The World Economic Forum estimates that we will see as much technological change in the next 10 years as we have in the last century. This change will be fueled by data, not oil. How much data? The WEF quotes McKinsey as estimating that there will be 50 billion devices communicating over the Industrial Internet of things and generating more than 79 zettabytes of data. That’s only by 2025. So, you can bet that data science careers are going to be in demand. At Employa, we’re excited to watch the field of data science development, and we’re already helping people find the best data science jobs.
At the same time, the best data science jobs might not always be those of the data scientist. Yes, in 2012, the Harvard Business Review called being a data scientist, “The Sexiest Job of the 21st Century”. Overall, they’re in the right direction. But the rise of data analytics has made a career in big data analytics a reality, and the lines between the data engineer and data scientist positions are blurring. At the same time, data security and data privacy engineering are also among the best data science jobs, especially after the European Union introduced the GDPR privacy regulation and many countries followed suit. Data is everywhere, and data-oriented jobs are following suit.
Get Started with Data Science
Entry-level data science requires an aptitude for math, but not a lot of arcane knowledge. The main programming language of data science, Python, ranks #2 in popularity among learners, according to the University of California, Berkeley’s Boot Camp extension. Online education abounds for this language.
Python alone is a good start, but for a career in big data (or small data, or wide data) working with more specialized tools such as pandas for manipulating data sets. When it comes to pandas, there are still a lot of learning options available. Once you get to this point, you get to decide where you want to go in the discipline.
Settling into Data Science
The primary data science jobs, namely, data analyst, data engineer, and data scientist, all work with data, but in different ways. In short, data analysts work with the data moved across infrastructure created and maintained by data engineers according to architectures and plans created by data scientists. At least, that’s the popular view.
In practice, the lines can blur, and knowing what the others do can be very useful, as practitioners themselves point out. In a self-admittedly biased article, data scientist Terence Shin makes the claim that data scientists such as himself need to know some data engineering, especially in companies that don’t have teams of data personnel around.
"...if you’re a data scientist without the fundamental knowledge of a data engineer, there will certainly be times when you’ll have to rely on someone else to fix an ETL pipeline or clean data as opposed to doing it on your own."
Terence Shin, Data Scientist at KOHO
The main takeaway here is that for many in data analytics, engineering and science, the importance of the breadth of your knowledge might carry as much weight as the depth of it. For more advanced positions, it could be vital.
Data scientist positions
While Terence Shin determined that in March 2021, at least, there were as many data engineering jobs available as data scientist jobs, a look at some of the data scientist positions possible is revealing.
The opportunities for data scientists, in particular, will be growing as the envelope of technology expands. For example, the race for a vaccine against COVID-19 led to a collision between data science and privacy regulations such as GPDR, which stipulates levels that data must be pseudonymized or anonymized to protect those the data came from. Vaccine makers sometimes needed information that a normal process would not, especially considering the shortened regulatory procedures needed to bring workable vaccines to the public. Once the data privacy issues were solved, it was up to the data scientists to see how it could work.
The example above points to two key traits for data scientists, namely a problem-solving mindset, and the ability to bolt on important points provided by others. Even without the disruption caused by COVID, a data scientist involved in privacy regulation will find herself at the connection of:
- systems engineering,
- law, and
- data science.
This is where the difference between data scientists and data engineers is clearest – the engineer has to figure out how to implement, but the scientist is figuring out what to implement in the first place.
There are some sources, such as the EU-funded PDP4E project, which worked with the regulator to create tools to implement the idea behind the law. Data scientists at this level have to put juridical laws into practice, and utilize what’s called model-driven design.
Do companies care about it? In July 2021 when Amazon received a 746 million euro (approx. $886 million) fine for violating GDPR regulations in regard to processing personal data. So, yes, and data scientists who can work with data privacy will be in demand, and those with a basic understanding of an industry, from retail to medicine and law, will be needed even more.
Finding your data science job
If you’re still asking, “How to get into data science,”, and you have at least a basic education in the technology stack for data science, there are two directions to take.
If you are particularly interested in a field – medicine, or logistics, for example, ask yourself, “where do data scientists work?” in your target direction. You’re likely to find them in the largest and most advanced companies in that field, even if it doesn’t seem so obvious at first.
For example, agriculture may seem far from IT in some ways, but precision agriculture is already a $3.58 billion market globally, according to Market Watch. That market is expected to double by 2030. Agriculture produces vast amounts of data, from soil conditions to farm machinery diagnostics to the weight of cows. A career in big data might land you with one of the ag engineering giants.
Most of the jobs in data science are found in enterprise IT departments, though, especially in obviously tech-oriented verticals such as telecoms. Because data scientists can be hard to find, especially at the upper echelons, companies often turn to specialists such as Employa to find their next hires. We should know – human resources and hiring is a very data-intensive industry!