“What makes a data scientist?” Since we are talking data science, the only

logical way to approach the question is to ask the data. And that's what we've done

for 3 consecutive years. Since 2018 we have explored 1001 data scientist LinkedIn profiles

to uncover the most interesting trends in the data science field.

In this video we will go through the most important findings from the last 3 years.

In fact, we have created a very cool and interactive PowerBI dashboard which you can use to analyze

the data yourself. Link in the description. If you prefer to digest our own analysis just

carry on with the video. According to the data, the average data scientist

from 2018 to 2020 is a male with a second-tier degree, coming from a quantitative background,

which is not necessarily data science or computer science. Their preferred programming language

is Python, but they'd often know R and SQL. Many of the new data scientist positions are

being filled by people who are already data scientists, so the field feels much more saturated.

Getting into data science still looks like a great opportunity, but the 'data scientist'

position becomes more and more exclusive. For sure, the current COVID-19 pandemic would

have a say on the demand for data science professionals, too. However, this is maybe

the right time for you to dedicate yourself to starting a career in data science, once

the pandemic is over. And if you've set your sight on a career

in data science – we've got you covered. We developed the '3-6-5 Data Science Program'

to help people of all backgrounds enter the field of data science. We have trained more

than 450,000 people around the world and are committed to continue doing so. If you are

interested to learn more, you can find a link in the description that will also give you

a special offer on all of our plans. That said, let's dive deeper into the education,

years of experience and programming skills of a data scientist from 2018 to 2020.

Education Our sample shows that at least 80% of the

people held a minimum of a Master's degree. This isn't as surprising, considering data

science is a field that expects advanced know-how from the person — usually achieved by graduate

or postgraduate types of education, or independent advanced research in other cases.

And while specialization is important, a Ph.D. is not really a requirement for breaking into

data science. Indeed, over the years, the number of PhD holders has remained consistent,

making up about 27 per cent of our study. On the contrary, starting from 2018 there

was a rise of about 20% in the professionals with a Master's degree compared to the 2019

cohort. Area of Studies

In 2018 and 2019, “Economics and Social Sciences”, “Computer Science” and “Statistics

and Mathematics” were filling up the top 3 most popular fields of study of data scientists.

2020 was the first year ever that featured “Data Science and Analysis” (22%) as the

top degree. Therefore, we can assume that universities have started to catch up with

the demand for data science education. Graduates form the Engineering, Natural Sciences,

and Other fields constitute approximately 11% of our data each. This indicator has remained

stable throughout the years. Interestingly, in 2020, most women in our

sample have earned a 'Statistics and Mathematics' related degree (24% of the female cohort).

In comparison, men most likely earned a degree in Data Science and Analysis (22%), with Computer

Science (19%) being a close second. Years on the job

If you are changing jobs, or working through your data analyst years, you must be wondering

whether you've got the right experience for the position. In terms of tenure, in 2018

almost all data scientists were 'newcomers' to the 'data scientist' position. Some

of this was driven by name changes to their occupation, but mostly – the supply was

so little that it was way easier to enter the field.

Currently, we are observing a much tougher playing field. The majority of data scientists

have more than 2 years on the job and it seems like a very small proportion of the total

data scientist pool is new. In fact, in 2020, 52% of the cohort, held the title 'data

scientist' at their previous position.

Programming languages

The programming skills a data scientist needs are arguably the most interesting area of

research (at least for us). For many years, R was the preferred language a data scientist

was expected to “speak”. In 2018 and 2019 Python started 'eating away at R'. And

it did so at a very fast pace. In 2020, we have reached the point where Python is by

far the preferred programming language in the data science community with 74% adoption!

R is not completely overthrown but becomes less and less favored among professionals.

An interesting development is the rapid year-to-year growth of SQL users. In 2020, more than 50%

of data scientists actively use the language. One common assumption is that companies expect

from a data scientist to solve all their data related problems, no matter if they are related

to data engineering or data architecture. On another note, the adoption of BI software

such as PowerBI and Tableau has also demanded a higher understanding of databases. Inevitably,

SQL had to be added to the data scientist toolbelt for the sake of 'getting the job

done'. Alright.

So, looking at the data, the answer to “What makes a data scientist?” becomes clearer.

Professionals are paving the way and universities are starting to provide a more tailored education.

From a career point of view, it seems that it is getting harder to become a data scientist

as data scientists tend to stay on their job for a longer period of time. However, different

opportunities to get into the field remain, as demand still varies across countries and

industries. One thing is for sure – learn Python, if

you are to become a data scientist! And if you'd like to become an expert in

all things data science, subscribe to our channel.

Thanks for watching and good luck!