Every decade has its hottest job opportunities. During the 1980s and early 1990s people were in a rush to apply for investment banking jobs. Then, in the late 1990s and early 2000s, it became clear that the Internet will soon change the world and a lot of tech savvy graduates started specializing in software and web development.
Today, it is ever clearer that big data, machine learning, and artificial intelligence will become (and in some ways already are) the key success factor that will determine whether businesses will be successful or not in the coming years. That said, it comes as no surprise that the hottest opportunity on the job market in 2017 and 2018 is the data scientist profession. The title “data scientist” sounds sophisticated and scares off people, but perhaps dissecting the typical profile of these professionals will help us show you they are, in fact, human, and if you were so inclined, you too could embark on the journey of becoming a data scientist.
Certainly, at a glance the title “data scientist” has an air of sophistication and pretense, but the data begs to differ. Crunching the numbers, it becomes obvious that there are traits data scientists share. To gain a better understanding of the typical data scientist profile, our team collected information from the LinkedIn profiles of 1,001 data scientist professionals.
Unlike previous publications, the primary source of data we used were not job ads, which skew findings towards the employers’ point of view. Instead, we relied on information posted by data scientists themselves. The underlying assumption was that one’s LinkedIn profile is a good estimator of their resume.
Then we proceeded to assign company and country quotas to limit bias. The cohort was divided into two groups depending on whether a person was employed by a Fortune 500 Company or not. In addition, the sample involved data scientists working in the US (around 40% of our sample), UK (another 30%), India (accounting for 15%), and other countries (the remaining 15%).
Convenience sampling was used, due to limited data accessibility. Once we gathered the numbers, we stumbled upon several interesting findings. The typical data scientist profile looks is a male, who speaks one foreign language, with four and a half years of overall work experience (this is a median).
He works with R and/or Python, and holds a Master’s and/or a PhD degree. Just from this simple overview, we get several noteworthy insights: You can be promoted to data scientist fairly quickly. Assuming you graduate your Master’s before turning 25, or your PhD before 30, a conservative estimate is that by the age of 30 to 35 you can expect to be a professional whose job title reads “data scientist”.
Another interesting finding is that R and Python are on the rise. Previous research shows that the two programming languages are increasing in popularity in the data science world, and that this is happening at the expense of other languages like Java and C/C++. The results observed here corroborate this trend.
You need to start learning R and Python if you want to become a data scientist in 2018. In addition, we can conclude that this is a job for highly educated people. Of course, there is the occasional exception to the rule, but three out of four data scientists in the cohort held a Master’s or a PhD degree.
Indeed, data science is a profession that requires strong academic background. However, given that this is a relatively new field, it comes as no surprise that the data scientists included in the study have heterogeneous academic profiles. Degrees such as Computer Science, Statistics and Mathematics, Economics and Social Sciences, Data Science and Analysis, Natural Sciences, and Engineering dominated the field with 91% of the professionals having graduated from one of them.
The conclusion? Universities and colleges still struggle to meet the growing job market demand for data scientists and companies hire intelligent candidates with different backgrounds. These people have probably been able to acquire the skills employers look for on their own through self-preparation or through extensive on-the-job training.
How can one self-prepare to become a data scientist? Some of the most popular online courses teach people how to run machine learning algorithms in Python and R, and how to deal with databases. E-learning is definitely a resource many data scientists take advantage of.
The study shows that 40% of data scientists have posted an online certificate on their LinkedIn profile, and the average number of certificates per person is 3. Is this a job for people coming from top tier universities only? It isn’t, actually.
Yes, more than 28% of data scientists came from top tier universities (top 50 in the “Times Higher Education” world university ranking), but a significant portion of professionals (more than 25%) graduated from schools that were not even included in the ranking or were ranked after the 1000th place. So, if you are an aspiring data scientist who is about to graduate (or has graduated from) a non-target school, you shouldn’t worry too much – you still have significant chances of landing the job. Self-preparation looks like the key to success in the current environment.
Which are the industries hiring the most data scientists? It has got to be the Tech/IT industry, right? Indeed, it is.
Technology companies are seen as a symbol of innovation. Moreover, data science is essential for such firms as it helps them read online behavior patterns, understand customers’ desires, analyze online search, improve product offering, and so on… Industrial firms come in second, hiring more than 37% of data scientists, while the financial (15%) and healthcare (5%) sectors come in as third and fourth, respectively. It gets even more interesting if we dissect this data by country.
We begin to see that the financial industry in the UK employs a significantly higher percentage of data scientists (~20%) with respect to the other clusters. And it makes sense: London is known as Europe’s financial capital and plenty of financial, trading, and brokerage firms reside there. The job market in India, on the other hand, mainly employs data scientists in the Tech/IT sector.
This is coherent with the country’s status as the world’s prime destination for outsourcing of Tech and IT services. Our conclusion? Hopefully, this research paints a clearer picture for you and helps you understand the core skills and qualifications people currently employed as data scientists have.
In addition, the country-wise segmentation is invaluable, as geographical differences pertain, and so does the skill set required to land the job. If you are interested in a solid data science preparation starting from scratch make sure you visit our website 365datascience. com where you can start your preparation and see if this is the career path for you.
