful way to gain a position as a data scientist is to learn at university or college. These days, there is a huge range of educational establishments offering courses both on- and offline in the area of data science – for example, you could study an MS in data science.
It’s also worth remembering that, if you’re looking for a change of career or just want a little more job security, taking online training could well be your best option and allow you to study in your own time and at your own pace without unduly interrupting or causing issues in your current employment. Moreover, online study is also normally considerably cheaper than traditional, real-world courses.
Career prospects for a data scientist
As you might expect, with the core skills of data science being applied to so many different industries and sectors, the demand for experienced data scientists remains buoyant. The industry is one of the fastest-growing in the whole computer and IT sector, with career aspects and longevity currently looking very good.
Salary prospects for a data scientist
All jobs vary and the salary you can expect working in the data science sector will also vary from country to country. Nonetheless, an experienced and skilled data scientist would expect wages at the higher end of the tech industry.
Skills that help as a data scientist
Data Science Course or Data Analyst Course will of course help you hone the abilities required to work in data science; however, it still helps if you have some underlying aptitude and skills. As a general rule, most data scientists need skills in a relatively diverse range of subjects, including (but not limited to):
Statistics and reporting: You will definitely need a strong base in statistics, including knowledge of testing hypotheses and concepts like linear regression.
Mathematics: As you might expect, much of data science is based on mathematical principles like calculus and algebra. Many of the most successful and in-demand data scientists have a least a base qualification in mathematics – but more are educated up to the Ph.D. level in Applied Mathematics or similar.
Programming: R is one specific programming language used in data science, so a knowledge of this will be invaluable, as will being able to write in Python.
Being comfortable handing data from multiple different sources: As a data scientist, you will typically source data from many different places and must be comfortable with organizing these huge reams of information.
Networking: The old saying, “It’s not what you know but who you know that’s important” is as applicable to data science as it is to any sector. Having good networking skills and the ability to meet, greet, and talk to a wide range of people will help you get ahead.
Presenting skills: Collecting and deciphering is just one small part of the story when it comes to the work of a data scientist – you also then need to be able to present your findings in an interesting and engaging way to the stakeholders involved in the job. Having good presenting skills will be an added bonus for this part of the job.
Knowledge of commonly used software: You will ideally be well versed in software tools like Tableau and Plotly, which data scientists commonly use for visualization purposes.
Where will a data scientist typically work?
As mentioned above, the demand for data scientists is currently huge, so you will likely find yourself working on an equally massive diversity of jobs and for companies in completely different sectors. Firms in all industries are increasingly relying on the data they collect to make important, strategy-based decisions on their future direction, so you could be working for a manufacturing company one day or an IT service provider the next.
Is the job of a data scientist just a flash in the pan?
Very, very unlikely. When you consider pretty much all companies exist to make a profit with the least possible outlay, it’s almost a no-brainer that a professional with the skills and knowledge to reduce overheads yet improve efficiency will remain in high demand for the foreseeable future.
Working as a data scientist, you will have the potential to make a real difference to firms – to help them streamline operations and make better decisions for their future direction. Individuals with those kinds of skills are surely going to remain a top priority.
What is the future of data science?
As with many web and computer-based sectors, there are compelling reasons to believe much of the tech that will be used in the future of tech has yet to be invented. Certainly, there are very interesting inroads being made with cutting-edge tech like Artificial Intelligence (AI) and Machine Learning (ML), both of which are ideal tools for interpreting the masses of data that data scientists commonly deal with. Also, tech like blockchain is already having an impact on the industry.
Reasons to study data science
To recap, the world of work has become an increasingly uncertain place, with many jobs coming under significant threat from emerging tech like AI, ML, and the trend for automation and robotics. This movement toward digital transformation isn’t just going to disappear, and tech has already exposed a great many previously secure jobs as being superfluous – the so-called pointless jobs.
With so little relative security in so many other career areas, data science is one of the increasingly few jobs that can offer good prospects both now and into the future along with an assured high wage – plus a massive diversity in the type of work you’ll take on.
What types of work do data scientists do?
The job is incredibly varied, and you will undoubtedly work with many different types of companies; however, some of the most common job titles you may hear relating to data science include:
- Data scientist (somewhat obviously)
- Data architect
- Data engineer
- Big Data engineer
- Data visualization developer
- Business Intelligence – various roles including Business Intelligence engineer, Business Intelligence solutions architect, and Business Intelligence specialist
- Statistician
- Analytics manager
How long does it take to become a data scientist?
While all colleges and universities are different, the majority offering data science qualifications tend to run them as a bachelor’s qualification typically spanning four years. Of course, additional qualifications and study will also help your chances of employment.
It’s also worth noting many people in the industry come from diverse backgrounds with often transferrable skills that can help massively in the role of a data scientist. For example, accomplished programmers and coders can often make the leap into the industry relatively easily. Likewise, those individuals with strong logic skills and a knowledge of mathematics will usually make the transition with ease.
